Summary of Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis, by Basile Van Hoorick et al.
Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis
by Basile Van Hoorick, Rundi Wu, Ege Ozguroglu, Kyle Sargent, Ruoshi Liu, Pavel Tokmakov, Achal Dave, Changxi Zheng, Carl Vondrick
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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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 A major challenge in computer vision is reconstructing complex dynamic scenes from a single viewpoint. Existing methods require multiple camera viewpoints, limiting their use in the wild and for embodied AI applications. This paper introduces , a pipeline that generates synchronous videos from different perspectives using large-scale diffusion priors. Unlike existing methods, does not require depth or 3D geometry, instead performing end-to-end video-to-video translation. Despite training on synthetic data only, the model shows promising zero-shot generalization results in robotics, object permanence, and driving environments. This framework has potential applications in rich dynamic scene understanding, perception for robotics, and interactive 3D video viewing experiences for virtual reality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a photo or recording a video from one angle, but then being able to see what it would look like if you were standing somewhere else. This is called “dynamic view synthesis” and it’s hard to do accurately, especially when there are many moving objects in the scene. Researchers have developed a new method that can create these kinds of videos without needing multiple cameras or depth information. They tested their method on different types of scenes and found that it worked well even when they didn’t train it specifically for those scenarios. This technology has the potential to be used in robots, virtual reality, and other areas where we want to understand complex dynamic scenes. |
Keywords
» Artificial intelligence » Diffusion » Generalization » Scene understanding » Synthetic data » Translation » Zero shot
