RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models
This addresses texture synthesis for 3D graphics and design, offering incremental improvements in consistency and seam reduction.
The paper tackles the problem of view inconsistencies, seams, and misalignment in text-to-texture generation by proposing a robust method that leverages diffusion models and novel techniques, resulting in outperforming existing state-of-the-art methods.
Text-to-texture generation has recently attracted increasing attention, but existing methods often suffer from the problems of view inconsistencies, apparent seams, and misalignment between textures and the underlying mesh. In this paper, we propose a robust text-to-texture method for generating consistent and seamless textures that are well aligned with the mesh. Our method leverages state-of-the-art 2D diffusion models, including SDXL and multiple ControlNets, to capture structural features and intricate details in the generated textures. The method also employs a symmetrical view synthesis strategy combined with regional prompts for enhancing view consistency. Additionally, it introduces novel texture blending and soft-inpainting techniques, which significantly reduce the seam regions. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods.