Pixal3D: Pixel-Aligned 3D Generation from Images

SIGGRAPH 2026

Dong-Yang Li¹ · Wang Zhao²* · Yuxin Chen² · Wenbo Hu² · Meng-Hao Guo¹ · Fang-Lue Zhang³ · Ying Shan² · Shi-Min Hu¹✉

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.

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✨ News

• May 2026: Release the improved version based on 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 backbone with better performance.
paper	Paper version — original implementation based on 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

🚀 Getting Started

Installation

Step 1: Follow TRELLIS.2 Installation

Please first follow the installation guide of TRELLIS.2 to set up the base environment.

Step 2: Install Additional Dependencies

pip install -r requirements.txt

Step 3: Install utils3d

pip install https://github.com/LDYang694/Storages/releases/download/20260430/utils3d-0.0.2-py3-none-any.whl

Note: requirements-hfdemo.txt is for the Hugging Face Spaces demo (H-series GPU architecture) and may not be compatible with other architectures.

Usage

Inference

Generate a GLB mesh from a single image:

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.

python app.py 

🤗 Acknowledgements

This project is heavily built upon Trellis.2 and Direct3D-S2. We sincerely thank the authors for their outstanding work on scalable 3D generation , which serves as the foundation of our codebase and model architecture.

We also thank the following repos for their great contributions:

• Direct3D-S2
• Trellis
• Trellis.2

📄 Citation

If you find this work useful, please consider citing:

@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}
}