Geometry Aware Texturing
Evgeniia Cheskidova, Aleksandr Arganaidi, Daniel-Ionut Rancea, Olaf Haag · 2023
In this work, we propose a novel approach to texture generation, making use of recent advancements in Latent Diffusion models, [Rombach et al. 2022] unlocked by [Zhang and Agrawala 2023], introducing control inputs to generation pipelines via ControlNet. We find that a special condition, where the mesh is encoded into UV space, can serve as a control input, producing textures that are geometrically and visually coherent, and of high quality. Using this approach, we are able to generate a unique look guided by text for existing meshes in a matter of seconds.