Stroke3D

This repository contains the released checkpoints and data for Stroke3D: Lifting 2D Strokes into Rigged 3D Model via Latent Diffusion Models (ICLR 2026).

[OpenReview] [Code] [Project page]

Repository layout

Checkpoints

Data

  • data/Skeleton-Data/: shared skeleton-coordinate training data for Sk-VAE and Sk-DiT; rendered PNG images are excluded.
  • data/DPO-Data/: 2,000 SKA-DPO preference pairs.
  • data/TextuRig/: 6,633 captioned skeleton/GLB pairs; rendered PNG images are excluded.

Detailed environments, commands, and data formats are documented in the component READMEs in the Stroke3D code repository: SkDiff/README.md, SKDream_DPO/README.md, and TextuRig/README.md.

Usage notes

The SKDream/SKA-DPO checkpoint is not a standalone Diffusers pipeline. Use repository ID zhaors00/Stroke3D and subfolder ckpt/SKDream-SKA-DPO with the Stroke3D code.

Download the DPO archive with:

hf download zhaors00/Stroke3D   data/DPO-Data/stroke3d_ska_dpo_margin_0.10.tar   --local-dir stroke3d-assets

Download all TextuRig metadata and shards with:

hf download zhaors00/Stroke3D   --include "data/TextuRig/*"   --local-dir stroke3d-assets

Licensing and provenance

No blanket license is asserted over upstream checkpoints or source-derived assets. Review the component cards and all applicable upstream terms before redistribution or commercial use. Camera metadata in the historical DPO archive uses Python pickle and must only be loaded from a trusted release.

Citation

@inproceedings{zhao2026stroke3d,
  title     = {Stroke3D: Lifting 2D Strokes into Rigged 3D Model via Latent Diffusion Models},
  author    = {Zhao, Ruisi and Zheng, Haoren and Yang, Zongxin and Fan, Hehe and Yang, Yi},
  booktitle = {The Fourteenth International Conference on Learning Representations},
  year      = {2026},
  url       = {https://openreview.net/forum?id=VgOWxor3LV}
}
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