Summary of Videorepair: Improving Text-to-video Generation Via Misalignment Evaluation and Localized Refinement, by Daeun Lee et al.
VideoRepair: Improving Text-to-Video Generation via Misalignment Evaluation and Localized Refinement
by Daeun Lee, Jaehong Yoon, Jaemin Cho, Mohit Bansal
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This paper introduces VideoRepair, a novel framework that improves the generation capabilities of text-to-video (T2V) diffusion models by identifying and refining misalignments between video and text prompts. The framework consists of two stages: first, it detects misalignments using fine-grained evaluation questions and localized prompts are constructed to refine misaligned regions. Second, a region-preserving segmentation module is used to repair misaligned regions while preserving correctly generated areas. VideoRepair outperforms recent baselines on two popular video generation benchmarks across various text-video alignment metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VideoRepair is a new way to make videos that match what people are saying. It helps fix mistakes when the words and pictures don’t line up. The method has two steps: first, it finds where the mismatch is by asking questions about the video. Then, it uses special techniques to repair those areas while keeping the rest of the video correct. This makes the videos look better and match what people are saying more accurately. |
Keywords
» Artificial intelligence » Alignment » Diffusion
