Summary of Spatialdreamer: Self-supervised Stereo Video Synthesis From Monocular Input, by Zhen Lv et al.
SpatialDreamer: Self-supervised Stereo Video Synthesis from Monocular Input
by Zhen Lv, Yangqi Long, Congzhentao Huang, Cao Li, Chengfei Lv, Hao Ren, Dian Zheng
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel self-supervised stereo video synthesis paradigm is introduced in this paper, addressing the challenges of generating high-quality paired stereo videos from a monocular input. The approach, dubbed SpatialDreamer, utilizes a video diffusion model to meet these demands. A key component is the Depth-based Video Generation (DVG) module, which employs a forward-backward rendering mechanism to generate paired videos with geometric and temporal priors. The RefinerNet framework, combined with a self-supervised synthetic framework, enables efficient training. Additionally, a consistency control module ensures geometric and temporal consistency through a stereo deviation strength metric and Temporal Interaction Learning (TIL) module. Experimental results demonstrate the proposed method’s superior performance compared to benchmark methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re watching a movie on your phone, but it looks like it was recorded by multiple cameras. That’s what this paper is all about – making videos look like they were filmed with multiple cameras when really only one camera was used. The authors of the paper came up with a new way to do this using something called video diffusion models. They also developed a special tool that helps make sure the resulting video looks good and consistent. To test their idea, they compared it to other methods and found that theirs worked best. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Self supervised
