  Action recognition is a relatively established task, where givenan input
sequence of human motion, the goal is to predict its ac-tion category. This
paper, on the other hand, considers a relativelynew problem, which could be
thought of as an inverse of actionrecognition: given a prescribed action type,
we aim to generateplausible human motion sequences in 3D. Importantly, the set
ofgenerated motions are expected to maintain itsdiversityto be ableto explore
the entire action-conditioned motion space; meanwhile,each sampled sequence
faithfully resembles anaturalhuman bodyarticulation dynamics. Motivated by
these objectives, we followthe physics law of human kinematics by adopting the
Lie Algebratheory to represent thenaturalhuman motions; we also propose
atemporal Variational Auto-Encoder (VAE) that encourages adiversesampling of
the motion space. A new 3D human motion dataset, HumanAct12, is also
constructed. Empirical experiments overthree distinct human motion datasets
(including ours) demonstratethe effectiveness of our approach.
