--- license: other license_name: pixal3d-license license_link: LICENSE extra_gated_eu_disallowed: true pipeline_tag: image-to-3d ---
# Pixal3D: Pixel-Aligned 3D Generation from Images

SIGGRAPH 2026

[Dong-Yang Li](https://ldyang694.github.io/)¹ · [Wang Zhao](https://thuzhaowang.github.io/)²* · [Yuxin Chen](https://orcid.org/0000-0002-7854-1072)² · [Wenbo Hu](https://wbhu.github.io/)² · [Meng-Hao Guo](https://menghaoguo.github.io/)¹ · [Fang-Lue Zhang](https://fanglue.github.io/)³ · [Ying Shan](https://www.linkedin.com/in/YingShanProfile)² · [Shi-Min Hu](https://cg.cs.tsinghua.edu.cn/shimin.htm)¹✉ ¹Tsinghua University (BNRist)    ²Tencent ARC Lab    ³Victoria University of Wellington *Project lead    ✉Corresponding author
**Pixal3D** generates high-fidelity 3D assets from a single image. Unlike previous methods that loosely inject image features via attention, Pixal3D explicitly lifts pixel features into 3D through back-projection, establishing direct pixel-to-3D correspondences. This enables near-reconstruction-level fidelity with detailed geometry and PBR textures. --- ## ✨ News - **May 2026**: Release the improved version based on [Trellis.2](https://github.com/microsoft/TRELLIS.2) backbone. 💪 - **May 2026**: Release inference code and online demo. 🤗 - **Apr 2026**: Our paper is accepted to SIGGRAPH 2026! 🎉 ## 📌 Branches | Branch | Description | |--------|-------------| | `main` | **Latest version** — improved implementation based on [Trellis.2](https://github.com/microsoft/TRELLIS.2) backbone with better performance. | | `paper` | **Paper version** — original implementation based on [Direct3D-S2](https://github.com/DreamTechAI/Direct3D-S2), corresponding to results reported in our SIGGRAPH 2026 paper. | > If you want to reproduce the results in our paper, please switch to the `paper` branch. ## 🎮 Try It Online You can try Pixal3D directly in your browser without any installation via our Hugging Face Gradio demo: 👉 [**Launch Demo**](https://huggingface.co/spaces/TencentARC/Pixal3D) ## 🚀 Getting Started ### Installation #### Step 1: Follow TRELLIS.2 Installation Please first follow the installation guide of [TRELLIS.2](https://github.com/microsoft/TRELLIS.2) to set up the base environment. #### Step 2: Install Additional Dependencies ```bash pip install -r requirements.txt ``` #### Step 3: Install utils3d ```bash pip install https://github.com/LDYang694/Storages/releases/download/20260430/utils3d-0.0.2-py3-none-any.whl ``` ### Usage #### Inference Generate a GLB mesh from a single image: ```bash python inference.py --image assets/test_image/0.png --output ./output.glb ``` ### Web Demo We provide a Gradio web demo for Pixal3D, which allows you to generate 3D meshes from images interactively. ```bash python app.py ``` ## 🤗 Acknowledgements This project is heavily built upon [Trellis.2](https://github.com/microsoft/TRELLIS.2) and [Direct3D-S2](https://github.com/DreamTechAI/Direct3D-S2). We also thank the following repos for their great contributions: [Trellis](https://github.com/microsoft/TRELLIS). ## 📄 Citation If you find this work useful, please consider citing: ```bibtex @article{li2026pixal3d, title = {Pixal3D: Pixel-Aligned 3D Generation from Images}, author = {Li, Dong-Yang and Zhao, Wang and Chen, Yuxin and Hu, Wenbo and Guo, Meng-Hao and Zhang, Fang-Lue and Shan, Ying and Hu, Shi-Min}, journal = {arXiv preprint arXiv:2605.10922}, year = {2026} } ```