Learning from Synthetic Human Group Activities
Authors: Che-Jui Chang, Danrui Li, Deep Patel, Parth Goel, Honglu Zhou, Seonghyeon Moon, Samuel S. Sohn, Sejong Yoon, Vladimir Pavlovic, Mubbasir Kapadia

The study of complex human interactions and group activities has become a focal point in human-centric computer vision. However progress in related tasks is often hindered by the challenges of obtaining large-scale labeled datasets from real-world scenarios. To address the limitation we introduce M3Act a synthetic data generator for multi-view multi-group multi-person human atomic actions and group activities. Powered by Unity Engine M3Act features multiple semantic groups highly diverse and photorealistic images and a comprehensive set of annotations which facilitates the learning of human-centered tasks across single-person multi-person and multi-group conditions. We demonstrate the advantages of M3Act across three core experiments. The results suggest our synthetic dataset can significantly improve the performance of several downstream methods and replace real-world datasets to reduce cost. Notably M3Act improves the state-of-the-art MOTRv2 on DanceTrack dataset leading to a hop on the leaderboard from 10th to 2nd place. Moreover M3Act opens new research for controllable 3D group activity generation. We define multiple metrics and propose a competitive baseline for the novel task. Our code and data are available at our project page: http://cjerry1243.github.io/M3Act.