StructParts

Seeing Both the Forest and the Trees: Reusing Holistic 3D Priors for Part-Decomposed Generation

Yiqun Zhao, Binbin Huang, Haobin Duan, Zibo Zhao, Shenghua Gao (corresponding author).

NeurIPS 2026

Weights

This repository distributes only our BF16 part diffusion transformer (dit.safetensors, 6.10 GB) and VAE mesh decoder (vae_decoder.safetensors, 0.43 GB). Download both into the inference repository's checkpoints/ directory. manifest.json contains their SHA-256 hashes.

hf download Goatzyq/structparts dit.safetensors vae_decoder.safetensors manifest.json --local-dir checkpoints

The input is an image, a foreground mask, and a segmented 3D scaffold. See the inference README for dependencies, input preparation, usage, and limitations. Scaffold inaccuracies can persist in generated parts.

DINOv2

DINOv2 weights are not included here. Obtain the compatible encoder directly from facebook/dinov2-large using python scripts/download_dinov2.py in the inference repository. The helper pins revision 47b73eefe95e8d44ec3623f8890bd894b6ea2d6c.

Release status

Private preparation pending the arXiv release. The project page and code repositories may remain private until publication.

Acknowledgments and licensing

We thank Hunyuan3D-2.1 and UltraShape-1.0 for their open-source models and implementations. Consult LICENSE, Notice.txt, and retained notices in the inference repository for applicable upstream terms. DINOv2 is downloaded separately under its upstream license.

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