Summary of Recapture: Generative Video Camera Controls For User-provided Videos Using Masked Video Fine-tuning, by David Junhao Zhang et al.
ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning
by David Junhao Zhang, Roni Paiss, Shiran Zada, Nikhil Karnad, David E. Jacobs, Yael Pritch, Inbar Mosseri, Mike Zheng Shou, Neal Wadhwa, Nataniel Ruiz
First submitted to arxiv on: 7 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 paper presents ReCapture, a method for generating new videos with novel camera trajectories from a single user-provided video. By combining multiview diffusion models or depth-based point cloud rendering with a masked video fine-tuning technique, the authors enable the re-generation of a reference video from vastly different angles and with cinematic camera motion. This approach also allows for the plausible hallucination of parts of the scene that were not observable in the original video. The method has implications for applications such as video editing, augmented reality, and virtual cinematography. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make videos look different from the one you start with. It’s like taking a video and then making it look like it was shot from a different angle or in a different style. The authors use special computer models to do this and they can even add parts that weren’t in the original video. This could be useful for things like editing videos, creating virtual reality experiences, or even making movies. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Hallucination
