Summary of Genesistex2: Stable, Consistent and High-quality Text-to-texture Generation, by Jiawei Lu et al.
GenesisTex2: Stable, Consistent and High-Quality Text-to-Texture Generation
by Jiawei Lu, Yingpeng Zhang, Zengjun Zhao, He Wang, Kun Zhou, Tianjia Shao
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a novel text-to-texture synthesis framework that leverages pre-trained diffusion models to generate textures for 3D geometries. The framework addresses the challenges of previous methods by introducing a local attention reweighing mechanism and a latent space merge pipeline. These mechanisms enable the model to concentrate on spatial-correlated patches across different views, preserving local details while maintaining cross-view consistency. The method outperforms existing state-of-the-art techniques in terms of texture consistency and visual quality, with results delivered faster than distillation-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to use big AI models to generate textures for 3D objects. This is important because it’s hard to make textures that look good from different angles. The authors introduce two new ideas: local attention reweighing and latent space merge. These help the model pay attention to details in different parts of the object while keeping the overall texture consistent. The method is better than others at making consistent textures, looks good, and works fast. |
Keywords
» Artificial intelligence » Attention » Diffusion » Distillation » Latent space
