Summary of Tri-modal Motion Retrieval by Learning a Joint Embedding Space, By Kangning Yin et al.
Tri-Modal Motion Retrieval by Learning a Joint Embedding Space
by Kangning Yin, Shihao Zou, Yuxuan Ge, Zheng Tian
First submitted to arxiv on: 1 Mar 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 framework called LAVIMO for three-modality learning, which integrates human-centric videos as an additional modality to bridge the gap between text and motion. The approach leverages a specially designed attention mechanism to foster enhanced alignment among text, video, and motion modalities. The authors demonstrate the effectiveness of LAVIMO on two datasets, HumanML3D and KIT-ML, achieving state-of-the-art performance in various cross-modal retrieval tasks, including text-to-motion, motion-to-text, video-to-motion, and motion-to-video. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to learn from three types of information: words, videos, and body movements. The goal is to improve how computers can understand relationships between these different types of data. The researchers create a special method called LAVIMO that combines all three types of information together, allowing for better connections and predictions. They test this method on two large datasets and show that it performs better than other methods in various tasks, such as matching text with body movements or videos with motion. |
Keywords
» Artificial intelligence » Alignment » Attention
