不依赖自回归生成，同时预测人体局部动作和全局运动轨迹。
Long-Term Prediction of Local and Global Human Motion with Occlusion Recovery
- 采用时空注意力机制的非自回归模型，避免误差累积
- 可同时预测局部姿态与全局空间运动轨迹
- 支持遮挡恢复和不同长度历史输入，适合真实场景
人体运动描述了三维全身体态的动态变化。预测此类运动在人机交互、自动驾驶、动画生成和医疗等领域具有重要意义。现有研究多采用双向注意力建模时空依赖，通常以自回归方式预测人体运动，易导致误差随时间累积，且仅关注局部姿态预测。为此，本文提出一种基于时空注意力的非自回归变换器模型，不仅用于局部姿态预测，还实现全局运动的空间预测。此外，为提升实际应用能力，模型还训练具备遮挡关节恢复能力，并能处理不同长度的历史观测序列。代码已公开于 https://github.com/Q-Y-Yang/Prediction-of-Local-and-Global-Human-Motion。
原文摘要 · Abstract (English)
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 anticipation, but also for global motion prediction in space. Furthermore, to enhance its applicability in real-world scenarios, our model is also trained to recover missing joints due to occlusions, and is capable of processing varying lengths of history observations. Our code is publicly available at https://github.com/Q-Y-Yang/Prediction-of-Local-and-Global-Human-Motion.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。