Infinite Texture: Text-guided High Resolution Diffusion Texture Synthesis

• URL: http://arxiv.org/abs/2405.08210v1
• Date: Mon, 13 May 2024 21:53:09 GMT
• Title: Infinite Texture: Text-guided High Resolution Diffusion Texture Synthesis
• Authors: Yifan Wang, Aleksander Holynski, Brian L. Curless, Steven M. Seitz,
• Abstract summary: 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. 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.
• Score: 61.189479577198846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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