We propose SceneTex, a novel method for effectively generating high-quality and style-consistent textures for indoor scenes using depth-to-image diffusion priors. Unlike previous methods that either iteratively warp 2D views onto a mesh surface or distillate diffusion latent features without accurate geometric and style cues, SceneTex formulates the texture synthesis task as an optimization problem in the RGB space where style and geometry consistency are properly reflected. At its core, SceneTex proposes a multiresolution texture field to implicitly encode the mesh appearance. We optimize the target texture via a score-distillation-based objective function in respective RGB renderings. To further secure the style consistency across views, we introduce a cross-attention decoder to predict the RGB values by cross-attending to the pre-sampled reference locations in each instance. SceneTex enables various and accurate texture synthesis for 3D-FRONT scenes, demonstrating significant improvements in visual quality and prompt fidelity over the prior texture generation methods.

SceneTex是一种使用基于深度到图像扩散先验的方法，在室内场景中有效地生成高质量且样式一致的纹理。它通过在RGB空间中将纹理合成任务形式化为一个优化问题，正确反映了样式和几何一致性。SceneTex通过多分辨率纹理场隐式编码网格外观，并通过基于分数蒸馏的目标函数在各自的RGB渲染中优化目标纹理。为了进一步确保视图之间的样式一致性，我们引入了交叉注意力解码器来通过交叉关注每个实例中的预采样参考位置来预测RGB值。SceneTex在3D-FRONT场景中实现了各种准确的纹理合成，相比之前的纹理生成方法在视觉质量和快速保真度上有显着改进。

SceneTex: 室内场景的高质量纹理合成基于扩散先验