Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916583696236544 |
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| author | Xiang, Xiaoyu Gorelik, Liat Sless Fan, Yuchen Armstrong, Omri Iandola, Forrest Li, Yilei Lifshitz, Ita Ranjan, Rakesh |
| author_facet | Xiang, Xiaoyu Gorelik, Liat Sless Fan, Yuchen Armstrong, Omri Iandola, Forrest Li, Yilei Lifshitz, Ita Ranjan, Rakesh |
| contents | We present Make-A-Texture, a new framework that efficiently synthesizes high-resolution texture maps from textual prompts for given 3D geometries. Our approach progressively generates textures that are consistent across multiple viewpoints with a depth-aware inpainting diffusion model, in an optimized sequence of viewpoints determined by an automatic view selection algorithm.
A significant feature of our method is its remarkable efficiency, achieving a full texture generation within an end-to-end runtime of just 3.07 seconds on a single NVIDIA H100 GPU, significantly outperforming existing methods. Such an acceleration is achieved by optimizations in the diffusion model and a specialized backprojection method. Moreover, our method reduces the artifacts in the backprojection phase, by selectively masking out non-frontal faces, and internal faces of open-surfaced objects.
Experimental results demonstrate that Make-A-Texture matches or exceeds the quality of other state-of-the-art methods. Our work significantly improves the applicability and practicality of texture generation models for real-world 3D content creation, including interactive creation and text-guided texture editing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07766 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds Xiang, Xiaoyu Gorelik, Liat Sless Fan, Yuchen Armstrong, Omri Iandola, Forrest Li, Yilei Lifshitz, Ita Ranjan, Rakesh Computer Vision and Pattern Recognition Graphics We present Make-A-Texture, a new framework that efficiently synthesizes high-resolution texture maps from textual prompts for given 3D geometries. Our approach progressively generates textures that are consistent across multiple viewpoints with a depth-aware inpainting diffusion model, in an optimized sequence of viewpoints determined by an automatic view selection algorithm. A significant feature of our method is its remarkable efficiency, achieving a full texture generation within an end-to-end runtime of just 3.07 seconds on a single NVIDIA H100 GPU, significantly outperforming existing methods. Such an acceleration is achieved by optimizations in the diffusion model and a specialized backprojection method. Moreover, our method reduces the artifacts in the backprojection phase, by selectively masking out non-frontal faces, and internal faces of open-surfaced objects. Experimental results demonstrate that Make-A-Texture matches or exceeds the quality of other state-of-the-art methods. Our work significantly improves the applicability and practicality of texture generation models for real-world 3D content creation, including interactive creation and text-guided texture editing. |
| title | Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2412.07766 |