Add links to paper, code, and project page

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}
+}
+```