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Multi-person pose understanding from RGB videos involves three complex tasks:pose estimation, tracking and motion forecasting. Intuitively, accuratemulti-person pose estimation facilitates robust tracking, and robust trackingbuilds crucial history for correct motion forecasting. Most existing workseither focus on a single task or employ multi-stage approaches to solvingmultiple tasks separately, which tends to make sub-optimal decision at eachstage and also fail to exploit correlations among the three tasks. In thispaper, we propose Snipper, a unified framework to perform multi-person 3D poseestimation, tracking, and motion forecasting simultaneously in a single stage.We propose an efficient yet powerful deformable attention mechanism toaggregate spatiotemporal information from the video snippet. Building upon thisdeformable attention, a video transformer is learned to encode thespatiotemporal features from the multi-frame snippet and to decode informativepose features for multi-person pose queries. Finally, these pose queries areregressed to predict multi-person pose trajectories and future motions in asingle shot. In the experiments, we show the effectiveness of Snipper on threechallenging public datasets where our generic model rivals specializedstate-of-art baselines for pose estimation, tracking, and forecasting.

Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet