Sorry, but the page you were trying to view does not exist.

A variety of common markup showing how the theme styles them.

Header one

Header two

Header three

Header four

Header five

Header six

Blockquotes

Single line blockquote:

Quotes are cool.

Tables

Definition Lists

Definition List Title

Definition list division.

Startup

A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.

#dowork

Coined by Rob Dyrdek and his personal body guard Christopher “Big Black” Boykins, “Do Work” works as a self motivator, to motivating your friends.

Do It Live

I’ll let Bill O’Reilly explain this one.

Unordered Lists (Nested)

• List item one
  • List item one
    • List item one
    • List item two
    • List item three
    • List item four
  • List item two
  • List item three
  • List item four
• List item two
• List item three
• List item four

Ordered List (Nested)

1. List item one
   1. List item one
      1. List item one
      2. List item two
      3. List item three
      4. List item four
   2. List item two
   3. List item three
   4. List item four
2. List item two
3. List item three
4. List item four

Buttons

Make any link standout more when applying the .btn class.

Notices

Watch out! You can also add notices by appending {: .notice} to a paragraph.

HTML Tags

Address Tag

Anchor Tag (aka. Link)

This is an example of a link.

Abbreviation Tag

The abbreviation CSS stands for “Cascading Style Sheets”.

Cite Tag

“Code is poetry.” —Automattic

Code Tag

You will learn later on in these tests that word-wrap: break-word; will be your best friend.

Strike Tag

This tag will let you strikeout text.

Emphasize Tag

The emphasize tag should italicize text.

Insert Tag

This tag should denote inserted text.

Keyboard Tag

This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.

Preformatted Tag

This tag styles large blocks of code.

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

Quote Tag

Developers, developers, developers… –Steve Ballmer

Strong Tag

This tag shows bold text.

Subscript Tag

Getting our science styling on with H₂O, which should push the “2” down.

Superscript Tag

Still sticking with science and Isaac Newton’s E = MC², which should lift the 2 up.

Variable Tag

This allows you to denote variables.

{% include base_path %} {% for post in site.pages %} {% include archive-single.html %} {% endfor %}

{% include base_path %} {% include group-by-array collection=site.posts field=”categories” %}

{% for category in group_names %} {% assign posts = group_items[forloop.index0] %}

{{ category }}

{% for post in posts %} {% include archive-single.html %} {% endfor %} {% endfor %}

{% include base_path %} {% capture written_label %}’None’{% endcapture %}

{% for collection in site.collections %} {% unless collection.output == false or collection.label == “posts” %} {% capture label %}{{ collection.label }}{% endcapture %} {% if label != written_label %} <h2 id="{{ label | slugify }}" class="archive__subtitle">{{ label }}</h2> {% capture written_label %}{{ label }}{% endcapture %} {% endif %} {% endunless %} {% for post in collection.docs %} {% unless collection.output == false or collection.label == “posts” %} {% include archive-single.html %} {% endunless %} {% endfor %} {% endfor %}

{% include base_path %}

{% include cv-template.html %}

{% include base_path %}

Education

• HKUST(GZ), MPhil in Artificial Intelligence (Sept 2026 – Present)
  • Advisor: Prof. Hui Xiong
• Fudan University, BS in Computer Science (Sept 2022 – June 2026)
  • GPA: 3.73/4.0, National Scholarship (2024 – 2025)
  • Coursework: Programming (Grade: A), Artificial Intelligence (Honors, Grade: A), Digital Image Processing (Grade: A), Set and Graph Theory (Honors, Grade: A)
• HKUST, Exchange Program on Bachelor’s level (Aug 2024 – Dec 2024)
  • Coursework: Design and Analysis of Algorithms, Computer Graphics, Probability

Research Experience

• Research Intern, CIS Lab, HKUST(GZ) (Mar 2025 – Aug 2025)
  • Supervisor: Dr. Zeyu Wang
  • Independently curated a large-scale multimodal video dataset with fine-grained emotion labels, and developed emotion-conditioned video generation methods.
  • Built a VR platform for immersive emotion elicitation and participated in an open-source project about sketch.
• Research Intern, Hong Kong Generative AI Research and Development Center (Aug 2024 – Dec 2024)
  • Supervisor: Prof. Wenhan Luo
  • Constructed a dataset of deepfake human animation videos.
  • Implemented a fully temporal convolution network (FTCN) to detect video deepfakes.

Skills

• Programming Languages: Python, C++, C, SQL
• Technologies: PyTorch, Deep Learning, LaTeX, Academic English (TOEFL 103), Photoshop & Premiere
• Research Interests: Video Generation, 3D Vision, MLLM

/*

• This file controls what is imported from /_sass *
• Note that the files are processed in the order they are imported, so they are partly sorted by the dependencies. Also, the first two lines of the file are required by Jekyll. */

@import “vendor/breakpoint/breakpoint”,

"themes",
"theme/{{ site.site_theme | default: 'default' | append: '_light' }}",
"theme/{{ site.site_theme | default: 'default' | append: '_dark' }}",

"include/mixins",
"vendor/susy/susy",

"layout/reset",
"layout/base",

"include/utilities",
"layout/tables",
"layout/buttons",
"layout/notices",
"layout/masthead",
"layout/navigation",
"layout/footer",
"syntax",

"layout/forms",

"layout/page",
"layout/archive",
"layout/sidebar",
"layout/json_cv",

"vendor/font-awesome/fontawesome",
"vendor/font-awesome/solid",
"vendor/font-awesome/brands" ;

{% include toc %}

Locations of key files/directories

• Basic config options: _config.yml
• Top navigation bar config: _data/navigation.yml
• Single pages: _pages/
• Collections of pages are .md or .html files in:
  • _publications/
  • _portfolio/
  • _posts/
  • _teaching/
  • _talks/
• Footer: _includes/footer.html
• Static files (like PDFs): /files/
• Profile image (can set in _config.yml): images/profile.png

Tips and hints

• Name a file “.md” to have it render in markdown, name it “.html” to render in HTML.
• Go to the commit list (on your repo) to find the last version GitHub built with Jekyll.
  • Green check: successful build
  • Orange circle: building
  • Red X: error
  • No icon: not built
• Academic Pages uses Jekyll Kramdown, GitHub Flavored Markdown (GFM) parser, which is similar to the version of Markdown used on GitHub, but may have some minor differences.
  • Some of emoji supported on GitHub should be supposed via the Jemoji plugin .
  • The best list of the supported emoji can be found in the Emojis for Jekyll via Jemoji blog post.
• While GitHub Pages prevents server side code from running, client-side scripts are supported.
  • This means that Google Analytics is supported, and the wiki should contain the most up-to-date information on getting it working.

• Your CV can be written using either Markdown (preview) or generated via JSON (preview) and the layouts are slightly different. You can update the path to the one being used in _data/navigation.yml with the JSON formatted CV being hidden by default.

• The Liquid syntax guide is a useful guide for those that want to add functionality to the template or to become contributors to the template on GitHub.

MathJax

Support for MathJax (version 3.* via jsDelivr, documentation) is included in the template:

The default delimiters of $$...$$ and \\[...\\] are supported for displayed mathematics, while \\(...\\) should be used for in-line mathematics (ex., \(a^2 + b^2 = c^2\))

Note that since Academic Pages uses Markdown which cases some interference with MathJax and LaTeX for escaping characters and new lines, although some workarounds exist. In some cases, such as when you are including MathJax in a citation field for publications, it may be necessary to use \(...\) for inline delineation.

Mermaid diagrams

Academic Pages includes support for Mermaid diagrams (version 11.* via jsDelivr) and in addition to their tutorials and GitHub documentation the basic syntax is as follows:

    ```mermaid
    graph LR
    A-->B
    ```

Which produces the following plot with the default theme applied:

graph LR
A-->B

While a more advanced plot with the forest theme applied looks like the following:

---
config:
  theme: 'forest'
---
graph TD;
    A-->B;
    A-->C;
    B-->D;
    C-->D;

Plotly

Academic Pages includes support for Plotly diagrams via a hook in the Markdown code elements, although those that are comfortable with HTML and JavaScript can also access it via those routes.

In order to render a Plotly plot via Markdown the relevant plot data need to be added as follows:

    ```plotly
    {
      "data": [
        {
          "x": [1, 2, 3, 4],
          "y": [10, 15, 13, 17],
          "type": "scatter"
        },
        {
          "x": [1, 2, 3, 4],
          "y": [16, 5, 11, 9],
          "type": "scatter"
        }
      ]
    }
    ```

Important! Since the data is parsed as JSON all of the keys will need to be quoted for the plot to render. The use of a tool like JSONLint to check syntax is highly recommended.

Which produces the following:

{
  "data": [
    {
      "x": [1, 2, 3, 4],
      "y": [10, 15, 13, 17],
      "type": "scatter"
    },
    {
      "x": [1, 2, 3, 4],
      "y": [16, 5, 11, 9],
      "type": "scatter"
    }
  ]
}

Essentially what is taking place is that the Plotly attributes are being taken from the code block as JSON data, parsed, and passed to Plotly along with a theme that matches the current site theme (i.e., a light theme, or a dark theme). This allows all plots that can be described via the data attribute to rendered with some limitations for the theme of the plot.

{
  "data": [
    {
      "x": [1, 2, 3, 4, 5],
      "y": [1, 6, 3, 6, 1],
      "mode": "markers",
      "type": "scatter",
      "name": "Team A",
      "text": ["A-1", "A-2", "A-3", "A-4", "A-5"],
      "marker": { "size": 12 }
    },
    {
      "x": [1.5, 2.5, 3.5, 4.5, 5.5],
      "y": [4, 1, 7, 1, 4],
      "mode": "markers",
      "type": "scatter",
      "name": "Team B",
      "text": ["B-a", "B-b", "B-c", "B-d", "B-e"],
      "marker": { "size": 12 }
    }    
  ],
  "layout": {
    "xaxis": {
      "range": [ 0.75, 5.25 ]
    },
    "yaxis": {
      "range": [0, 8]
    },
    "title": {"text": "Data Labels Hover"}
  }
}

{
  "data": [{
      "x": [1, 2, 3],
      "y": [4, 5, 6],
      "type": "scatter"
    },
    {
      "x": [20, 30, 40],
      "y": [50, 60, 70],
      "xaxis": "x2",
      "yaxis": "y2",
      "type": "scatter"
  }],
  "layout": {
    "grid": {
      "rows": 1,
      "columns": 2,
      "pattern": "independent"
    },
    "title": {
      "text": "Simple Subplot"
    }    
  }
}

{
  "data": [{
		"z": [[10, 10.625, 12.5, 15.625, 20],
          [5.625, 6.25, 8.125, 11.25, 15.625],
          [2.5, 3.125, 5.0, 8.125, 12.5],
          [0.625, 1.25, 3.125, 6.25, 10.625],
          [0, 0.625, 2.5, 5.625, 10]],
		"type": "contour"
	}],
  "layout": {
    "title": {
      "text": "Basic Contour Plot"
    }
  }
}

Markdown guide

Academic Pages uses kramdown for Markdown rendering, which has some differences from other Markdown implementations such as GitHub’s. In addition to this guide, please see the kramdown Syntax page for full documentation.

Header three

Header four

Header five

Header six

Blockquotes

Single line blockquote:

Quotes are cool.

Tables

Table 1

Table 2

Definition Lists

Definition List Title

Definition list division.

Startup

A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.

#dowork

Coined by Rob Dyrdek and his personal body guard Christopher “Big Black” Boykins, “Do Work” works as a self motivator, to motivating your friends.

Do It Live

I’ll let Bill O’Reilly explain this one.

Unordered Lists (Nested)

• List item one
  • List item one
    • List item one
    • List item two
    • List item three
    • List item four
  • List item two
  • List item three
  • List item four
• List item two
• List item three
• List item four

Ordered List (Nested)

1. List item one
   1. List item one
      1. List item one
      2. List item two
      3. List item three
      4. List item four
   2. List item two
   3. List item three
   4. List item four
2. List item two
3. List item three
4. List item four

Buttons

Make any link standout more when applying the .btn class.

Notices

Basic notices or call-outs are supported using the following syntax:

**Watch out!** You can also add notices by appending `{: .notice}` to the line following paragraph.
{: .notice}

which wil render as:

Watch out! You can also add notices by appending {: .notice} to the line following paragraph.

Footnotes

Footnotes can be useful for clarifying points in the text, or citing information.¹ Markdown support numeric footnotes, as well as text as long as the values are unique.²

This is the regular text.[^1] This is more regular text.[^note]

[^1]: This is the footnote itself.
[^note]: This is another footnote.

HTML Tags

Address Tag

Anchor Tag (aka. Link)

This is an example of a link.

Abbreviation Tag

The abbreviation CSS stands for “Cascading Style Sheets”.

Cite Tag

“Code is poetry.” —Automattic

Code Tag

You will learn later on in these tests that word-wrap: break-word; will be your best friend.

You can also write larger blocks of code with syntax highlighting supported for some languages, such as Python:

print('Hello World!')

or R:

print("Hello World!", quote = FALSE)

Details Tag (collapsible sections)

The HTML <details> tag works well with Markdown and allows you to include collapsible sections, see W3Schools for more information on how to use the tag.

The source code:

<details>
  <summary>Collapsed by default</summary>
  This section was collapsed by default!
</details>

Or, you can leave a section open by default by including the open attribute in the tag:

Emphasize Tag

The emphasize tag should italicize text.

Insert Tag

This tag should denote inserted text.

Keyboard Tag

This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.

Preformatted Tag

This tag styles large blocks of code.

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

Quote Tag

Developers, developers, developers… –Steve Ballmer

Strike Tag

This tag will let you strikeout text.

Strong Tag

This tag shows bold text.

Subscript Tag

Getting our science styling on with H₂O, which should push the “2” down.

Superscript Tag

Still sticking with science and Isaac Newton’s E = MC², which should lift the 2 up.

Variable Tag

This allows you to denote variables.

Footnotes

The footnotes in the page will be returned following this line, return to the section on Markdown Footnotes.

This is a page not in the menu. You can use markdown in this page.

Heading 1

Heading 2

{% include base_path %} {% for post in site.pages %} {% include archive-single.html %} {% endfor %}

{% include base_path %}

{% for post in site.portfolio %} {% include archive-single.html %} {% endfor %}

{% if site.author.googlescholar %}

{% endif %}

{% include base_path %}

{% if site.publication_category %} {% for category in site.publication_category %} {% assign title_shown = false %} {% for post in site.publications reversed %} {% if post.category != category[0] %} {% continue %} {% endif %} {% unless title_shown %} <h2>{{ category[1].title }}</h2><hr /> {% assign title_shown = true %} {% endunless %} {% include archive-single.html %} {% endfor %} {% endfor %} {% else %} {% for post in site.publications reversed %} {% include archive-single.html %} {% endfor %} {% endif %}

Service

• Reviewer, AAAI 2026
• Reviewer, IEEE Transactions on Affective Computing

{% include base_path %}

A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

{% for post in site.pages %} {% include archive-single.html %} {% endfor %}

Posts

{% for post in site.posts %} {% include archive-single.html %} {% endfor %}

{% capture written_label %}’None’{% endcapture %}

{% for collection in site.collections %} {% unless collection.output == false or collection.label == “posts” %} {% capture label %}{{ collection.label }}{% endcapture %} {% if label != written_label %}

{{ label }}

{% capture written_label %}{{ label }}{% endcapture %} {% endif %} {% endunless %} {% for post in collection.docs %} {% unless collection.output == false or collection.label == “posts” %} {% include archive-single.html %} {% endunless %} {% endfor %} {% endfor %}

{% include base_path %} {% include group-by-array collection=site.posts field=”tags” %}

{% for tag in group_names %} {% assign posts = group_items[forloop.index0] %}

{{ tag }}

{% for post in posts %} {% include archive-single.html %} {% endfor %} {% endfor %}

This map is generated from a Jupyter Notebook file in talkmap.ipynb, which mines the location fields in the .md files in _talks/.

{% if site.talkmap_link == true %}

See a map of all the places I've given a talk!

{% endif %}

{% for post in site.talks reversed %} {% include archive-single-talk.html %} {% endfor %}

{% include base_path %}

{% for post in site.teaching reversed %} {% include archive-single.html %} {% endfor %}

{% include base_path %} {% include toc %}

Privacy Policy

The privacy of my visitors is extremely important. This Privacy Policy outlines the types of personal information that is received and collected and how it is used.

First and foremost, I will never share your email address or any other personal information to anyone without your direct consent.

Log Files

Like many other websites, this site uses log files to help learn about when, from where, and how often traffic flows to this site. The information in these log files include:

• Internet Protocol addresses (IP)
• Types of browser
• Internet Service Provider (ISP)
• Date and time stamp
• Referring and exit pages
• Number of clicks

All of this information is not linked to anything that is personally identifiable.

Cookies and Web Beacons

When you visit this site “convenience” cookies are stored on your computer when you submit a comment to help you log in faster to Disqus the next time you leave a comment.

Third-party advertisers may also place and read cookies on your browser and/or use web beacons to collect information. This site has no access or control over these cookies. You should review the respective privacy policies on any and all third-party ad servers for more information regarding their practices and how to opt-out.

If you wish to disable cookies, you may do so through your web browser options. Instructions for doing so can be found on the specific web browsers’ websites.

Google Analytics

Google Analytics is a web analytics tool I use to help understand how visitors engage with this website. It reports website trends using cookies and web beacons without identifying individual visitors. You can read Google Analytics Privacy Policy.

{% include base_path %} {% capture written_year %}’None’{% endcapture %} {% for post in site.posts %} {% capture year %}{{ post.date | date: ‘%Y’ }}{% endcapture %} {% if year != written_year %} <h2 id="{{ year | slugify }}" class="archive__subtitle">{{ year }}</h2> {% capture written_year %}{{ year }}{% endcapture %} {% endif %} {% include archive-single.html %} {% endfor %}

@import “jekyll-theme-primer”;

{“/about/”:”https://zane-zyqiu.github.io/”,”/about.html”:”https://zane-zyqiu.github.io/”,”/resume-json”:”https://zane-zyqiu.github.io/cv-json/”,”/resume”:”https://zane-zyqiu.github.io/cv/”,”/md/”:”https://zane-zyqiu.github.io/markdown/”,”/markdown.html”:”https://zane-zyqiu.github.io/markdown/”,”/nmp/”:”https://zane-zyqiu.github.io/non-menu-page/”,”/nmp.html”:”https://zane-zyqiu.github.io/non-menu-page/”,”/wordpress/blog-posts/”:”https://zane-zyqiu.github.io/year-archive/”}

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Repository overview

This is Zongyang (Zane) Qiu’s personal academic portfolio site, built on top of the Academic Pages Jekyll template (a detached fork of Minimal Mistakes). It is deployed at https://zane-zyqiu.github.io via GitHub Pages (built from master).

Most day-to-day edits are content updates in Markdown/YAML. Structural changes touch Liquid templates, SCSS, and the asset build.

Common commands

Local development (native Ruby)

bundle install                          # first time, or after Gemfile changes
bundle exec jekyll serve -l -H localhost  # live-reload server on localhost:4000

Editing _config.yml requires restarting jekyll serve (Jekyll does not auto-reload config). Markdown/HTML edits are picked up automatically.

Local development (Docker)

docker compose up                       # serves at localhost:4000, auto-rebuilds

Uses _config.yml merged with _config_docker.yml. VS Code Dev Container is also configured (.devcontainer/devcontainer.json).

JS asset build

assets/js/main.min.js is the uglified bundle of jQuery + fitvids + smooth-scroll + greedy-nav + _main.js. Rebuild after editing any source JS:

npm install
npm run build:js      # one-shot uglify
npm run watch:js      # rebuild on change

CV pipeline

The CV has two rendering paths that must stay in sync:

• _pages/cv.md — Markdown source of truth, rendered at /cv/.
• _data/cv.json — JSON Resume schema, rendered by _includes/cv-template.html at /cv-json/ (currently disabled in _data/navigation.yml).

After editing _pages/cv.md, regenerate the JSON:

./scripts/update_cv_json.sh
# or directly:
python3 scripts/cv_markdown_to_json.py --input _pages/cv.md --output _data/cv.json --config _config.yml

Publications / talks generators (optional)

markdown_generator/ contains Jupyter notebooks and .py equivalents that turn publications.tsv / talks.tsv into individual files under _publications/ and _talks/. Only needed when bulk-importing; single entries are usually authored as Markdown directly.

Talk map

talkmap.ipynb / talkmap.py geocode talk locations into talkmap.html. A GitHub Actions workflow (.github/workflows/scrape_talks.yml) re-runs the notebook and commits back whenever talks/**, _talks/**, or talkmap.ipynb change on push.

Architecture

Jekyll collections (content lives here)

Defined in _config.yml under collections. Each item is a Markdown file whose front matter determines rendering:

• _publications/ → /publication/:path/ — papers. category in front matter must match a key in publication_category in _config.yml (books, manuscripts, conferences) to appear under the right heading on /publications/. Rich fields supported by _includes/archive-single.html: authors, venue, arxiv, project_page, bibtex, header.teaser.
• _talks/ → /talks/:path/ — uses layout: talk (_layouts/talk.html) by default.
• _portfolio/, _teaching/ — analogous.
• _posts/ → blog posts, surfaced via /year-archive/.
• _pages/ — standalone pages. The landing page is _pages/about.html (permalink /), a single-page site using the standalone _layouts/homepage.html layout (its own assets/css/homepage.css + assets/js/homepage.js, not the theme’s main.css). It inlines the publication list by looping over site.publications and rendering each with _includes/paper-card.html. Top-nav anchors come from _data/navigation.yml and must match the section ids in about.html.

Site-wide defaults: in _config.yml set the default layout and sidebar (author_profile: true) per collection; individual files can override via front matter.

Navigation

Top-nav items are controlled by _data/navigation.yml. Commenting out an entry removes it from the header without deleting the underlying page — this is how Talks / Teaching / Portfolio / JSON-CV are currently hidden. The page files still exist and render at their permalinks.

Layouts, includes, styling

• _layouts/ — top-level page shells (single.html, archive.html, talk.html, cv-layout.html, splash.html, default.html, compress.html). compress.html is a minifier wrapper applied via layout: chaining.
• _includes/ — partials. Key ones when changing site chrome: masthead.html, author-profile.html (sidebar, driven by author: block in _config.yml), archive-single.html (publication/talk entry rendering), cv-template.html (JSON-CV renderer), head/custom.html (extra <head> injection point).
• _sass/ — partials imported by assets/css/main.scss. Theme palettes in _sass/theme/ (_default_light.scss, _default_dark.scss, _air_light.scss, _air_dark.scss) are selected via site_theme in _config.yml. Layout-level SCSS in _sass/layout/.
• _data/authors.yml — alternate author profiles referenced by author: in a post’s front matter (multi-author sites).
• _data/ui-text.yml — i18n strings; selected via locale in _config.yml.

Author profile / personal data

The sidebar shown on most pages is driven entirely by the author: block in _config.yml (avatar, bio, links to GitHub/Scholar/ORCID/LinkedIn/etc.). An empty field hides the corresponding icon — do not delete the key, just leave it blank.

Avatar image lives in images/; filename is referenced by author.avatar in _config.yml.

Static files

• files/ — PDFs and downloads. Served verbatim; link as /files/foo.pdf.
• images/ — site imagery including publication teasers (referenced by header.teaser in publication front matter).
• assets/ — compiled CSS/JS/fonts. assets/js/_main.js is the source; assets/js/main.min.js is the build output (see JS asset build above).

GitHub Pages build constraint

The site is built by GitHub Pages’ safe-mode Jekyll. Plugins must be listed in the whitelist: block in _config.yml in addition to plugins: — adding a plugin that isn’t GitHub-Pages-supported will break the deployed build even if it works locally.

Jupyter notebook markdown generator

These .ipynb files are Jupyter notebook files that convert a TSV containing structured data about talks (talks.tsv) or presentations (presentations.tsv) into individual markdown files that will be properly formatted for the academicpages template. The notebooks contain a lot of documentation about the process. The .py files are pure python that do the same things if they are executed in a terminal, they just don’t have pretty documentation.

<?xml version=”1.0” encoding=”UTF-8”?> {% if page.xsl %}<?xml-stylesheet type=”text/xsl” href=”{{ “/sitemap.xsl” | absolute_url }}”?> {% endif %} {% assign collections = site.collections | where_exp:'collection','collection.output != false' %}{% for collection in collections %}{% assign docs = collection.docs | where_exp:'doc','doc.sitemap != false' %}{% for doc in docs %}

{% if doc.last_modified_at or doc.date %}{{ doc.last_modified_at | default: doc.date | date_to_xmlschema }} {% endif %}</url> {% endfor %}{% endfor %}{% assign pages = site.html_pages | where_exp:’doc’,’doc.sitemap != false’ | where_exp:’doc’,’doc.url != “/404.html”’ %}{% for page in pages %}

{% if page.last_modified_at %}{{ page.last_modified_at | date_to_xmlschema }} {% endif %}</url> {% endfor %}{% assign static_files = page.static_files | where_exp:’page’,’page.sitemap != false’ | where_exp:’page’,’page.name != “404.html”’ %}{% for file in static_files %}

</url> {% endfor %}</urlset>

This is an item in your portfolio. It can be have images or nice text. If you name the file .md, it will be parsed as markdown. If you name the file .html, it will be parsed as HTML.

This is an item in your portfolio. It can be have images or nice text. If you name the file .md, it will be parsed as markdown. If you name the file .html, it will be parsed as HTML.

Abstract: Emotion plays a pivotal role in video-based expression, but existing video generation systems predominantly focus on low-level visual metrics while neglecting affective dimensions. Although emotion analysis has made progress in the visual domain, the video community lacks dedicated resources to bridge emotion understanding with generative tasks, particularly for stylized and non-realistic contexts. To address this gap, we introduce EmoVid, the first multimodal, emotion-annotated video dataset specifically designed for creative media, which includes cartoon animations, movie clips, and animated stickers. Each video is annotated with emotion labels, visual attributes (brightness, colorfulness, hue), and text captions. Through systematic analysis, we uncover spatial and temporal patterns linking visual features to emotional perceptions across diverse video forms. Building on these insights, we develop an emotion-conditioned video generation technique by fine-tuning the Wan2.1 model. The results show a significant improvement in both quantitative metrics and the visual quality of generated videos for text-to-video and image-to-video tasks. EmoVid establishes a new benchmark for affective video computing. Our work not only offers valuable insights into visual emotion analysis in artistically styled videos, but also provides practical methods for enhancing emotional expression in video generation.

Abstract: Emotion is important for creating compelling virtual reality (VR) content. Although some generative methods have been applied to lower the barrier to creating emotionally rich content, they fail to capture the nuanced emotional semantics and the fine-grained control essential for immersive experiences. To address these limitations, we introduce EmoSpace, a novel framework for emotion-aware content generation that learns dynamic, interpretable emotion prototypes through vision-language alignment. We employ a hierarchical emotion representation with rich learnable prototypes that evolve during training, enabling fine-grained emotional control without requiring explicit emotion labels. We develop a controllable generation pipeline featuring multi-prototype guidance, temporal blending, and attention reweighting that supports diverse applications, including emotional image outpainting, stylized generation, and emotional panorama generation for VR environments. Our experiments demonstrate the superior performance of EmoSpace over existing methods in both qualitative and quantitative evaluations. Additionally, we present a comprehensive user study investigating how VR environments affect emotional perception compared to desktop settings. Our work facilitates immersive visual content generation with fine-grained emotion control and supports applications like therapy, education, storytelling, artistic creation, and cultural preservation. Code and models will be made publicly available.

Abstract: Unified multimodal models (UMMs) are motivated by the hope that understanding and generation reinforce each other but controlled ablations repeatedly find that adding a generation objective leaves understanding flat. Joint-training studies cannot settle the disagreement: with overlapping supervision, a gain cannot be attributed to the architecture rather than the data. To further investigate the relationship between the two directions in UMMs, we separate them by construction. A novel visual entity, a rendered 3D asset paired with a pseudo-word screened for absence from the frozen model’s behavior, is bound through exactly one task direction, and the untrained direction is then measured. We find that the channel is real in both directions, but the directions differ in kind: generation training installs a name the model can only match among candidates; understanding training installs one it can also produce. What governs cross-task usability is where the binding enters the shared computation. An alignment probe predicts export across 36 configurations (Spearman ρ = +0.68). That objective’s alignment term, maximized in closed form over activations with every weight frozen, makes a concept drawable when injected at layer 7 of 28 and is indistinguishable from the base model from layer 14 on, while the weight-based version of the same edit peaks at layers 10-14. In an observational series of four models, this window appears only where the understanding pathway is a semantic vision encoder, suggesting that unified weights are not enough: the two directions must share a semantic format at the entry point. Exploiting the rule, a mid-stack alignment objective acquires the concept for a 0.1% relative loss of the model’s general text-to-image ability, against 41% for the standard generative route.

Abstract: InternLumina-U2 unifies language, images, video, and 3D within a single multi-codebook diffusion large language model, sharing one MoE diffusion backbone across text question answering, image understanding and editing, image generation, video understanding, and 3D understanding. A multi-codebook visual tokenizer represents visual content as discrete VQ levels, processing spatial positions in parallel while sequentially decoding codebooks, enabling omni-visual understanding and generation within one model. The technical report is forthcoming; code is available on GitHub.

This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!

More information here

This is a description of your tutorial, note the different field in type. This is a markdown files that can be all markdown-ified like any other post. Yay markdown!

More information here

This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!

This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.

This is a description of a teaching experience. You can use markdown like any other post.

Heading 1

Heading 2

Heading 3

This is a description of a teaching experience. You can use markdown like any other post.

Heading 1

Heading 2

Heading 3

Sorry, but the page you were trying to view does not exist.

A variety of common markup showing how the theme styles them.

Header one

Header two

Header three

Header four

Header five

Header six

Blockquotes

Single line blockquote:

Quotes are cool.

Tables

Definition Lists

Definition List Title

Definition list division.

Startup

A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.

#dowork

Coined by Rob Dyrdek and his personal body guard Christopher “Big Black” Boykins, “Do Work” works as a self motivator, to motivating your friends.

Do It Live

I’ll let Bill O’Reilly explain this one.

Unordered Lists (Nested)

• List item one
  • List item one
    • List item one
    • List item two
    • List item three
    • List item four
  • List item two
  • List item three
  • List item four
• List item two
• List item three
• List item four

Ordered List (Nested)

1. List item one
   1. List item one
      1. List item one
      2. List item two
      3. List item three
      4. List item four
   2. List item two
   3. List item three
   4. List item four
2. List item two
3. List item three
4. List item four

Buttons

Make any link standout more when applying the .btn class.

Notices

Watch out! You can also add notices by appending {: .notice} to a paragraph.

HTML Tags

Address Tag

Anchor Tag (aka. Link)

This is an example of a link.

Abbreviation Tag

The abbreviation CSS stands for “Cascading Style Sheets”.

Cite Tag

“Code is poetry.” —Automattic

Code Tag

You will learn later on in these tests that word-wrap: break-word; will be your best friend.

Strike Tag

This tag will let you strikeout text.

Emphasize Tag

The emphasize tag should italicize text.

Insert Tag

This tag should denote inserted text.

Keyboard Tag

This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.

Preformatted Tag

This tag styles large blocks of code.

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

Quote Tag

Developers, developers, developers… –Steve Ballmer

Strong Tag

This tag shows bold text.

Subscript Tag

Getting our science styling on with H₂O, which should push the “2” down.

Superscript Tag

Still sticking with science and Isaac Newton’s E = MC², which should lift the 2 up.

Variable Tag

This allows you to denote variables.

Zongyang (Zane) Qiu 「邱宗扬」

Abstract

Zongyang (Zane) Qiu|邱宗扬

MPhil Student in Artificial Intelligence @ HKUST(GZ)

zane.zy.qiu [at] gmail.com

I'm Zongyang Qiu, an MPhil student at HKUST(GZ), advised by Prof. Hui Xiong, and co-advised by Prof. Zeyu Wang. I received my B.S. degree in Computer Science from Fudan University.

Currently, I am a student researcher at Shanghai Innovation Institute (SII), supervised by Yutao Sun, working on world models, specifically VLMs for embodied models. Previously, I was a research intern at InternLM Group, Shanghai AI Lab, supervised by Dr. Bin Fu, focusing on building the next-generation DLLM and Visual Agents. Before that, I worked with Dr. Zeyu Wang at CIS Lab, HKUST(GZ), and with Prof. Wenhan Luo at C4 Group, HKUST.

I also work closely with Yijiang Li and Nuno Vasconcelos at UCSD in the field of video reasoning.

I am deeply passionate about extensive topics in Computer Vision and Computer Graphics. I aim to explore the construction of biological-like visual intelligence, which should be capable of autonomous understanding, decision-making, and generation within both physical and digital worlds. My current research focuses on multimodal learning of VLMs and reasoning ability in visual generative models.

• Multimodal Learning
• Vision-Language Models
• Visual Generative Models
• Computer Vision & Graphics

Email Google Scholar GitHub LinkedIn X RedNote

News

[2026/09] Joined Shanghai Innovation Institute as a Student Researcher.

[2026/04] Honored to be named a Shanghai Outstanding Graduate (Top 5%).

[2026/02] Joined the Statistical Visual Computing Laboratory (SVCL) at UCSD as a Research Assistant.

[2026/01] Joined Shanghai AI Laboratory as a Research Intern.

[2026/01] Delighted to attend AAAI 2026 in Singapore and give an oral presentation.

Publications

InternLumina-U2: A Multi-Codebook Diffusion Large Language Model for Omni-Visual Understanding and Image Generation

Intern Lumina U2 Team, Shanghai AI Laboratory

Project Page /Code

Technical report (in preparation)

InternLumina-U2 unifies language, images, video, and 3D within a single multi-codebook diffusion large language model, sharing one MoE diffusion backbone across text question answering, image understanding and editing, image generation, video understanding, and 3D understanding. A multi-codebook visual tokenizer represents visual content as discrete VQ levels, processing spatial positions in parallel while sequentially decoding codebooks, enabling omni-visual understanding and generation within one model. The technical report is forthcoming; code is available on GitHub.

Where a New Concept Must Enter: Entry Point Gates Cross-Task Usability in Unified Multimodal Models

Zongyang Qiu, Yihan Wu, Kaixuan Fan, Bo Li, Hui Xiong

arXiv /Code /BibTeX

arXiv preprint

Unified multimodal models (UMMs) are motivated by the hope that understanding and generation reinforce each other but controlled ablations repeatedly find that adding a generation objective leaves understanding flat. Joint-training studies cannot settle the disagreement: with overlapping supervision, a gain cannot be attributed to the architecture rather than the data. To further investigate the relationship between the two directions in UMMs, we separate them by construction. A novel visual entity, a rendered 3D asset paired with a pseudo-word screened for absence from the frozen model’s behavior, is bound through exactly one task direction, and the untrained direction is then measured. We find that the channel is real in both directions, but the directions differ in kind: generation training installs a name the model can only match among candidates; understanding training installs one it can also produce. What governs cross-task usability is where the binding enters the shared computation. An alignment probe predicts export across 36 configurations (Spearman ρ = +0.68). That objective’s alignment term, maximized in closed form over activations with every weight frozen, makes a concept drawable when injected at layer 7 of 28 and is indistinguishable from the base model from layer 14 on, while the weight-based version of the same edit peaks at layers 10-14. In an observational series of four models, this window appears only where the understanding pathway is a semantic vision encoder, suggesting that unified weights are not enough: the two directions must share a semantic format at the entry point. Exploiting the rule, a mid-stack alignment objective acquires the concept for a 0.1% relative loss of the model’s general text-to-image ability, against 41% for the standard generative route.

EmoSpace: Fine-Grained Emotion Prototype Learning for Immersive Affective Content Generation

Bingyuan Wang, Xingbei Chen, Zongyang Qiu, Linping Yuan, Zeyu Wang

arXiv /BibTeX

arXiv preprint

Emotion is important for creating compelling virtual reality (VR) content. Although some generative methods have been applied to lower the barrier to creating emotionally rich content, they fail to capture the nuanced emotional semantics and the fine-grained control essential for immersive experiences. To address these limitations, we introduce EmoSpace, a novel framework for emotion-aware content generation that learns dynamic, interpretable emotion prototypes through vision-language alignment. We employ a hierarchical emotion representation with rich learnable prototypes that evolve during training, enabling fine-grained emotional control without requiring explicit emotion labels. We develop a controllable generation pipeline featuring multi-prototype guidance, temporal blending, and attention reweighting that supports diverse applications, including emotional image outpainting, stylized generation, and emotional panorama generation for VR environments. Our experiments demonstrate the superior performance of EmoSpace over existing methods in both qualitative and quantitative evaluations. Additionally, we present a comprehensive user study investigating how VR environments affect emotional perception compared to desktop settings. Our work facilitates immersive visual content generation with fine-grained emotion control and supports applications like therapy, education, storytelling, artistic creation, and cultural preservation. Code and models will be made publicly available.

EmoVid: A Multimodal Emotion Video Dataset for Emotion-Centric Video Understanding and Generation

Zongyang Qiu, Bingyuan Wang, Xingbei Chen, Yingqing He, Zeyu Wang

arXiv /Project Page /BibTeX

AAAI 2026 (Oral)

Emotion plays a pivotal role in video-based expression, but existing video generation systems predominantly focus on low-level visual metrics while neglecting affective dimensions. Although emotion analysis has made progress in the visual domain, the video community lacks dedicated resources to bridge emotion understanding with generative tasks, particularly for stylized and non-realistic contexts. To address this gap, we introduce EmoVid, the first multimodal, emotion-annotated video dataset specifically designed for creative media, which includes cartoon animations, movie clips, and animated stickers. Each video is annotated with emotion labels, visual attributes (brightness, colorfulness, hue), and text captions. Through systematic analysis, we uncover spatial and temporal patterns linking visual features to emotional perceptions across diverse video forms. Building on these insights, we develop an emotion-conditioned video generation technique by fine-tuning the Wan2.1 model. The results show a significant improvement in both quantitative metrics and the visual quality of generated videos for text-to-video and image-to-video tasks. EmoVid establishes a new benchmark for affective video computing. Our work not only offers valuable insights into visual emotion analysis in artistically styled videos, but also provides practical methods for enhancing emotional expression in video generation.

Experience

Student Researcher

Shanghai Innovation Institute (上海创智学院)

2026.09 – Present

Research Assistant

Statistical Visual Computing Lab, UCSD

2026.02 – Present

Research Intern

Shanghai AI Laboratory

2026.01 – 2026.09

Research Assistant

HKUST (Guangzhou)

2025.03 – 2025.08

Education

The Hong Kong University of Science and Technology (Guangzhou)

2026.09 – 2028 (Expected)

MPhil in Artificial Intelligence

Advised by Prof. Hui Xiong (Fellow of ACM, CCF, AAAI, AAAS, and IEEE)

Research Topic: Multimodal Reasoning and Visual Generative AI

Fudan University (复旦大学)

2022.09 – 2026.06

B.S. in Computer Science · GPA 3.74/4.0

Coursework: Programming (A), Artificial Intelligence (Honors, A), Digital Image Processing (A), Set and Graph Theory (Honors, A)

The Hong Kong University of Science and Technology

2024.08 – 2024.12

Exchange Student (Bachelor's level)

Coursework: Design and Analysis of Algorithms, Computer Graphics, Probability

Awards and Honors

2026

Shanghai Outstanding Graduate (Top 5%)

2025

National Scholarship (Top 1%, ranked 1st in the school)

2025

Meritorious Winner, Mathematical Contest in Modeling (Top 7%)

2024

Gold Award, China International College Student Innovation Competition

Service

Reviewer

AAAI Conference on Artificial Intelligence (AAAI 2026)
IEEE Transactions on Affective Computing

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

"themes",
"theme/{{ site.site_theme | default: 'default' | append: '_light' }}",
"theme/{{ site.site_theme | default: 'default' | append: '_dark' }}",

"include/mixins",
"vendor/susy/susy",

"layout/reset",
"layout/base",

"include/utilities",
"layout/tables",
"layout/buttons",
"layout/notices",
"layout/masthead",
"layout/navigation",
"layout/footer",
"syntax",

"layout/forms",

"layout/page",
"layout/archive",
"layout/sidebar",
"layout/json_cv",

"vendor/font-awesome/fontawesome",
"vendor/font-awesome/solid",
"vendor/font-awesome/brands" ;

    ```mermaid
    graph LR
    A-->B
    ```

graph LR
A-->B

---
config:
  theme: 'forest'
---
graph TD;
    A-->B;
    A-->C;
    B-->D;
    C-->D;

    ```plotly
    {
      "data": [
        {
          "x": [1, 2, 3, 4],
          "y": [10, 15, 13, 17],
          "type": "scatter"
        },
        {
          "x": [1, 2, 3, 4],
          "y": [16, 5, 11, 9],
          "type": "scatter"
        }
      ]
    }
    ```

{
  "data": [
    {
      "x": [1, 2, 3, 4],
      "y": [10, 15, 13, 17],
      "type": "scatter"
    },
    {
      "x": [1, 2, 3, 4],
      "y": [16, 5, 11, 9],
      "type": "scatter"
    }
  ]
}

{
  "data": [
    {
      "x": [1, 2, 3, 4, 5],
      "y": [1, 6, 3, 6, 1],
      "mode": "markers",
      "type": "scatter",
      "name": "Team A",
      "text": ["A-1", "A-2", "A-3", "A-4", "A-5"],
      "marker": { "size": 12 }
    },
    {
      "x": [1.5, 2.5, 3.5, 4.5, 5.5],
      "y": [4, 1, 7, 1, 4],
      "mode": "markers",
      "type": "scatter",
      "name": "Team B",
      "text": ["B-a", "B-b", "B-c", "B-d", "B-e"],
      "marker": { "size": 12 }
    }    
  ],
  "layout": {
    "xaxis": {
      "range": [ 0.75, 5.25 ]
    },
    "yaxis": {
      "range": [0, 8]
    },
    "title": {"text": "Data Labels Hover"}
  }
}

{
  "data": [{
      "x": [1, 2, 3],
      "y": [4, 5, 6],
      "type": "scatter"
    },
    {
      "x": [20, 30, 40],
      "y": [50, 60, 70],
      "xaxis": "x2",
      "yaxis": "y2",
      "type": "scatter"
  }],
  "layout": {
    "grid": {
      "rows": 1,
      "columns": 2,
      "pattern": "independent"
    },
    "title": {
      "text": "Simple Subplot"
    }    
  }
}

{
  "data": [{
		"z": [[10, 10.625, 12.5, 15.625, 20],
          [5.625, 6.25, 8.125, 11.25, 15.625],
          [2.5, 3.125, 5.0, 8.125, 12.5],
          [0.625, 1.25, 3.125, 6.25, 10.625],
          [0, 0.625, 2.5, 5.625, 10]],
		"type": "contour"
	}],
  "layout": {
    "title": {
      "text": "Basic Contour Plot"
    }
  }
}

**Watch out!** You can also add notices by appending `{: .notice}` to the line following paragraph.
{: .notice}

This is the regular text.[^1] This is more regular text.[^note]

[^1]: This is the footnote itself.
[^note]: This is another footnote.

print('Hello World!')

print("Hello World!", quote = FALSE)

<details>
  <summary>Collapsed by default</summary>
  This section was collapsed by default!
</details>

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

bundle install                          # first time, or after Gemfile changes
bundle exec jekyll serve -l -H localhost  # live-reload server on localhost:4000

docker compose up                       # serves at localhost:4000, auto-rebuilds

npm install
npm run build:js      # one-shot uglify
npm run watch:js      # rebuild on change

./scripts/update_cv_json.sh
# or directly:
python3 scripts/cv_markdown_to_json.py --input _pages/cv.md --output _data/cv.json --config _config.yml

Education

• HKUST(GZ), MPhil in Artificial Intelligence (Sept 2026 – Present)
  • Advisor: Prof. Hui Xiong
• Fudan University, BS in Computer Science (Sept 2022 – June 2026)
  • GPA: 3.73/4.0, National Scholarship (2024 – 2025)
  • Coursework: Programming (Grade: A), Artificial Intelligence (Honors, Grade: A), Digital Image Processing (Grade: A), Set and Graph Theory (Honors, Grade: A)
• HKUST, Exchange Program on Bachelor’s level (Aug 2024 – Dec 2024)
  • Coursework: Design and Analysis of Algorithms, Computer Graphics, Probability

Research Experience

• Research Intern, CIS Lab, HKUST(GZ) (Mar 2025 – Aug 2025)
  • Supervisor: Dr. Zeyu Wang
  • Independently curated a large-scale multimodal video dataset with fine-grained emotion labels, and developed emotion-conditioned video generation methods.
  • Built a VR platform for immersive emotion elicitation and participated in an open-source project about sketch.
• Research Intern, Hong Kong Generative AI Research and Development Center (Aug 2024 – Dec 2024)
  • Supervisor: Prof. Wenhan Luo
  • Constructed a dataset of deepfake human animation videos.
  • Implemented a fully temporal convolution network (FTCN) to detect video deepfakes.

Skills

• Programming Languages: Python, C++, C, SQL
• Technologies: PyTorch, Deep Learning, LaTeX, Academic English (TOEFL 103), Photoshop & Premiere
• Research Interests: Video Generation, 3D Vision, MLLM

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ent:””}.fa-free-code-camp:before{content:””}.fa-hornbill:before{content:””}.fa-js:before{content:””}.fa-ideal:before{content:””}.fa-git:before{content:””}.fa-dev:before{content:””}.fa-sketch:before{content:””}.fa-yandex-international:before{content:””}.fa-cc-amex:before{content:””}.fa-uber:before{content:””}.fa-github:before{content:””}.fa-php:before{content:””}.fa-alipay:before{content:””}.fa-youtube:before{content:””}.fa-skyatlas:before{content:””}.fa-firefox-browser:before{content:””}.fa-replyd:before{content:””}.fa-suse:before{content:””}.fa-jenkins:before{content:””}.fa-twitter:before{content:””}.fa-rockrms:before{content:””}.fa-pinterest:before{content:””}.fa-buffer:before{content:””}.fa-npm:before{content:””}.fa-yammer:before{content:””}.fa-btc:before{content:””}.fa-dribbble:before{content:””}.fa-stumbleupon-circle:before{content:””}.fa-internet-explorer:before{content:””}.fa-stubber:before{content:””}.fa-telegram:before{content:””}.fa-telegram-plane:before{content:””}.fa-old-republic:before{content:””}.fa-odysee:before{content:””}.fa-square-whatsapp:before{content:””}.fa-whatsapp-square:before{content:””}.fa-node-js:before{content:””}.fa-edge-legacy:before{content:””}.fa-slack:before{content:””}.fa-slack-hash:before{content:””}.fa-medrt:before{content:””}.fa-usb:before{content:””}.fa-tumblr:before{content:””}.fa-vaadin:before{content:””}.fa-quora:before{content:””}.fa-square-x-twitter:before{content:””}.fa-reacteurope:before{content:””}.fa-medium:before{content:””}.fa-medium-m:before{content:””}.fa-amilia:before{content:””}.fa-mixcloud:before{content:””}.fa-flipboard:before{content:””}.fa-viacoin:before{content:””}.fa-critical-role:before{content:””}.fa-sitrox:before{content:””}.fa-discourse:before{content:””}.fa-joomla:before{content:””}.fa-mastodon:before{content:””}.fa-airbnb:before{content:””}.fa-wolf-pack-battalion:before{content:””}.fa-buy-n-large:before{content:””}.fa-gulp:before{content:””}.fa-creative-commons-sampling-plus:before{content:””}.fa-strava:before{content:””}.fa-ember:before{content:””}.fa-canadian-maple-leaf:before{content:””}.fa-teamspeak:before{content:””}.fa-pushed:before{content:””}.fa-wordpress-simple:before{content:””}.fa-nutritionix:before{content:””}.fa-wodu:before{content:””}.fa-google-pay:before{content:””}.fa-intercom:before{content:””}.fa-zhihu:before{content:””}.fa-korvue:before{content:””}.fa-pix:before{content:””}.fa-steam-symbol:before{content:””}

Locations of key files/directories

• Basic config options: _config.yml
• Top navigation bar config: _data/navigation.yml
• Single pages: _pages/
• Collections of pages are .md or .html files in:
  • _publications/
  • _portfolio/
  • _posts/
  • _teaching/
  • _talks/
• Footer: _includes/footer.html
• Static files (like PDFs): /files/
• Profile image (can set in _config.yml): images/profile.png

Tips and hints

• Name a file “.md” to have it render in markdown, name it “.html” to render in HTML.
• Go to the commit list (on your repo) to find the last version GitHub built with Jekyll.
  • Green check: successful build
  • Orange circle: building
  • Red X: error
  • No icon: not built
• Academic Pages uses Jekyll Kramdown, GitHub Flavored Markdown (GFM) parser, which is similar to the version of Markdown used on GitHub, but may have some minor differences.
  • Some of emoji supported on GitHub should be supposed via the Jemoji plugin .
  • The best list of the supported emoji can be found in the Emojis for Jekyll via Jemoji blog post.
• While GitHub Pages prevents server side code from running, client-side scripts are supported.
  • This means that Google Analytics is supported, and the wiki should contain the most up-to-date information on getting it working.

• Your CV can be written using either Markdown (preview) or generated via JSON (preview) and the layouts are slightly different. You can update the path to the one being used in _data/navigation.yml with the JSON formatted CV being hidden by default.

• The Liquid syntax guide is a useful guide for those that want to add functionality to the template or to become contributors to the template on GitHub.

MathJax

Support for MathJax (version 3.* via jsDelivr, documentation) is included in the template:

[\displaylines{ \nabla \cdot E= \frac{\rho}{\epsilon_0} \
\nabla \cdot B=0 \
\nabla \times E= -\partial_tB \
\nabla \times B = \mu_0 \left(J + \varepsilon_0 \partial_t E \right) }]

The default delimiters of $$...$$ and \\[...\\] are supported for displayed mathematics, while \\(...\\) should be used for in-line mathematics (ex., \(a^2 + b^2 = c^2\))

Note that since Academic Pages uses Markdown which cases some interference with MathJax and LaTeX for escaping characters and new lines, although some workarounds exist. In some cases, such as when you are including MathJax in a citation field for publications, it may be necessary to use \(...\) for inline delineation.

Mermaid diagrams

Academic Pages includes support for Mermaid diagrams (version 11.* via jsDelivr) and in addition to their tutorials and GitHub documentation the basic syntax is as follows:

    ```mermaid
    graph LR
    A-->B
    ```

Which produces the following plot with the default theme applied:

graph LR
A-->B

While a more advanced plot with the forest theme applied looks like the following:

---
config:
  theme: 'forest'
---
graph TD;
    A-->B;
    A-->C;
    B-->D;
    C-->D;

Plotly

Academic Pages includes support for Plotly diagrams via a hook in the Markdown code elements, although those that are comfortable with HTML and JavaScript can also access it via those routes.

In order to render a Plotly plot via Markdown the relevant plot data need to be added as follows:

    ```plotly
    {
      "data": [
        {
          "x": [1, 2, 3, 4],
          "y": [10, 15, 13, 17],
          "type": "scatter"
        },
        {
          "x": [1, 2, 3, 4],
          "y": [16, 5, 11, 9],
          "type": "scatter"
        }
      ]
    }
    ```

Important! Since the data is parsed as JSON all of the keys will need to be quoted for the plot to render. The use of a tool like JSONLint to check syntax is highly recommended.

Which produces the following:

{
  "data": [
    {
      "x": [1, 2, 3, 4],
      "y": [10, 15, 13, 17],
      "type": "scatter"
    },
    {
      "x": [1, 2, 3, 4],
      "y": [16, 5, 11, 9],
      "type": "scatter"
    }
  ]
}

Essentially what is taking place is that the Plotly attributes are being taken from the code block as JSON data, parsed, and passed to Plotly along with a theme that matches the current site theme (i.e., a light theme, or a dark theme). This allows all plots that can be described via the data attribute to rendered with some limitations for the theme of the plot.

{
  "data": [
    {
      "x": [1, 2, 3, 4, 5],
      "y": [1, 6, 3, 6, 1],
      "mode": "markers",
      "type": "scatter",
      "name": "Team A",
      "text": ["A-1", "A-2", "A-3", "A-4", "A-5"],
      "marker": { "size": 12 }
    },
    {
      "x": [1.5, 2.5, 3.5, 4.5, 5.5],
      "y": [4, 1, 7, 1, 4],
      "mode": "markers",
      "type": "scatter",
      "name": "Team B",
      "text": ["B-a", "B-b", "B-c", "B-d", "B-e"],
      "marker": { "size": 12 }
    }    
  ],
  "layout": {
    "xaxis": {
      "range": [ 0.75, 5.25 ]
    },
    "yaxis": {
      "range": [0, 8]
    },
    "title": {"text": "Data Labels Hover"}
  }
}

{
  "data": [{
      "x": [1, 2, 3],
      "y": [4, 5, 6],
      "type": "scatter"
    },
    {
      "x": [20, 30, 40],
      "y": [50, 60, 70],
      "xaxis": "x2",
      "yaxis": "y2",
      "type": "scatter"
  }],
  "layout": {
    "grid": {
      "rows": 1,
      "columns": 2,
      "pattern": "independent"
    },
    "title": {
      "text": "Simple Subplot"
    }    
  }
}

{
  "data": [{
		"z": [[10, 10.625, 12.5, 15.625, 20],
          [5.625, 6.25, 8.125, 11.25, 15.625],
          [2.5, 3.125, 5.0, 8.125, 12.5],
          [0.625, 1.25, 3.125, 6.25, 10.625],
          [0, 0.625, 2.5, 5.625, 10]],
		"type": "contour"
	}],
  "layout": {
    "title": {
      "text": "Basic Contour Plot"
    }
  }
}

Markdown guide

Academic Pages uses kramdown for Markdown rendering, which has some differences from other Markdown implementations such as GitHub’s. In addition to this guide, please see the kramdown Syntax page for full documentation.

Header three

Header four

Header five

Header six

Blockquotes

Single line blockquote:

Quotes are cool.

Tables

Table 1

Table 2

Definition Lists

Definition List Title

Definition list division.

Startup

A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.

#dowork

Coined by Rob Dyrdek and his personal body guard Christopher “Big Black” Boykins, “Do Work” works as a self motivator, to motivating your friends.

Do It Live

I’ll let Bill O’Reilly explain this one.

Unordered Lists (Nested)

• List item one
  • List item one
    • List item one
    • List item two
    • List item three
    • List item four
  • List item two
  • List item three
  • List item four
• List item two
• List item three
• List item four

Ordered List (Nested)

1. List item one
   1. List item one
      1. List item one
      2. List item two
      3. List item three
      4. List item four
   2. List item two
   3. List item three
   4. List item four
2. List item two
3. List item three
4. List item four

Buttons

Make any link standout more when applying the .btn class.

Notices

Basic notices or call-outs are supported using the following syntax:

**Watch out!** You can also add notices by appending `{: .notice}` to the line following paragraph.
{: .notice}

which wil render as:

Watch out! You can also add notices by appending {: .notice} to the line following paragraph.

Footnotes

Footnotes can be useful for clarifying points in the text, or citing information.¹ Markdown support numeric footnotes, as well as text as long as the values are unique.²

This is the regular text.[^1] This is more regular text.[^note]

[^1]: This is the footnote itself.
[^note]: This is another footnote.

HTML Tags

Address Tag

Anchor Tag (aka. Link)

This is an example of a link.

Abbreviation Tag

The abbreviation CSS stands for “Cascading Style Sheets”.

Cite Tag

“Code is poetry.” —Automattic

Code Tag

You will learn later on in these tests that word-wrap: break-word; will be your best friend.

You can also write larger blocks of code with syntax highlighting supported for some languages, such as Python:

print('Hello World!')

or R:

print("Hello World!", quote = FALSE)

Details Tag (collapsible sections)

The HTML <details> tag works well with Markdown and allows you to include collapsible sections, see W3Schools for more information on how to use the tag.

The source code:

<details>
  <summary>Collapsed by default</summary>
  This section was collapsed by default!
</details>

Or, you can leave a section open by default by including the open attribute in the tag:

Emphasize Tag

The emphasize tag should italicize text.

Insert Tag

This tag should denote inserted text.

Keyboard Tag

This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.

Preformatted Tag

This tag styles large blocks of code.

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

Quote Tag

Developers, developers, developers… –Steve Ballmer

Strike Tag

This tag will let you strikeout text.

Strong Tag

This tag shows bold text.

Subscript Tag

Getting our science styling on with H₂O, which should push the “2” down.

Superscript Tag

Still sticking with science and Isaac Newton’s E = MC², which should lift the 2 up.

Variable Tag

This allows you to denote variables.

Footnotes

The footnotes in the page will be returned following this line, return to the section on Markdown Footnotes.

This is a page not in the menu. You can use markdown in this page.

Heading 1

Heading 2

{% include base_path %} {% for post in site.pages %} {% include archive-single.html %} {% endfor %}

{% include base_path %}

{% for post in site.portfolio %} {% include archive-single.html %} {% endfor %}

{% if site.author.googlescholar %}

{% endif %}

{% include base_path %}

{% if site.publication_category %} {% for category in site.publication_category %} {% assign title_shown = false %} {% for post in site.publications reversed %} {% if post.category != category[0] %} {% continue %} {% endif %} {% unless title_shown %} <h2>{{ category[1].title }}</h2><hr /> {% assign title_shown = true %} {% endunless %} {% include archive-single.html %} {% endfor %} {% endfor %} {% else %} {% for post in site.publications reversed %} {% include archive-single.html %} {% endfor %} {% endif %}

Service

• Reviewer, AAAI 2026
• Reviewer, IEEE Transactions on Affective Computing

{% include base_path %}

A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

{% for post in site.pages %} {% include archive-single.html %} {% endfor %}

Posts

{% for post in site.posts %} {% include archive-single.html %} {% endfor %}

{% capture written_label %}’None’{% endcapture %}

{% for collection in site.collections %} {% unless collection.output == false or collection.label == “posts” %} {% capture label %}{{ collection.label }}{% endcapture %} {% if label != written_label %}

{{ label }}

{% capture written_label %}{{ label }}{% endcapture %} {% endif %} {% endunless %} {% for post in collection.docs %} {% unless collection.output == false or collection.label == “posts” %} {% include archive-single.html %} {% endunless %} {% endfor %} {% endfor %}

{% include base_path %} {% include group-by-array collection=site.posts field=”tags” %}

{% for tag in group_names %} {% assign posts = group_items[forloop.index0] %}

{{ tag }}

{% for post in posts %} {% include archive-single.html %} {% endfor %} {% endfor %}

This map is generated from a Jupyter Notebook file in talkmap.ipynb, which mines the location fields in the .md files in _talks/.

{% if site.talkmap_link == true %}

See a map of all the places I've given a talk!

{% endif %}

{% for post in site.talks reversed %} {% include archive-single-talk.html %} {% endfor %}

{% include base_path %}

{% for post in site.teaching reversed %} {% include archive-single.html %} {% endfor %}

{% include base_path %} {% include toc %}

Privacy Policy

The privacy of my visitors is extremely important. This Privacy Policy outlines the types of personal information that is received and collected and how it is used.

First and foremost, I will never share your email address or any other personal information to anyone without your direct consent.

Log Files

Like many other websites, this site uses log files to help learn about when, from where, and how often traffic flows to this site. The information in these log files include:

• Internet Protocol addresses (IP)
• Types of browser
• Internet Service Provider (ISP)
• Date and time stamp
• Referring and exit pages
• Number of clicks

All of this information is not linked to anything that is personally identifiable.

Cookies and Web Beacons

When you visit this site “convenience” cookies are stored on your computer when you submit a comment to help you log in faster to Disqus the next time you leave a comment.

Third-party advertisers may also place and read cookies on your browser and/or use web beacons to collect information. This site has no access or control over these cookies. You should review the respective privacy policies on any and all third-party ad servers for more information regarding their practices and how to opt-out.

If you wish to disable cookies, you may do so through your web browser options. Instructions for doing so can be found on the specific web browsers’ websites.

Google Analytics

Google Analytics is a web analytics tool I use to help understand how visitors engage with this website. It reports website trends using cookies and web beacons without identifying individual visitors. You can read Google Analytics Privacy Policy.

{% include base_path %} {% capture written_year %}’None’{% endcapture %} {% for post in site.posts %} {% capture year %}{{ post.date | date: ‘%Y’ }}{% endcapture %} {% if year != written_year %} <h2 id="{{ year | slugify }}" class="archive__subtitle">{{ year }}</h2> {% capture written_year %}{{ year }}{% endcapture %} {% endif %} {% include archive-single.html %} {% endfor %}

@import “jekyll-theme-primer”;

{“/about/”:”https://zane-zyqiu.github.io/”,”/about.html”:”https://zane-zyqiu.github.io/”,”/resume-json”:”https://zane-zyqiu.github.io/cv-json/”,”/resume”:”https://zane-zyqiu.github.io/cv/”,”/md/”:”https://zane-zyqiu.github.io/markdown/”,”/markdown.html”:”https://zane-zyqiu.github.io/markdown/”,”/nmp/”:”https://zane-zyqiu.github.io/non-menu-page/”,”/nmp.html”:”https://zane-zyqiu.github.io/non-menu-page/”,”/wordpress/blog-posts/”:”https://zane-zyqiu.github.io/year-archive/”}

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Repository overview

This is Zongyang (Zane) Qiu’s personal academic portfolio site, built on top of the Academic Pages Jekyll template (a detached fork of Minimal Mistakes). It is deployed at https://zane-zyqiu.github.io via GitHub Pages (built from master).

Most day-to-day edits are content updates in Markdown/YAML. Structural changes touch Liquid templates, SCSS, and the asset build.

Common commands

Local development (native Ruby)

bundle install                          # first time, or after Gemfile changes
bundle exec jekyll serve -l -H localhost  # live-reload server on localhost:4000

Editing _config.yml requires restarting jekyll serve (Jekyll does not auto-reload config). Markdown/HTML edits are picked up automatically.

Local development (Docker)

docker compose up                       # serves at localhost:4000, auto-rebuilds

Uses _config.yml merged with _config_docker.yml. VS Code Dev Container is also configured (.devcontainer/devcontainer.json).

JS asset build

assets/js/main.min.js is the uglified bundle of jQuery + fitvids + smooth-scroll + greedy-nav + _main.js. Rebuild after editing any source JS:

npm install
npm run build:js      # one-shot uglify
npm run watch:js      # rebuild on change

CV pipeline

The CV has two rendering paths that must stay in sync:

• _pages/cv.md — Markdown source of truth, rendered at /cv/.
• _data/cv.json — JSON Resume schema, rendered by _includes/cv-template.html at /cv-json/ (currently disabled in _data/navigation.yml).

After editing _pages/cv.md, regenerate the JSON:

./scripts/update_cv_json.sh
# or directly:
python3 scripts/cv_markdown_to_json.py --input _pages/cv.md --output _data/cv.json --config _config.yml

Publications / talks generators (optional)

markdown_generator/ contains Jupyter notebooks and .py equivalents that turn publications.tsv / talks.tsv into individual files under _publications/ and _talks/. Only needed when bulk-importing; single entries are usually authored as Markdown directly.

Talk map

talkmap.ipynb / talkmap.py geocode talk locations into talkmap.html. A GitHub Actions workflow (.github/workflows/scrape_talks.yml) re-runs the notebook and commits back whenever talks/**, _talks/**, or talkmap.ipynb change on push.

Architecture

Jekyll collections (content lives here)

Defined in _config.yml under collections. Each item is a Markdown file whose front matter determines rendering:

• _publications/ → /publication/:path/ — papers. category in front matter must match a key in publication_category in _config.yml (books, manuscripts, conferences) to appear under the right heading on /publications/. Rich fields supported by _includes/archive-single.html: authors, venue, arxiv, project_page, bibtex, header.teaser.
• _talks/ → /talks/:path/ — uses layout: talk (_layouts/talk.html) by default.
• _portfolio/, _teaching/ — analogous.
• _posts/ → blog posts, surfaced via /year-archive/.
• _pages/ — standalone pages. The landing page is _pages/about.html (permalink /), a single-page site using the standalone _layouts/homepage.html layout (its own assets/css/homepage.css + assets/js/homepage.js, not the theme’s main.css). It inlines the publication list by looping over site.publications and rendering each with _includes/paper-card.html. Top-nav anchors come from _data/navigation.yml and must match the section ids in about.html.

Site-wide defaults: in _config.yml set the default layout and sidebar (author_profile: true) per collection; individual files can override via front matter.

Navigation

Top-nav items are controlled by _data/navigation.yml. Commenting out an entry removes it from the header without deleting the underlying page — this is how Talks / Teaching / Portfolio / JSON-CV are currently hidden. The page files still exist and render at their permalinks.

Layouts, includes, styling

• _layouts/ — top-level page shells (single.html, archive.html, talk.html, cv-layout.html, splash.html, default.html, compress.html). compress.html is a minifier wrapper applied via layout: chaining.
• _includes/ — partials. Key ones when changing site chrome: masthead.html, author-profile.html (sidebar, driven by author: block in _config.yml), archive-single.html (publication/talk entry rendering), cv-template.html (JSON-CV renderer), head/custom.html (extra <head> injection point).
• _sass/ — partials imported by assets/css/main.scss. Theme palettes in _sass/theme/ (_default_light.scss, _default_dark.scss, _air_light.scss, _air_dark.scss) are selected via site_theme in _config.yml. Layout-level SCSS in _sass/layout/.
• _data/authors.yml — alternate author profiles referenced by author: in a post’s front matter (multi-author sites).
• _data/ui-text.yml — i18n strings; selected via locale in _config.yml.

Author profile / personal data

The sidebar shown on most pages is driven entirely by the author: block in _config.yml (avatar, bio, links to GitHub/Scholar/ORCID/LinkedIn/etc.). An empty field hides the corresponding icon — do not delete the key, just leave it blank.

Avatar image lives in images/; filename is referenced by author.avatar in _config.yml.

Static files

• files/ — PDFs and downloads. Served verbatim; link as /files/foo.pdf.
• images/ — site imagery including publication teasers (referenced by header.teaser in publication front matter).
• assets/ — compiled CSS/JS/fonts. assets/js/_main.js is the source; assets/js/main.min.js is the build output (see JS asset build above).

GitHub Pages build constraint

The site is built by GitHub Pages’ safe-mode Jekyll. Plugins must be listed in the whitelist: block in _config.yml in addition to plugins: — adding a plugin that isn’t GitHub-Pages-supported will break the deployed build even if it works locally.

Jupyter notebook markdown generator

These .ipynb files are Jupyter notebook files that convert a TSV containing structured data about talks (talks.tsv) or presentations (presentations.tsv) into individual markdown files that will be properly formatted for the academicpages template. The notebooks contain a lot of documentation about the process. The .py files are pure python that do the same things if they are executed in a terminal, they just don’t have pretty documentation.

<?xml version=”1.0” encoding=”UTF-8”?> {% if page.xsl %}<?xml-stylesheet type=”text/xsl” href=”{{ “/sitemap.xsl” | absolute_url }}”?> {% endif %} {% assign collections = site.collections | where_exp:'collection','collection.output != false' %}{% for collection in collections %}{% assign docs = collection.docs | where_exp:'doc','doc.sitemap != false' %}{% for doc in docs %}

{% if doc.last_modified_at or doc.date %}{{ doc.last_modified_at | default: doc.date | date_to_xmlschema }} {% endif %}</url> {% endfor %}{% endfor %}{% assign pages = site.html_pages | where_exp:’doc’,’doc.sitemap != false’ | where_exp:’doc’,’doc.url != “/404.html”’ %}{% for page in pages %}

{% if page.last_modified_at %}{{ page.last_modified_at | date_to_xmlschema }} {% endif %}</url> {% endfor %}{% assign static_files = page.static_files | where_exp:’page’,’page.sitemap != false’ | where_exp:’page’,’page.name != “404.html”’ %}{% for file in static_files %}

</url> {% endfor %}</urlset>

This is an item in your portfolio. It can be have images or nice text. If you name the file .md, it will be parsed as markdown. If you name the file .html, it will be parsed as HTML.

This is an item in your portfolio. It can be have images or nice text. If you name the file .md, it will be parsed as markdown. If you name the file .html, it will be parsed as HTML.

Abstract: InternLumina-U2 unifies language, images, video, and 3D within a single multi-codebook diffusion large language model, sharing one MoE diffusion backbone across text question answering, image understanding and editing, image generation, video understanding, and 3D understanding. A multi-codebook visual tokenizer represents visual content as discrete VQ levels, processing spatial positions in parallel while sequentially decoding codebooks, enabling omni-visual understanding and generation within one model. The technical report is forthcoming; code is available on GitHub.

Abstract: Unified multimodal models (UMMs) are motivated by the hope that understanding and generation reinforce each other but controlled ablations repeatedly find that adding a generation objective leaves understanding flat. Joint-training studies cannot settle the disagreement: with overlapping supervision, a gain cannot be attributed to the architecture rather than the data. To further investigate the relationship between the two directions in UMMs, we separate them by construction. A novel visual entity, a rendered 3D asset paired with a pseudo-word screened for absence from the frozen model’s behavior, is bound through exactly one task direction, and the untrained direction is then measured. We find that the channel is real in both directions, but the directions differ in kind: generation training installs a name the model can only match among candidates; understanding training installs one it can also produce. What governs cross-task usability is where the binding enters the shared computation. An alignment probe predicts export across 36 configurations (Spearman ρ = +0.68). That objective’s alignment term, maximized in closed form over activations with every weight frozen, makes a concept drawable when injected at layer 7 of 28 and is indistinguishable from the base model from layer 14 on, while the weight-based version of the same edit peaks at layers 10-14. In an observational series of four models, this window appears only where the understanding pathway is a semantic vision encoder, suggesting that unified weights are not enough: the two directions must share a semantic format at the entry point. Exploiting the rule, a mid-stack alignment objective acquires the concept for a 0.1% relative loss of the model’s general text-to-image ability, against 41% for the standard generative route.

Abstract: Emotion is important for creating compelling virtual reality (VR) content. Although some generative methods have been applied to lower the barrier to creating emotionally rich content, they fail to capture the nuanced emotional semantics and the fine-grained control essential for immersive experiences. To address these limitations, we introduce EmoSpace, a novel framework for emotion-aware content generation that learns dynamic, interpretable emotion prototypes through vision-language alignment. We employ a hierarchical emotion representation with rich learnable prototypes that evolve during training, enabling fine-grained emotional control without requiring explicit emotion labels. We develop a controllable generation pipeline featuring multi-prototype guidance, temporal blending, and attention reweighting that supports diverse applications, including emotional image outpainting, stylized generation, and emotional panorama generation for VR environments. Our experiments demonstrate the superior performance of EmoSpace over existing methods in both qualitative and quantitative evaluations. Additionally, we present a comprehensive user study investigating how VR environments affect emotional perception compared to desktop settings. Our work facilitates immersive visual content generation with fine-grained emotion control and supports applications like therapy, education, storytelling, artistic creation, and cultural preservation. Code and models will be made publicly available.

Abstract: Emotion plays a pivotal role in video-based expression, but existing video generation systems predominantly focus on low-level visual metrics while neglecting affective dimensions. Although emotion analysis has made progress in the visual domain, the video community lacks dedicated resources to bridge emotion understanding with generative tasks, particularly for stylized and non-realistic contexts. To address this gap, we introduce EmoVid, the first multimodal, emotion-annotated video dataset specifically designed for creative media, which includes cartoon animations, movie clips, and animated stickers. Each video is annotated with emotion labels, visual attributes (brightness, colorfulness, hue), and text captions. Through systematic analysis, we uncover spatial and temporal patterns linking visual features to emotional perceptions across diverse video forms. Building on these insights, we develop an emotion-conditioned video generation technique by fine-tuning the Wan2.1 model. The results show a significant improvement in both quantitative metrics and the visual quality of generated videos for text-to-video and image-to-video tasks. EmoVid establishes a new benchmark for affective video computing. Our work not only offers valuable insights into visual emotion analysis in artistically styled videos, but also provides practical methods for enhancing emotional expression in video generation.