Summary of Contexture: Consistent Multiview Images to Texture, by Jaehoon Ahn et al.
ConTEXTure: Consistent Multiview Images to Texture
by Jaehoon Ahn, Sumin Cho, Harim Jung, Kibeom Hong, Seonghoon Ban, Moon-Ryul Jung
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The authors introduce ConTEXTure, a generative network designed to create texture maps or atlases for 3D meshes using images from multiple viewpoints. The process begins with generating a front-view image from a text prompt, such as “Napoleon, front view”, describing the 3D mesh. Additional images are derived from this front-view image and camera poses relative to it. ConTEXTure builds upon the TEXTure network, which uses text prompts for six viewpoints (e.g., “Napoleon, front view”, “Napoleon, left view”, etc.). The authors address an issue with TEXTure by employing Zero123++, which generates multiple view-consistent images simultaneously, conditioned on the initial front-view image and depth maps of the mesh. ConTEXTure learns texture atlases from all viewpoint images concurrently, unlike previous methods that do so sequentially. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ConTEXTure is a new way to create texture maps for 3D objects using pictures taken from different angles. It starts by creating a picture of the front side of an object and then uses this picture to make pictures of other sides. This helps fix a problem with previous methods that made some parts of the object look strange when viewed from certain angles. |
Keywords
» Artificial intelligence » Prompt
