Summary of Photorealistic Object Insertion with Diffusion-guided Inverse Rendering, by Ruofan Liang et al.
Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering
by Ruofan Liang, Zan Gojcic, Merlin Nimier-David, David Acuna, Nandita Vijaykumar, Sanja Fidler, Zian Wang
First submitted to arxiv on: 19 Aug 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 The proposed method combines personalized large diffusion models with inverse rendering to accurately insert virtual objects into real-world scenes. By leveraging the guidance of a large-scale diffusion model, the approach recovers scene lighting and tone-mapping parameters, allowing for photorealistic composition of virtual objects in single frames or videos. This physically based pipeline also enables automatic materials and tone-mapping refinement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to put digital objects into real-life pictures. They used big computer models to help figure out how light behaves in different scenes. By combining these models with special rendering techniques, they created a system that can seamlessly insert virtual objects into real-world environments while preserving details and lighting effects. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model
