Summary of Massively Multi-person 3d Human Motion Forecasting with Scene Context, by Felix B Mueller et al.
Massively Multi-Person 3D Human Motion Forecasting with Scene Context
by Felix B Mueller, Julian Tanke, Juergen Gall
First submitted to arxiv on: 18 Sep 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 proposed scene-aware social transformer model (SAST) aims to forecast long-term human motion, taking into account the stochasticity of human behavior. By incorporating information on the scene environment and the motion of nearby people, SAST improves upon previous models in its ability to handle interactions between varying numbers of people and objects. The approach combines a temporal convolutional encoder-decoder architecture with a Transformer-based bottleneck, allowing for efficient integration of motion and scene data. A denoising diffusion model is used to model the conditional motion distribution. Benchmarking on the Humans in Kitchens dataset shows SAST outperforms other approaches in terms of realism and diversity, as measured by various metrics and a user study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new model for forecasting human motion that takes into account the scene environment and people nearby. This helps make the generated motion look more realistic. The model is called SAST and uses a special combination of techniques to combine information from the scene and the people moving around. This allows it to handle scenes with many different objects and people. The authors tested their model on a dataset that has many different scenarios, and it performed better than other models in terms of how realistic and diverse the generated motion was. |
Keywords
» Artificial intelligence » Diffusion model » Encoder decoder » Transformer
