Summary of Human Motion Synthesis_ a Diffusion Approach For Motion Stitching and In-betweening, by Michael Adewole et al.
Human Motion Synthesis_ A Diffusion Approach for Motion Stitching and In-Betweening
by Michael Adewole, Oluwaseyi Giwa, Favour Nerrise, Martins Osifeko, Ajibola Oyedeji
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Human-Computer Interaction (cs.HC); 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 This paper tackles human motion generation by proposing a diffusion model with a transformer-based denoiser for motion stitching and in-betweening. The current methods either require manual efforts or are incapable of handling longer sequences. Our method demonstrated strong performance in generating in-betweening sequences, transforming a variable number of input poses into smooth and realistic motion sequences consisting of 75 frames at 15 fps, resulting in a total duration of 5 seconds. We evaluated our method using quantitative metrics such as Frechet Inception Distance (FID), Diversity, and Multimodality, along with visual assessments of the generated outputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine creating realistic human movements without needing to manually edit them. This paper makes that possible by developing a new way to generate smooth and realistic human motion sequences. The current methods have limitations, but our approach can handle longer sequences and doesn’t require manual editing. We tested our method and found it generates high-quality motion sequences that look like real human movement. |
Keywords
» Artificial intelligence » Diffusion model » Transformer
