HUMOF: Human Motion Forecasting in Interactive Social Scenes

• URL: http://arxiv.org/abs/2506.03753v2
• Date: Thu, 05 Jun 2025 05:26:07 GMT
• Title: HUMOF: Human Motion Forecasting in Interactive Social Scenes
• Authors: Caiyi Sun, Yujing Sun, Xiao Han, Zemin Yang, Jiawei Liu, Xinge Zhu, Siu Ming Yiu, Yuexin Ma,
• Abstract summary: Complex scenes present significant challenges for predicting human behaviour due to the abundance of interaction information.<n>We propose an effective method for human motion forecasting in interactive scenes.<n>Our method achieves state-of-the-art performance across four public datasets.
• Score: 29.621970821619424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Code will be released when this paper is published.

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