Summary of Simvs: Simulating World Inconsistencies For Robust View Synthesis, by Alex Trevithick et al.
SimVS: Simulating World Inconsistencies for Robust View Synthesis
by Alex Trevithick, Roni Paiss, Philipp Henzler, Dor Verbin, Rundi Wu, Hadi Alzayer, Ruiqi Gao, Ben Poole, Jonathan T. Barron, Aleksander Holynski, Ravi Ramamoorthi, Pratul P. Srinivasan
First submitted to arxiv on: 10 Dec 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 This paper addresses the limitations of novel-view synthesis techniques in handling inconsistent scenes. Existing approaches struggle to model variations in illumination, scene motion, and other unintended effects that can occur during casual capture. To overcome this challenge, the authors propose using generative video models to simulate these inconsistencies. By combining this simulation with existing multi-view datasets, they create synthetic data for training a multi-view harmonization network. This approach enables highly accurate static 3D reconstructions in the presence of various scene variations. The paper demonstrates that world-simulation strategy outperforms traditional augmentation methods in handling real-world scene inconsistencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a picture or video, but the lighting changes, people move around, and other things happen that make it hard to get a clear view. This is what happens when we try to use special computer techniques to create 3D images from regular photos or videos. The problem is that these techniques don’t account for all the unexpected things that can happen during capture. To solve this issue, researchers have developed a new way of simulating these unexpected events and using them to train computers to make better 3D reconstructions. This approach leads to much more accurate results than previous methods. |
Keywords
» Artificial intelligence » Synthetic data
