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TEXTRIX: Latent Attribute Grid for Native Texture Generation and Beyond

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# TEXTRIX: Latent Attribute Grid for Native Texture Generation and Beyond

<div align="center">
 Yifei Zeng1,2*&emsp;
 Yajie Bao1,2*&emsp;
 Jiachen Qian3,2&emsp;
 Shuang Wu1,2&emsp;
 Youtian Lin1&emsp;
 
 Hao Zhu1&emsp;
 Buyu Li4&emsp;
 Feihu Zhang2&emsp;
 Xun Cao1&emsp;
 Yao Yao1&dagger;
</div>

<div align="center">
 
 1Nanjing University&emsp;
 2DreamTech&emsp;
 3HKU&emsp;
 4OriginArk
 
 (* Equal Contribution, &dagger; Corresponding Author)
</div>

**Seamless texturing and precise segmentation with TEXTRIX.** Our model generates geometrically aligned textures and segmentations from a single-view input, avoiding the inter-view inconsistencies that commonly affect prevailing 3D generation.

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## ✨ News
- **[2025/12/05]** 🚀 We have released the paper and project page!
- **[Coming Soon]** 🔨 Code and model weights will be released soon. Please stay tuned!

## 📝 Abstract

Prevailing 3D texture generation methods, which often rely on multi-view fusion, are frequently hindered by inter-view inconsistencies and incomplete coverage of complex surfaces, limiting the fidelity and completeness of the generated content.

To overcome these challenges, we introduce **TEXTRIX**, a native 3D attribute generation framework for high-fidelity texture synthesis and downstream applications such as precise 3D part segmentation. Our approach constructs a **latent 3D attribute grid** and leverages a **Diffusion Transformer equipped with sparse attention**, enabling direct coloring of 3D models in volumetric space and fundamentally avoiding the limitations of multi-view fusion.

Built upon this native representation, the framework naturally extends to high-precision 3D segmentation by training the same architecture to predict semantic attributes on the grid. Extensive experiments demonstrate state-of-the-art performance on both tasks, producing seamless, high-fidelity textures and accurate 3D part segmentation with precise boundaries.

## 📖 Citation

If you find our work useful for your research, please consider citing our paper:

```bibtex
@article{zeng2025textrix,
  title={TEXTRIX: Latent Attribute Grid for Native Texture Generation and Beyond},
  author={Yifei Zeng and Yajie Bao and Jiachen Qian and Shuang Wu and Youtian Lin and Hao Zhu and Buyu Li and Feihu Zhang and Xun Cao and Yao Yao},
  journal={arXiv preprint arXiv:2512.02993},
  year={2025}
}

TEXTRIX

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