Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Models
Donggoo Kang, Jangyeong Kim, Dasol Jeong, Junyoung Choi, Jeonga Wi, Hyunmin Lee, Joonho Gwon, Joonki Paik

TL;DR
VideoTex introduces a geometry-aware diffusion framework leveraging video models to generate consistent, high-quality 3D textures with temporal stability and seamless UV boundary transitions.
Contribution
The paper presents a novel VideoTex framework that combines geometry-aware conditions and UV diffusion to improve 3D texture synthesis consistency and quality.
Findings
Outperforms existing methods in texture fidelity and seam blending
Achieves high temporal stability in generated textures
Enhances occluded area generation through structure-wise diffusion
Abstract
Current texture synthesis methods, which generate textures from fixed viewpoints, suffer from inconsistencies due to the lack of global context and geometric understanding. Meanwhile, recent advancements in video generation models have demonstrated remarkable success in achieving temporally consistent videos. In this paper, we introduce VideoTex, a novel framework for seamless texture synthesis that leverages video generation models to address both spatial and temporal inconsistencies in 3D textures. Our approach incorporates geometry-aware conditions, enabling precise utilization of 3D mesh structures. Additionally, we propose a structure-wise UV diffusion strategy, which enhances the generation of occluded areas by preserving semantic information, resulting in smoother and more coherent textures. VideoTex not only achieves smoother transitions across UV boundaries but also ensures…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · 3D Shape Modeling and Analysis · Computer Graphics and Visualization Techniques
MethodsDiffusion
