Jointly Generating Multi-view Consistent PBR Textures using Collaborative Control
<|reference_start|>Jointly Generating Multi-view Consistent PBR Textures using Collaborative Control: Multi-view consistency remains a challenge for image diffusion models. Even within the Text-to-Texture problem, where perfect geometric correspondences are known a priori, many methods fail to yield aligned predictions across views, necessitating non-trivial fusion methods to incorporate the results onto the original mesh. We explore this issue for a Collaborative Control workflow specifically in PBR Text-to-Texture. Collaborative Control directly models PBR image probability distributions, including normal bump maps; to our knowledge, the only diffusion model to directly output full PBR stacks. We discuss the design decisions involved in making this model multi-view consistent, and demonstrate the effectiveness of our approach in ablation studies, as well as practical applications.<|reference_end|>
@article{vainer2024jointly,
    title={Jointly Generating Multi-view Consistent PBR Textures using
  Collaborative Control},
    author={Shimon Vainer, Konstantin Kutsy, Dante De Nigris, Ciara Rowles, Slava
  Elizarov, Simon Donn'e},
    journal={arXiv preprint arXiv:2410.06985},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06985},
    primaryClass={cs.CV cs.GR}
}