  Synthesis of long-term human motion skeleton sequences is essential to aid
human-centric video generation with potential applications in Augmented
Reality, 3D character animations, pedestrian trajectory prediction, etc.
Long-term human motion synthesis is a challenging task due to multiple factors
like, long-term temporal dependencies among poses, cyclic repetition across
poses, bi-directional and multi-scale dependencies among poses, variable speed
of actions, and a large as well as partially overlapping space of temporal pose
variations across multiple class/types of human activities. This paper aims to
address these challenges to synthesize a long-term (> 6000 ms) human motion
trajectory across a large variety of human activity classes (>50). We propose a
two-stage activity generation method to achieve this goal, where the first
stage deals with learning the long-term global pose dependencies in activity
sequences by learning to synthesize a sparse motion trajectory while the second
stage addresses the generation of dense motion trajectories taking the output
of the first stage. We demonstrate the superiority of the proposed method over
SOTA methods using various quantitative evaluation metrics on publicly
available datasets.
