SceneTex: High-Quality Texture Synthesis for Indoor Scenes via Diffusion
  Priors
Dave Zhenyu Chen Haoxuan Li Hsin-Ying Lee Sergey Tulyakov Matthias Nießner
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.