RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models

• URL: http://arxiv.org/abs/2409.19989v1
• Date: Mon, 30 Sep 2024 06:29:50 GMT
• Title: RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models
• Authors: Jangyeong Kim, Donggoo Kang, Junyoung Choi, Jeonga Wi, Junho Gwon, Jiun Bae, Dumim Yoon, Junghyun Han,
• Abstract summary: 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.
• Score: 3.714901836138171
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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