用三维隐空间网格直接生成纹理，避免多视角融合缺陷。
TEXTRIX: Latent Attribute Grid for Native Texture Generation and Beyond
- 构建三维隐属性网格，直接在体素空间上着色
- 在真实3D部分分割任务中达到顶尖精度，边界更清晰
- 同一架构可同时完成高质量纹理生成与分割
现有3D纹理生成方法依赖多视角融合，常因视角间不一致和复杂表面覆盖不全，限制生成内容的保真度与完整性。为此，我们提出TEXTRIX，一种原生3D属性生成框架，用于高保真纹理合成及下游任务如精确3D部件分割。该方法构建一个三维隐属性网格，并利用带有稀疏注意力的扩散变换器，实现对3D模型在体素空间中的直接着色，从根本上规避了多视角融合的局限。基于此原生表示，框架可自然扩展至高精度3D分割任务，通过在同一架构上训练预测网格上的语义属性。大量实验证明，该方法在两项任务上均达到领先性能，生成无缝、高保真的纹理，并实现边界精准的3D部件分割。
原文摘要 · Abstract (English)
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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