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
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.