使用几何感知扩散和时间视频模型的一致零样本3D纹理合成
Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Models
AI总结：
提出VideoTex框架，利用视频生成模型和几何感知条件解决3D纹理合成中的时空不一致问题，通过结构级UV扩散策略提升遮挡区域生成质量，实现高保真且时空稳定的纹理合成。
摘要：
当前的纹理合成方法从固定视角生成纹理，由于缺乏全局上下文和几何理解，存在不一致性问题。同时，视频生成模型的最新进展在实现时间一致的视频方面取得了显著成功。在本文中，我们提出了VideoTex，一个无缝纹理合成的新框架，利用视频生成模型来解决3D纹理中的空间和时间不一致性问题。我们的方法结合了几何感知条件，能够精确利用3D网格结构。此外，我们提出了一种结构级UV扩散策略，通过保留语义信息来增强遮挡区域的生成，从而产生更平滑、更连贯的纹理。VideoTex不仅在UV边界上实现了更平滑的过渡，还确保了跨视频帧的高质量、时间稳定的纹理。大量实验表明，VideoTex在纹理保真度、接缝融合和稳定性方面优于现有方法，为同时要求视觉质量和时间连贯性的动态实时应用铺平了道路。
英文摘要：
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 high-quality, temporally stable textures across video frames. Extensive experiments demonstrate that VideoTex outperforms existing methods in texture fidelity, seam blending, and stability, paving the way for dynamic real-time applications that demand both visual quality and temporal coherence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
