Computational framework demonstrates joint modeling of individual pose dynamics and group coordination from video, suggesting improved multi-person activity recognition.
Understanding coordinated human activities involving multiple interacting people — such as team sports, group dance, or collaborative physical tasks — requires reasoning not just about individual pose and motion but about the relational and temporal coordination between multiple people simultaneously. This project proposes a multi-person, human-centric motion understanding framework that jointly models individual pose dynamics and inter-person coordination patterns to recognize and analyze complex coordinated activities from video.
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Adishree Gupta (2026) studied this question.
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