Summary of Via: Unified Spatiotemporal Video Adaptation Framework For Global and Local Video Editing, by Jing Gu et al.
VIA: Unified Spatiotemporal Video Adaptation Framework for Global and Local Video Editing
by Jing Gu, Yuwei Fang, Ivan Skorokhodov, Peter Wonka, Xinya Du, Sergey Tulyakov, Xin Eric Wang
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 unified spatiotemporal Video Adaptation framework, dubbed VIA, is introduced to tackle the challenges of comprehensively understanding both global and local contexts in video editing. The framework, designed for long videos, consists of test-time editing adaptation for local consistency and spatiotemporal adaptation for maintaining global coherence. Compared to baseline methods, VIA produces more faithful, coherent, and precise edits, enabling consistent long video editing in minutes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video editing is crucial for digital media, but previous methods often overlook the importance of understanding both global and local contexts. The new VIA framework fixes this by introducing a unified spatiotemporal Video Adaptation approach. It uses two main techniques: test-time editing adaptation for precise local control and spatiotemporal adaptation to ensure consistency across the entire video sequence. This results in more accurate, coherent, and precise edits that can even handle long videos. |
Keywords
» Artificial intelligence » Spatiotemporal
