Improve model card title and integrate paper information

Hi, I'm Niels from the Hugging Face community team. This PR improves the model card for AniGen by:
1. Updating the title to the full paper title for better discoverability.
2. Adding the training dataset (`VAST-AI/AniGen-Sample-Dataset`) to the YAML metadata.
3. Linking the model checkpoints to the research paper and authors for better attribution.
4. Refined the layout to ensure easy navigation for users.

README.md

@@ -1,9 +1,9 @@
 ---
-license: mit
-language:
-- en
 base_model:
 - microsoft/TRELLIS-image-large
+language:
+- en
+license: mit
 pipeline_tag: image-to-3d
 tags:
 - animatable
@@ -11,10 +11,15 @@ tags:
 - 3D
 - Tripo
 - VAST
+datasets:
+- VAST-AI/AniGen-Sample-Dataset
 ---
-# AniGen_Weights
 
-Pretrained checkpoints for [AniGen](https://github.com/VAST-AI-Research/AniGen), a unified framework for generating animatable 3D assets from a single image.
+# AniGen: Unified $S^3$ Fields for Animatable 3D Asset Generation
+
+Pretrained checkpoints for **AniGen**, a unified framework for generating animatable 3D assets from a single image.
+
+**Authors**: Yi-Hua Huang, Zi-Xin Zou, Yuting He, Chirui Chang, Cheng-Feng Pu, Ziyi Yang, Yuan-Chen Guo, Yan-Pei Cao, Xiaojuan Qi.
 
 <p align="center">
   <a href="https://arxiv.org/pdf/2604.08746"><img src="https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white" alt="arXiv"></a>
@@ -24,8 +29,9 @@ Pretrained checkpoints for [AniGen](https://github.com/VAST-AI-Research/AniGen),
   <a href="https://github.com/VAST-AI-Research/AniGen"><img src="https://img.shields.io/badge/GitHub-Repository-black?logo=github&logoColor=white" alt="GitHub"></a>
 </p>
 
-This repository stores the contents of the `ckpts/` directory used by the AniGen codebase, including:
+AniGen represents shape, skeleton, and skinning as mutually consistent $S^3$ Fields (Shape, Skeleton, Skin) defined over a shared spatial domain. Built upon a two-stage flow-matching pipeline, it first synthesizes a sparse structural scaffold and then generates dense geometry and articulation in a structured latent space.
 
+This repository stores the contents of the `ckpts/` directory used by the AniGen codebase, including:
 - AniGen stage checkpoints
 - DINOv2 vision encoder weights
 - DSINE normal estimation weights
@@ -33,7 +39,7 @@ This repository stores the contents of the `ckpts/` directory used by the AniGen
 
 ## What Is Included
 
-The repository is organized exactly like the `ckpts/` folder expected by the main AniGen repo:
+The repository is organized exactly like the `ckpts/` folder expected by the [main AniGen repo](https://github.com/VAST-AI-Research/AniGen):
 
 ```text
 ckpts/
@@ -56,11 +62,8 @@ Approximate total size: about 23 GB.
 ## Recommended Checkpoints
 
 For most users, we recommend:
-
-- `ss_flow_duet` for sparse structure generation
-- `slat_flow_auto` for structured latent generation
-
-This combination is also the default setup used by the AniGen inference example.
+- `ss_flow_duet` for sparse structure generation (stronger skeleton detail)
+- `slat_flow_auto` for structured latent generation (automatic joint-count prediction)
 
 ## Checkpoint Overview
 
@@ -87,37 +90,24 @@ This combination is also the default setup used by the AniGen inference example.
 
 ## How To Use
 
-Clone the main AniGen repository first:
+First, clone the main AniGen repository:
 
 ```bash
 git clone --recurse-submodules https://github.com/VAST-AI-Research/AniGen.git
 cd AniGen
 ```
 
-Then download this weights repository so that the folder structure is preserved under the project root.
+### Download with `huggingface_hub`
 
-### Option 1: Download with `huggingface_hub`
+Download this weights repository so that the folder structure is preserved under the project root:
 
 ```bash
 python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='VAST-AI/AniGen_Weights', repo_type='model', local_dir='.', local_dir_use_symlinks=False)"
 ```
 
-After download, you should have paths like:
-
-```text
-ckpts/anigen/ss_flow_duet/ckpts/denoiser.pt
-ckpts/anigen/slat_flow_auto/ckpts/denoiser.pt
-ckpts/dsine/dsine.pt
-ckpts/vgg/vgg16-397923af.pth
-```
-
-### Option 2: Download from the web UI
-
-You can also download this repository from the Hugging Face file browser and place the entire `ckpts/` folder at the root of the AniGen project.
+### Run Inference
 
-## Run AniGen With These Weights
-
-Once the `ckpts/` folder is in place, you can run:
+Once the `ckpts/` folder is in place, you can run the minimal example:
 
 ```bash
 python example.py --image_path assets/cond_images/trex.png
@@ -129,20 +119,6 @@ Or launch the Gradio demo:
 python app.py
 ```
 
-## Notes
-
-- Keep the directory names unchanged. The AniGen code expects the exact `ckpts/...` layout shown above.
-- The code repository may automatically fetch missing files in some setups, but this weights repository is the recommended way to pre-download and manage checkpoints explicitly.
-- `slat_flow_control` supports joint density control, while `slat_flow_auto` is the best default for general use.
-
-## Related Links
-
-- Best AI 3D studio -- Tripo: https://www.tripo3d.ai
-- Main code repository: https://github.com/VAST-AI-Research/AniGen
-- Project page: https://yihua7.github.io/AniGen-web/
-- Demo: https://huggingface.co/spaces/VAST-AI/AniGen
-- Paper: https://arxiv.org/pdf/2604.08746
-
 ## Citation
 
 ```bibtex