GenesisTex: Adapting Image Denoising Diffusion to Texture Space
<|reference_start|>GenesisTex: Adapting Image Denoising Diffusion to Texture Space: 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.<|reference_end|>
@article{gao2024genesistex:,
    title={GenesisTex: Adapting Image Denoising Diffusion to Texture Space},
    author={Chenjian Gao, Boyan Jiang, Xinghui Li, Yingpeng Zhang, Qian Yu},
    journal={arXiv preprint arXiv:2403.17782},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17782},
    primaryClass={cs.CV cs.GR}
}