Summary of Text-guided Controllable Mesh Refinement For Interactive 3d Modeling, by Yun-chun Chen et al.
Text-guided Controllable Mesh Refinement for Interactive 3D Modeling
by Yun-Chun Chen, Selena Ling, Zhiqin Chen, Vladimir G. Kim, Matheus Gadelha, Alec Jacobson
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Graphics (cs.GR); 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 This paper presents a novel approach for enhancing 3D meshes with geometric details guided by text prompts. The technique is composed of three stages: generating a single-view RGB image conditioned on the input coarse geometry and prompt, using a multi-view normal generation architecture to produce six views of normal images, and optimizing the mesh to generate a fine, detailed output. This method produces results within seconds and offers user control over the coarse structure, pose, and details. The paper’s contribution lies in its ability to balance geometric detail and realism with text-based guidance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to add details to 3D models using words. It has three parts: first, it makes a picture of what the model will look like from one angle, based on the initial shape and some text. This helps users see what the final result will be. Next, it generates six different views of the model’s surface to make sure all angles are consistent and detailed. Finally, it refines the original model using these views to produce a high-quality output. The method is fast and allows for user control over the details. |
Keywords
» Artificial intelligence » Prompt
