Summary of Towards Real-world Video Face Restoration: a New Benchmark, by Ziyan Chen et al.
Towards Real-world Video Face Restoration: A New Benchmark
by Ziyan Chen, Jingwen He, Xinqi Lin, Yu Qiao, Chao Dong
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM); 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 The proposed paper explores the challenges of real-world video face restoration (VFR), a more complex task than blind face restoration (BFR) due to varying face motions and orientations. The authors introduce new datasets named FOS, featuring diverse degradations and complex scenarios, to evaluate current BFR and video super resolution (VSR) methods comprehensively. By benchmarking state-of-the-art approaches and assessing image quality using IQA and FIQA metrics, the study identifies potential and limitations in VFR tasks. The findings provide insights into successes and failures of current methods, posing challenges for future advances in VFR research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Real-world video face restoration is a big challenge because it’s hard to make faces look good when they’re moving or changing orientation. Right now, most face restoration methods are only tested on simple images, but this doesn’t prepare us for real-life videos. The authors created new datasets with lots of different types of face problems to test current methods and see what works best. They also compared how well different quality metrics work in judging restored faces. By doing this research, we can learn what’s good and bad about current approaches and make better ones for the future. |
Keywords
» Artificial intelligence » Super resolution
