Add links to paper, code, and project page

This PR updates the dataset card to include links to the official paper, the GitHub repository, and the project page. It also adds the `image-to-video` task category to the YAML metadata to improve the dataset's discoverability on the Hub.

README.md

@@ -1,109 +1,110 @@
----
-license: cc-by-nc-4.0
-task_categories:
-  - video-generation
-  - computer-vision
-language:
-  - en
-pretty_name: MACE-Dance
-size_categories:
-  - 10K<n<100K
----
-
-# 🎵 MACE-Dance Dataset
-
-**MACE-Dance** is a large-scale dataset for **music-driven dance video generation**, released with our SIGGRAPH 2026 paper:
-
-> **MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation**
-
-It is designed to support research on generating dance videos that are both:
-
-- 🕺 **kinematically plausible**
-- 🎨 **visually coherent**
-- 🎼 **well aligned with music**
-
----
-
-## ✨ Overview
-
-The dataset contains approximately:
-
-- **70K** dance video clips
-- **5–10 seconds** per clip
-- **116 hours** in total
-- **20+ dance genres**
-
-The data is curated from two complementary sources:
-
-### 1. Motion-centric subset
-- Derived from **FineDance**
-- Front-view rendered dance videos from 3D motion sequences
-- Focused on **professional dance motion quality**
-
-### 2. Appearance-centric subset
-- Collected from high-engagement internet dance videos
-- Focused on **visual appearance diversity and realism**
-
-This design helps benchmark both **motion quality** and **appearance quality** in music-driven dance video generation.
-
----
-
-## 📂 Folder Structure
-
-```bash
-MACE-Dance/
-├── Appearance/
-└── Kinematic/
-```
-
-> The exact file organization may vary depending on the released version.
-
----
-
-
-## 🧹 Data Curation
-
-For the in-the-wild subset, we apply a multi-stage cleaning pipeline:
-
-- ✂️ shot boundary detection
-- 🚶 motion filtering
-- 🧍 single-person filtering
-- ⏱️ clip segmentation into 5–10 second windows
-
-This improves data quality for the music-driven dance generation task.
-
----
-
-## 🎯 Intended Usage
-
-This dataset is intended for research on:
-
-- **music-driven dance video generation**
-- **music-driven 3D dance generation**
-- **pose-driven / motion-driven human animation**
-- **motion and appearance evaluation**
-
----
-
-## ⚠️ Notes
-
-- This dataset is released for **research purposes only**.
-- Please ensure your use complies with the corresponding license and platform policies.
-- Some samples may originate from internet videos and are curated for academic research.
-
----
-
-## 📚 Citation
-
-If you find this dataset useful, please cite:
-
-```bibtex
-@article{yang2026macedance,
-  title={MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation},
-  author={Yang, Kaixing and Zhu, Jiashu and Tang, Xulong and Peng, Ziqiao and Zhang, Xiangyue and Wang, Puwei and Wu, Jiahong and Chu, Xiangxiang and Liu, Hongyan and He, Jun},
-  journal={ACM Transactions on Graphics (SIGGRAPH 2026)},
-  year={2026}
-}
-```
-
----
+---
+language:
+- en
+license: cc-by-nc-4.0
+size_categories:
+- 10K<n<100K
+task_categories:
+- image-to-video
+- video-generation
+- computer-vision
+pretty_name: MACE-Dance
+---
+
+# 🎵 MACE-Dance Dataset
+
+[**Project Page**](https://macedance.github.io/) | [**Paper**](https://huggingface.co/papers/2512.18181) | [**Code**](https://github.com/AMAP-ML/MACE-Dance)
+
+**MACE-Dance** is a large-scale dataset for **music-driven dance video generation**, released with the paper:
+
+> **MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation**
+
+It is designed to support research on generating dance videos that are both:
+
+- 🕺 **kinematically plausible**
+- 🎨 **visually coherent**
+- 🎼 **well aligned with music**
+
+---
+
+## ✨ Overview
+
+The dataset (referred to as **MA-Data** in the paper) contains approximately:
+
+- **70K** dance video clips
+- **5–10 seconds** per clip
+- **116 hours** in total
+- **20+ dance genres**
+
+The data is curated from two complementary sources:
+
+### 1. Motion-centric subset
+- Derived from **FineDance**
+- Front-view rendered dance videos from 3D motion sequences
+- Focused on **professional dance motion quality**
+
+### 2. Appearance-centric subset
+- Collected from high-engagement internet dance videos
+- Focused on **visual appearance diversity and realism**
+
+This design helps benchmark both **motion quality** and **appearance quality** in music-driven dance video generation.
+
+---
+
+## 📂 Folder Structure
+
+```bash
+MACE-Dance/
+├── Appearance/
+└── Kinematic/
+```
+
+> The exact file organization may vary depending on the released version.
+
+---
+
+
+## 🧹 Data Curation
+
+For the in-the-wild subset, we apply a multi-stage cleaning pipeline:
+
+- ✂️ shot boundary detection
+- 🚶 motion filtering
+- 🧍 single-person filtering
+- ⏱️ clip segmentation into 5–10 second windows
+
+This improves data quality for the music-driven dance generation task.
+
+---
+
+## 🎯 Intended Usage
+
+This dataset is intended for research on:
+
+- **music-driven dance video generation**
+- **music-driven 3D dance generation**
+- **pose-driven / motion-driven human animation**
+- **motion and appearance evaluation**
+
+---
+
+## ⚠️ Notes
+
+- This dataset is released for **research purposes only**.
+- Please ensure your use complies with the corresponding license and platform policies.
+- Some samples may originate from internet videos and are curated for academic research.
+
+---
+
+## 📚 Citation
+
+If you find this dataset useful, please cite:
+
+```bibtex
+@article{yang2026macedance,
+  title={MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation},
+  author={Yang, Kaixing and Zhu, Jiashu and Tang, Xulong and Peng, Ziqiao and Zhang, Xiangyue and Wang, Puwei and Wu, Jiahong and Chu, Xiangxiang and Liu, Hongyan and He, Jun},
+  journal={ACM Transactions on Graphics (SIGGRAPH 2026)},
+  year={2026}
+}
+```