HUMOF: Human Motion Forecasting in Interactive Social Scenes
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910091981094912 |
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| author | Sun, Caiyi Sun, Yujing Han, Xiao Yang, Zemin Liu, Jiawei Zhu, Xinge Yiu, Siu Ming Ma, Yuexin |
| author_facet | Sun, Caiyi Sun, Yujing Han, Xiao Yang, Zemin Liu, Jiawei Zhu, Xinge Yiu, Siu Ming Ma, Yuexin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03753 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HUMOF: Human Motion Forecasting in Interactive Social Scenes Sun, Caiyi Sun, Yujing Han, Xiao Yang, Zemin Liu, Jiawei Zhu, Xinge Yiu, Siu Ming Ma, Yuexin Computer Vision and Pattern Recognition 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. |
| title | HUMOF: Human Motion Forecasting in Interactive Social Scenes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.03753 |