Summary of Translation-based Video-to-video Synthesis, by Pratim Saha and Chengcui Zhang
Translation-based Video-to-Video Synthesis
by Pratim Saha, Chengcui Zhang
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 In this paper, researchers explore the rapidly growing field of Translation-based Video Synthesis (TVS), which enables videos to be transformed between distinct domains while preserving temporal continuity and underlying content features. TVS has far-reaching applications in video super-resolution, colorization, segmentation, and more, extending traditional image-to-image translation to the temporal domain. However, mitigating flickering artifacts and inconsistencies between frames is a significant challenge due to the need for smooth transitions between video frames. The paper reviews recent progress in TVS, examining emerging methodologies, their strengths, limitations, applications, and potential avenues for future development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Translation-based Video Synthesis (TVS) helps videos change from one style to another while keeping the original content and timing. This is useful for things like making old movies look new again or changing a video’s color scheme. However, TVS can also introduce weird glitches that make the video look unnatural. To solve this problem, researchers have developed different methods and algorithms that aim to fix these issues. This paper looks at all the latest advancements in TVS, explaining how they work, what they’re good for, and where we might see them used in the future. |
Keywords
» Artificial intelligence » Super resolution » Translation
