Motions as Queries: One-Stage Multi-Person Holistic Human Motion Capture

Abstract

Existing methods for capturing multi-person holistic human motions from a monocular video usually involve integrating the detector, the tracker, and the human pose & shape estimator into a cascaded system. Differently, we develop a one-stage multi-person holistic human motion capture system, which 1) employs only one network, enabling significant benefits from the end-to-end training on a large-scale dataset; 2) enables performance improving of the tracking module during training, avoiding being limited by a pre-trained tracker; 3) captures the motions of all individuals within a single shot, rather than tracking and estimating each person sequentially. In this system, each query within a temporal cross-attention module is responsible for the long motion of a specific individual, implicitly aggregating individual-specific information throughout the entire video. To further boost the proposed system from end-to-end training, we also construct a synthetic human video dataset, with multi-person and whole-body annotations. Extensive experiments across different datasets demonstrate both the efficacy and the efficiency of both the proposed method and the dataset. Codes are avaiable at https://github.com/KenkunLiu/MaQ.