Add paper link, project page, code, and task category

Hi, I'm Niels from the community science team at Hugging Face. This PR improves the dataset card for the AniGen Sample Data by:
- Adding the `image-to-3d` task category to the metadata.
- Linking the dataset to its associated research paper ([AniGen: Unified S3 Fields for Animatable 3D Asset Generation](https://huggingface.co/papers/2604.08746)).
- Providing links to the official project page and GitHub repository.
- Adding training commands and a BibTeX citation for better usability.

README.md

@@ -1,22 +1,28 @@
 ---
 license: mit
-pretty_name: AniGen Sample Data
 size_categories:
-  - n<1K
+- n<1K
+pretty_name: AniGen Sample Data
 tags:
-  - 3d
-  - image
+- 3d
+- image
+task_categories:
+- image-to-3d
 configs:
-  - config_name: default
-    default: true
-    data_files:
-      - split: train
-        path: samples.csv
+- config_name: default
+  default: true
+  data_files:
+  - split: train
+    path: samples.csv
 ---
 
 # AniGen Sample Data
 
-This directory is a compact example subset of the AniGen training dataset.
+[Paper](https://huggingface.co/papers/2604.08746) | [Project Page](https://yihua7.github.io/AniGen_web/) | [GitHub](https://github.com/VAST-AI-Research/AniGen)
+
+This directory is a compact example subset of the AniGen training dataset, as presented in the paper [AniGen: Unified $S^3$ Fields for Animatable 3D Asset Generation](https://huggingface.co/papers/2604.08746). 
+
+AniGen is a unified framework that directly generates animate-ready 3D assets conditioned on a single image by representing shape, skeleton, and skinning as mutually consistent $S^3$ Fields.
 
 ## What Is Included
 
@@ -54,4 +60,38 @@ For a row with sample key `<file_identifier>`:
 - voxel files: `voxels/<file_identifier>.ply` and `voxels/<file_identifier>_skeleton.ply`
 - image feature: `features/dinov2_vitl14_reg/<file_identifier>.npz`
 - mesh latents: files under `latents/*/<file_identifier>.npz`
-- structure latents: files under `ss_latents/*/<file_identifier>.npz`
+- structure latents: files under `ss_latents/*/<file_identifier>.npz`
+
+## Sample Usage (Training)
+
+According to the [official repository](https://github.com/VAST-AI-Research/AniGen), you can use this data for training by following these stages:
+
+```bash
+# Stage 1: Skin AutoEncoder
+python train.py --config configs/anigen_skin_ae.json --output_dir outputs/anigen_skin_ae
+
+# Stage 2: Sparse Structure DAE
+python train.py --config configs/ss_dae.json --output_dir outputs/ss_dae
+
+# Stage 3: Structured Latent DAE
+python train.py --config configs/slat_dae.json --output_dir outputs/slat_dae
+
+# Stage 4: SS Flow Matching (image-conditioned generation)
+python train.py --config configs/ss_flow_duet.json --output_dir outputs/ss_flow_duet
+
+# Stage 5: SLAT Flow Matching (image-conditioned generation)
+python train.py --config configs/slat_flow_auto.json --output_dir outputs/slat_flow_auto
+```
+
+## Citation
+
+```bibtex
+@article{huang2026anigen,
+  title     = {AniGen: Unified $S^3$ Fields for Animatable 3D Asset Generation},
+  author    = {Huang, Yi-Hua and Zhou, Zi-Xin and He, Yuting and Chang, Chirui
+               and Pu, Cheng-Feng and Yang, Ziyi and Guo, Yuan-Chen
+               and Cao, Yan-Pei and Qi, Xiaojuan},
+  journal   = {ACM SIGGRAPH},
+  year      = {2026}
+}
+```