Long-Term Prediction of Local and Global Human Motion with Occlusion Recovery
Human motion describes the three-dimensional full-body movement of a person. Anticipating such motion holds significant relevance across a wide range of application domains such as human-robot interaction, autonomous driving, animation, and healthcare. In recent research, spatial and temporal dependencies are modeled by bidirectional attention mechanisms. These typically anticipate human motion in an autoregressive manner which could cause an accumulation of errors over time. As a consequence, they solely focus on local pose forecasting. To address these limitations, we propose a non-autoregressive transformer based on spatio-temporal attention, and train it not only for local pose antici...
| Authors | Qiaoyue Yang, Sven Heutger, Christopher Niemann et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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