  We consider the problem of synthesizing multi-action human motion sequences
of arbitrary lengths. Existing approaches have mastered motion sequence
generation in single action scenarios, but fail to generalize to multi-action
and arbitrary-length sequences. We fill this gap by proposing a novel efficient
approach that leverages expressiveness of Recurrent Transformers and generative
richness of conditional Variational Autoencoders. The proposed iterative
approach is able to generate smooth and realistic human motion sequences with
an arbitrary number of actions and frames while doing so in linear space and
time. We train and evaluate the proposed approach on PROX and Charades
datasets, where we augment PROX with ground-truth action labels and Charades
with human mesh annotations. Experimental evaluation shows significant
improvements in FID score and semantic consistency metrics compared to the
state-of-the-art.
