Inter-X: Towards Versatile Human-Human Interaction Analysis

Liang Xu; Xintao Lv; Yichao Yan; Xin Jin; Shuwen Wu; Congsheng Xu; Yifan Liu; Yizhou Zhou; Fengyun Rao; Xingdong Sheng; Yunhui Liu; Wenjun Zeng; Xiaokang Yang

arXiv · 2312.16051

Inter-X: Towards Versatile Human-Human Interaction Analysis

Liang Xu·Xintao Lv·Yichao Yan·Xin Jin·Shuwen Wu·Congsheng Xu·Yifan Liu·Yizhou Zhou·Fengyun Rao·Xingdong Sheng·Yunhui Liu·Wenjun Zeng·Xiaokang Yang

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Abstract

The analysis of the ubiquitous human-human interactions is pivotal for understanding humans as social beings. Existing human-human interaction datasets typically suffer from inaccurate body motions, lack of hand gestures and fine-grained textual descriptions. To better perceive and generate human-human interactions, we propose Inter-X, a currently largest human-human interaction dataset with accurate body movements and diverse interaction patterns, together with detailed hand gestures. The dataset includes ~11K interaction sequences and more than 8.1M frames. We also equip Inter-X with versatile annotations of more than 34K fine-grained human part-level textual descriptions, semantic interaction categories, interaction order, and the relationship and personality of the subjects. Based on the elaborate annotations, we propose a unified benchmark composed of 4 categories of downstream tasks from both the perceptual and generative directions. Extensive experiments and comprehensive analysis show that Inter-X serves as a testbed for promoting the development of versatile human-human interaction analysis. Our dataset and benchmark will be publicly available for research purposes.

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Liang Xu, Xintao Lv, Yichao Yan, Xin Jin, Shuwen Wu, Congsheng Xu, Yifan Liu, Yizhou Zhou, Fengyun Rao, Xingdong Sheng, Yunhui Liu, Wenjun Zeng, Xiaokang Yang. 2023-12-26. Inter-X: Towards Versatile Human-Human Interaction Analysis. https://arxiv.org/abs/2312.16051

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