README.md

metadata

license: mit
size_categories:
  - n<1K
pretty_name: AniGen Sample Data
tags:
  - 3d
  - image
task_categories:
  - image-to-3d
configs:
  - config_name: default
    default: true
    data_files:
      - split: train
        path: samples.csv

AniGen Sample Data

Paper | Project Page | GitHub

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.

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

• 10 examples
• 10 unique raw assets
• Full cross-modal files for each example
• A subset metadata.csv with 10 rows

The retained directory layout follows the core structure of the reference test set:

raw/
renders/
renders_cond/
skeleton/
voxels/
features/
metadata.csv
statistics.txt

latents/ (encoded by the trained slat auto-encoder)
ss_latents/  (encoded by the trained ss auto-encoder)

How To Read One Example

Each row in metadata.csv corresponds to one example identifier in the sha256 column. In practice this value is the sample key used across modalities.

For a row with sample key <file_identifier>:

• raw asset: local_path field, for example raw/<raw_file>
• rendered views: renders/<file_identifier>/
• conditional rendered views: renders_cond/<file_identifier>/
• skeleton files: skeleton/<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

Sample Usage (Training)

According to the official repository, you can use this data for training by following these stages:

# 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

@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}
}