Infinite Texture: Text-guided High Resolution Diffusion Texture Synthesis
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909201524064256 |
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| author | Wang, Yifan Holynski, Aleksander Curless, Brian L. Seitz, Steven M. |
| author_facet | Wang, Yifan Holynski, Aleksander Curless, Brian L. Seitz, Steven M. |
| contents | We present Infinite Texture, a method for generating arbitrarily large texture images from a text prompt. Our approach fine-tunes a diffusion model on a single texture, and learns to embed that statistical distribution in the output domain of the model. We seed this fine-tuning process with a sample texture patch, which can be optionally generated from a text-to-image model like DALL-E 2. At generation time, our fine-tuned diffusion model is used through a score aggregation strategy to generate output texture images of arbitrary resolution on a single GPU. We compare synthesized textures from our method to existing work in patch-based and deep learning texture synthesis methods. We also showcase two applications of our generated textures in 3D rendering and texture transfer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08210 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Infinite Texture: Text-guided High Resolution Diffusion Texture Synthesis Wang, Yifan Holynski, Aleksander Curless, Brian L. Seitz, Steven M. Computer Vision and Pattern Recognition We present Infinite Texture, a method for generating arbitrarily large texture images from a text prompt. Our approach fine-tunes a diffusion model on a single texture, and learns to embed that statistical distribution in the output domain of the model. We seed this fine-tuning process with a sample texture patch, which can be optionally generated from a text-to-image model like DALL-E 2. At generation time, our fine-tuned diffusion model is used through a score aggregation strategy to generate output texture images of arbitrary resolution on a single GPU. We compare synthesized textures from our method to existing work in patch-based and deep learning texture synthesis methods. We also showcase two applications of our generated textures in 3D rendering and texture transfer. |
| title | Infinite Texture: Text-guided High Resolution Diffusion Texture Synthesis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.08210 |