SceneTex: High-Quality Texture Synthesis for Indoor Scenes via Diffusion Priors
This addresses texture generation for indoor scenes, which is a domain-specific problem with incremental improvements over existing approaches.
The paper tackles the problem of generating high-quality, style-consistent textures for indoor 3D scenes by formulating texture synthesis as an optimization problem in RGB space using diffusion priors, resulting in significant improvements in visual quality and prompt fidelity over prior methods.
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.