  Scene-aware global human motion forecasting is critical for manifold
applications, including virtual reality, robotics, and sports. The task
combines human trajectory and pose forecasting within the provided scene
context, which represents a significant challenge.
  So far, only Mao et al. NeurIPS'22 have addressed scene-aware global motion,
cascading the prediction of future scene contact points and the global motion
estimation. They perform the latter as the end-to-end forecasting of future
trajectories and poses. However, end-to-end contrasts with the coarse-to-fine
nature of the task and it results in lower performance, as we demonstrate here
empirically.
  We propose a STAGed contact-aware global human motion forecasting STAG, a
novel three-stage pipeline for predicting global human motion in a 3D
environment. We first consider the scene and the respective human interaction
as contact points. Secondly, we model the human trajectory forecasting within
the scene, predicting the coarse motion of the human body as a whole. The third
and last stage matches a plausible fine human joint motion to complement the
trajectory considering the estimated contacts.
  Compared to the state-of-the-art (SoA), STAG achieves a 1.8% and 16.2%
overall improvement in pose and trajectory prediction, respectively, on the
scene-aware GTA-IM dataset. A comprehensive ablation study confirms the
advantages of staged modeling over end-to-end approaches. Furthermore, we
establish the significance of a newly proposed temporal counter called the
"time-to-go", which tells how long it is before reaching scene contact and
endpoints. Notably, STAG showcases its ability to generalize to datasets
lacking a scene and achieves a new state-of-the-art performance on CMU-Mocap,
without leveraging any social cues. Our code is released at:
https://github.com/L-Scofano/STAG
