Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware
  Trajectory Conditioning
Jaewoo Jeong Daehee Park Kuk-Jin Yoon
Human pose forecasting garners attention for its diverse applications.
However, challenges in modeling the multi-modal nature of human motion and
intricate interactions among agents persist, particularly with longer
timescales and more agents. In this paper, we propose an interaction-aware
trajectory-conditioned long-term multi-agent human pose forecasting model,
utilizing a coarse-to-fine prediction approach: multi-modal global trajectories
are initially forecasted, followed by respective local pose forecasts
conditioned on each mode. In doing so, our Trajectory2Pose model introduces a
graph-based agent-wise interaction module for a reciprocal forecast of local
motion-conditioned global trajectory and trajectory-conditioned local pose. Our
model effectively handles the multi-modality of human motion and the complexity
of long-term multi-agent interactions, improving performance in complex
environments. Furthermore, we address the lack of long-term (6s+) multi-agent
(5+) datasets by constructing a new dataset from real-world images and 2D
annotations, enabling a comprehensive evaluation of our proposed model.
State-of-the-art prediction performance on both complex and simpler datasets
confirms the generalized effectiveness of our method. The code is available at
https://github.com/Jaewoo97/T2P.