Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Models
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911023192080384 |
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| author | Kang, Donggoo Kim, Jangyeong Jeong, Dasol Choi, Junyoung Wi, Jeonga Lee, Hyunmin Gwon, Joonho Paik, Joonki |
| author_facet | Kang, Donggoo Kim, Jangyeong Jeong, Dasol Choi, Junyoung Wi, Jeonga Lee, Hyunmin Gwon, Joonho Paik, Joonki |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_20946 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Models Kang, Donggoo Kim, Jangyeong Jeong, Dasol Choi, Junyoung Wi, Jeonga Lee, Hyunmin Gwon, Joonho Paik, Joonki Graphics Artificial Intelligence Computer Vision and Pattern Recognition 68T45, 68U05 I.3.7; I.4.10; I.2.10 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. |
| title | Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Models |
| topic | Graphics Artificial Intelligence Computer Vision and Pattern Recognition 68T45, 68U05 I.3.7; I.4.10; I.2.10 |
| url | https://arxiv.org/abs/2506.20946 |