Summary of Boosting 3d Object Generation Through Pbr Materials, by Yitong Wang et al.
Boosting 3D Object Generation through PBR Materials
by Yitong Wang, Xudong Xu, Li Ma, Haoran Wang, Bo Dai
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed approach in this paper aims to improve the quality of automatically generated 3D objects by incorporating Physics-Based Rendering (PBR) materials. This is achieved by analyzing PBR material components, such as albedo, roughness, metalness, and bump maps, and leveraging Stable Diffusion models fine-tuned on synthetic data. The method involves extracting albedo UV and bump UV values for generated objects using novel model usages, while roughness and metalness maps are obtained through a semi-automatic process. This approach is demonstrated to be beneficial for various state-of-the-art generation methods, resulting in higher quality and realism of generated 3D objects with natural relighting effects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new method to create realistic 3D objects from single images. They use special computer models to understand how materials look and behave in the real world. This helps generate more accurate textures, colors, and shapes for 3D objects. The results show that these improved methods can be used with other state-of-the-art techniques to make even better 3D objects. |
Keywords
» Artificial intelligence » Diffusion » Synthetic data
