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arXiv · 2512.02993

TEXTRIX: Latent Attribute Grid for Native Texture Generation and Beyond

Yifei Zeng·Yajie Bao·Jiachen Qian·Shuang Wu·Youtian Lin·Hao Zhu·Buyu Li·Feihu Zhang·Xun Cao·Yao Yao
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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.

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Yifei Zeng, Yajie Bao, Jiachen Qian, Shuang Wu, Youtian Lin, Hao Zhu, Buyu Li, Feihu Zhang, Xun Cao, Yao Yao. 2025-12-02. TEXTRIX: Latent Attribute Grid for Native Texture Generation and Beyond. https://arxiv.org/abs/2512.02993

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