We present GenesisTex, a novel method for synthesizing textures for 3D
geometries from text descriptions. GenesisTex adapts the pretrained image
diffusion model to texture space by texture space sampling. Specifically, we
maintain a latent texture map for each viewpoint, which is updated with
predicted noise on the rendering of the corresponding viewpoint. The sampled
latent texture maps are then decoded into a final texture map. During the
sampling process, we focus on both global and local consistency across multiple
viewpoints: global consistency is achieved through the integration of style
consistency mechanisms within the noise prediction network, and low-level
consistency is achieved by dynamically aligning latent textures. Finally, we
apply reference-based inpainting and img2img on denser views for texture
refinement. Our approach overcomes the limitations of slow optimization in
distillation-based methods and instability in inpainting-based methods.
Experiments on meshes from various sources demonstrate that our method
surpasses the baseline methods quantitatively and qualitatively.

GenesisTex 是一种从文本描述中合成 3D 几何纹理的新方法，通过纹理空间采样来适应预训练图像扩散模型，通过全局一致性和局部一致性实现多个视点之间的纹理生成，并通过基于参考的修复和图像转换进一步优化纹理。实验证明，我们的方法在数量和质量上优于基准方法。