Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds

Xiaoyu Xiang, Liat Sless Gorelik, Yuchen Fan, Omri Armstrong, Forrest Iandola +3 more
12/10/2024
cs.CVcs.GR

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Abstract

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.

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Cite this paper

@article{xiang2024makeatexture,
  title  = {Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds},
  author = {Xiaoyu Xiang and Liat Sless Gorelik and Yuchen Fan and Omri Armstrong and Forrest Iandola and Yilei Li and Ita Lifshitz and Rakesh Ranjan},
  year   = {2024},
  eprint = {2412.07766},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.07766v2}
}

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