Summary of Texgen: a Generative Diffusion Model For Mesh Textures, by Xin Yu et al.
TEXGen: a Generative Diffusion Model for Mesh Textures
by Xin Yu, Ze Yuan, Yuan-Chen Guo, Ying-Tian Liu, JianHui Liu, Yangguang Li, Yan-Pei Cao, Ding Liang, Xiaojuan Qi
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to learning texture maps in the UV space, departing from conventional methods that rely on pre-trained 2D diffusion models. The authors propose a scalable network architecture that interleaves convolutions on UV maps with attention layers on point clouds, allowing for efficient learning in high-resolution UV spaces. They train a large diffusion model capable of generating high-resolution texture maps in a feed-forward manner, guided by text prompts and single-view images. The model can be used for various extended applications, including text-guided texture inpainting, sparse-view texture completion, and text-driven texture synthesis. Notably, the paper focuses on learning directly in the UV texture space itself, rather than relying on pre-trained models. The authors’ approach has significant implications for realistic 3D asset rendering, as high-quality texture maps are essential for achieving photorealism. By leveraging this architectural design, the model can generate UV texture maps with ease, making it a valuable tool for researchers and practitioners in computer vision and graphics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating really realistic textures for 3D models. Right now, most methods use pre-trained models that aren’t specifically designed to work with 3D data. The authors of this paper wanted to change that by learning directly from the texture space itself. They created a special kind of model that can generate high-resolution texture maps in a single pass, using text prompts and single-view images as guidance. This model can do all sorts of cool things, like filling in missing texture details or generating textures based on written descriptions. The authors hope that their approach will be useful for researchers and developers who want to create more realistic 3D models. |
Keywords
» Artificial intelligence » Attention » Diffusion » Diffusion model
