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

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}
+}
+```