We present GOATex, a diffusion-based method for 3D mesh texturing that generates high-quality textures for both exterior and interior surfaces. While existing methods perform well on visible regions, they inherently lack mechanisms to handle occluded interiors, resulting in incomplete textures and visible seams. To address this, we introduce an occlusion-aware texturing framework based on the concept of hit levels, which quantify the relative depth of mesh faces via multi-view ray casting. This allows us to partition mesh faces into ordered visibility layers, from outermost to innermost. We then apply a two-stage visibility control strategy that progressively reveals interior regions with structural coherence, followed by texturing each layer using a pretrained diffusion model. To seamlessly merge textures obtained across layers, we propose a soft UV-space blending technique that weighs each texture's contribution based on view-dependent visibility confidence. Empirical results demonstrate that GOATex consistently outperforms existing methods, producing seamless, high-fidelity textures across both visible and occluded surfaces. Unlike prior works, GOATex operates entirely without costly fine-tuning of a pretrained diffusion model and allows separate prompting for exterior and interior mesh regions, enabling fine-grained control over layered appearances. For more qualitative results, please visit our project page: https://goatex3d.github.io/.
翻译:本文提出GOATex,一种基于扩散模型的3D网格纹理生成方法,能够为网格的外表面和内表面同时生成高质量纹理。现有方法在可见区域表现良好,但本质上缺乏处理被遮挡内部区域的机制,导致生成纹理不完整且存在可见接缝。为解决这一问题,我们引入了一种基于命中层级概念的遮挡感知纹理生成框架,该概念通过多视角光线投射量化网格面片的相对深度。这使得我们能够将网格面片划分为从最外层到最内层的有序可见性层级。随后,我们采用两阶段可见性控制策略:首先渐进式地以结构一致性揭示内部区域,然后使用预训练的扩散模型为每个层级生成纹理。为实现跨层级纹理的无缝融合,我们提出了一种基于UV空间的软融合技术,该技术根据视角相关的可见性置信度加权各纹理的贡献。实验结果表明,GOATex在可见表面与被遮挡表面上均能持续超越现有方法,生成无缝的高保真纹理。与先前工作不同,GOATex完全无需对预训练扩散模型进行耗时的微调,并允许对外部与内部网格区域分别进行提示,从而实现对分层外观的细粒度控制。更多定性结果请访问项目页面:https://goatex3d.github.io/。