Summary of Multi-agent Long-term 3d Human Pose Forecasting Via Interaction-aware Trajectory Conditioning, by Jaewoo Jeong et al.
Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioning
by Jaewoo Jeong, Daehee Park, Kuk-Jin Yoon
First submitted to arxiv on: 8 Apr 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 A novel human pose forecasting model, dubbed Trajectory2Pose, has been proposed to tackle the challenges of modeling multi-modal human motion and intricate interactions among agents. This medium-difficulty summary will delve into the specifics of this approach, which utilizes a coarse-to-fine prediction strategy to forecast both global trajectories and local poses. The model introduces a graph-based agent-wise interaction module that allows for reciprocal forecasting of local motion-conditioned global trajectory and trajectory-conditioned local pose. By leveraging this framework, Trajectory2Pose effectively handles the complexities of human motion and long-term multi-agent interactions, leading to improved performance in complex environments. A new dataset has been constructed from real-world images and 2D annotations to facilitate comprehensive evaluation of the proposed model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to predict where people will be in the future, considering how they move and interact with each other. This is important because it can help us understand human behavior and improve applications like robotics, gaming, and surveillance. The authors created a special kind of computer program that looks at both big movements (like walking or running) and small movements (like waving or pointing). They also made a new dataset to test their model and showed that it works better than other models in certain situations. |
Keywords
» Artificial intelligence » Multi modal
