  We tackle the problem of action-conditioned generation of realistic and
diverse human motion sequences. In contrast to methods that complete, or
extend, motion sequences, this task does not require an initial pose or
sequence. Here we learn an action-aware latent representation for human motions
by training a generative variational autoencoder (VAE). By sampling from this
latent space and querying a certain duration through a series of positional
encodings, we synthesize variable-length motion sequences conditioned on a
categorical action. Specifically, we design a Transformer-based architecture,
ACTOR, for encoding and decoding a sequence of parametric SMPL human body
models estimated from action recognition datasets. We evaluate our approach on
the NTU RGB+D, HumanAct12 and UESTC datasets and show improvements over the
state of the art. Furthermore, we present two use cases: improving action
recognition through adding our synthesized data to training, and motion
denoising. Code and models are available on our project page.
