Interaction-Aware Trajectory Conditioning for Efficient Long-Term Multi-Agent 3D Human Pose Forecasting

The core message of this paper is to propose an interaction-aware trajectory-conditioned approach for efficient long-term multi-agent 3D human pose forecasting. The method leverages a coarse-to-fine strategy, first forecasting multi-modal global trajectories and then conditioning local pose predictions on each trajectory mode to jointly constitute the final human motion.