  We explore recurrent encoder multi-decoder neural network architectures for
semi-supervised sequence classification and reconstruction. We find that the
use of multiple reconstruction modules helps models generalize in a
classification task when only a small amount of labeled data is available,
which is often the case in practice. Such models provide useful high-level
representations of motions allowing clustering, searching and faster labeling
of new sequences. We also propose a new, realistic partitioning of a
well-known, high quality motion-capture dataset for better evaluations. We
further explore a novel formulation for future-predicting decoders based on
conditional recurrent generative adversarial networks, for which we propose
both soft and hard constraints for transition generation derived from desired
physical properties of synthesized future movements and desired animation
goals. We find that using such constraints allow to stabilize the training of
recurrent adversarial architectures for animation generation.
