Summary of Fleximo: Towards Flexible Text-to-human Motion Video Generation, by Yuhang Zhang et al.
Fleximo: Towards Flexible Text-to-Human Motion Video Generation
by Yuhang Zhang, Yuan Zhou, Zeyu Liu, Yuxuan Cai, Qiuyue Wang, Aidong Men, Huan Yang
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 introduces Fleximo, a novel framework for generating human motion videos from reference images and natural language. Unlike current methods, which rely on pose sequences extracted from reference videos, Fleximo leverages large-scale pre-trained text-to-3D motion models to generate motion videos of any desired length. To overcome challenges such as inaccurate pose detection and inconsistent scaling, the authors propose an anchor point-based rescale method and a skeleton adapter to fill in missing details. A video refinement process is also introduced to enhance video quality. The authors evaluate Fleximo using a new benchmark called MotionBench and propose a new metric, MotionScore, to assess motion accuracy. The results demonstrate that Fleximo outperforms existing text-conditioned image-to-video generation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to make videos of people moving by looking at pictures and understanding what the words say. It’s different from current ways because it doesn’t need reference videos. Instead, it uses big models that can turn text into 3D motions. The authors solve some problems with this approach by making sure the scales are right and adding missing details. They also have a special way to make the videos look better. To see how well it works, they made a new test set called MotionBench and came up with a new way to measure how good the motion is. |
