Summary of Text-guided 3d Human Motion Generation with Keyframe-based Parallel Skip Transformer, by Zichen Geng et al.
Text-guided 3D Human Motion Generation with Keyframe-based Parallel Skip Transformer
by Zichen Geng, Caren Han, Zeeshan Hayder, Jian Liu, Mubarak Shah, Ajmal Mian
First submitted to arxiv on: 24 May 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 This paper proposes a novel approach to text-driven human motion generation, called KeyMotion, which addresses the limitations of existing algorithms by focusing on key poses. The authors use a Variational Autoencoder (VAE) to reduce dimensionality and accelerate the process, followed by a parallel skip transformer for cross-modal attention between keyframes and text condition. A text-guided transformer is then used to complete the motion sequence while preserving physical constraints. KeyMotion achieves state-of-the-art results on the HumanML3D dataset and competitive performance on the KIT dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to create realistic human movements based on written descriptions. The authors created a new way to generate human motions that focuses on important poses, rather than trying to create the entire motion sequence at once. They use special algorithms like Variational Autoencoders and Transformers to make the process faster and more accurate. This method works well for creating realistic human movements that match what someone would write about. |
Keywords
» Artificial intelligence » Attention » Transformer » Variational autoencoder
