| language: | |
| - en | |
| tags: | |
| - image-to-3d | |
| - part-decomposed-generation | |
| - structparts | |
| # 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** | |
| [Project page](https://zhao-yiqun.github.io/structparts/) · [Inference code and setup](https://github.com/zhao-yiqun/structparts-code) | |
| ## 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. | |
| ```bash | |
| 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](https://huggingface.co/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](https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1) and [UltraShape-1.0](https://github.com/PKU-YuanGroup/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. | |