提升复杂社交场景下人类运动预测的准确性。
HUMOF: Human Motion Forecasting in Interactive Social Scenes
- 分层交互特征表示捕捉全局与细节信息
- 粗到精的交互推理模块融合空间与频率视角
- 在四个公开数据集上达到领先效果
复杂场景中，人与人、人与环境的交互信息繁多，显著增加了行为分析难度和运动预测不确定性，现有方法在此类场景表现不佳。本文提出一种高效的人类运动预测方法，设计分层交互特征表示，使高层特征捕捉整体交互上下文，低层特征关注细粒度细节；并引入粗到精的交互推理模块，从空间与频率双视角有效利用分层特征，提升预测精度。该方法在四个公开数据集上均达到当前最优性能。源代码将发布于 https://github.com/scy639/HUMOF。
原文摘要 · Abstract (English)
Complex scenes present significant challenges for predicting human behaviour due to the abundance of interaction information, such as human-human and humanenvironment interactions. These factors complicate the analysis and understanding of human behaviour, thereby increasing the uncertainty in forecasting human motions. Existing motion prediction methods thus struggle in these complex scenarios. In this paper, we propose an effective method for human motion forecasting in interactive scenes. To achieve a comprehensive representation of interactions, we design a hierarchical interaction feature representation so that high-level features capture the overall context of the interactions, while low-level features focus on fine-grained details. Besides, we propose a coarse-to-fine interaction reasoning module that leverages both spatial and frequency perspectives to efficiently utilize hierarchical features, thereby enhancing the accuracy of motion predictions. Our method achieves state-of-the-art performance across four public datasets. The source code will be available at https://github.com/scy639/HUMOF.
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