M3Act: Learning from Synthetic Human Group Activities
This addresses the data scarcity problem for researchers in human-centric computer vision, though it is incremental as it builds on existing synthetic data approaches.
The paper tackles the challenge of limited labeled data for human group activity analysis by introducing M3Act, a synthetic data generator that produces multi-view, multi-group human actions, and shows it improves state-of-the-art methods, such as boosting MOTRv2 from 10th to 2nd place on the DanceTrack dataset.
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.