Summary of Learning Online Scale Transformation For Talking Head Video Generation, by Fa-ting Hong et al.
Learning Online Scale Transformation for Talking Head Video Generation
by Fa-Ting Hong, Dan Xu
First submitted to arxiv on: 13 Jul 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 The paper proposes an innovative approach to one-shot talking head video generation, where a source image and a driving video are used to create a synthetic video of the source person’s facial movements mimicking those of the driving video. The existing methods for face reenactment struggle with differences in scale between the source and driving images, leading to suboptimal outcomes. This paper aims to address this challenge by developing an effective method for aligning frames from the driving video with the source image. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary One-shot talking head video generation is a technology that allows us to create realistic videos of people’s faces mimicking those in other videos or images. The current methods for doing this have some problems, like not being able to match up the size and movement of the face correctly. This can make the final result look unnatural. Researchers are working on new ways to solve this problem. |
Keywords
» Artificial intelligence » One shot
