Summary of Neural Gaffer: Relighting Any Object Via Diffusion, by Haian Jin et al.
Neural Gaffer: Relighting Any Object via Diffusion
by Haian Jin, Yuan Li, Fujun Luan, Yuanbo Xiangli, Sai Bi, Kai Zhang, Zexiang Xu, Jin Sun, Noah Snavely
First submitted to arxiv on: 11 Jun 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 introduces Neural Gaffer, an end-to-end 2D relighting diffusion model that can synthesize high-quality relit images under novel environmental lighting conditions. The model takes a single image of any object and conditionally generates the relit image by fine-tuning a pre-trained diffusion model on a synthetic relighting dataset. Unlike existing methods, Neural Gaffer does not require explicit scene decomposition or special capture conditions. The paper evaluates the model’s performance on both synthetic and in-the-wild Internet imagery, demonstrating its generalization and accuracy advantages. Furthermore, the model can be combined with other generative methods for tasks like text-based relighting, object insertion, and serving as a strong prior for 3D relighting tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to make pictures look good in different lighting conditions. The method uses computer vision to understand how lighting works and can take an old picture and make it look like it was taken with a different light source. This is helpful because many existing methods only work for specific types of images or require special equipment. The new approach doesn’t need that extra information and can even be used to insert objects into pictures or change the text in them. The researchers tested their method on real-world images and found it worked well, making it a useful tool for various applications. |
Keywords
* Artificial intelligence * Diffusion model * Fine tuning * Generalization
