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arXiv · 2410.06985

Jointly Generating Multi-view Consistent PBR Textures using Collaborative Control

Shimon Vainer·Konstantin Kutsy·Dante De Nigris·Ciara Rowles·Slava Elizarov·Simon Donné
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Abstract

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.

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Shimon Vainer, Konstantin Kutsy, Dante De Nigris, Ciara Rowles, Slava Elizarov, Simon Donné. 2024-10-09. Jointly Generating Multi-view Consistent PBR Textures using Collaborative Control. https://arxiv.org/abs/2410.06985

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