  To synthesize a realistic action sequence based on a single human image, it
is crucial to model both motion patterns and diversity in the action video.
This paper proposes an Action Conditional Temporal Variational AutoEncoder
(ACT-VAE) to improve motion prediction accuracy and capture movement diversity.
ACT-VAE predicts pose sequences for an action clips from a single input image.
It is implemented as a deep generative model that maintains temporal coherence
according to the action category with a novel temporal modeling on latent
space. Further, ACT-VAE is a general action sequence prediction framework. When
connected with a plug-and-play Pose-to-Image (P2I) network, ACT-VAE can
synthesize image sequences. Extensive experiments bear out our approach can
predict accurate pose and synthesize realistic image sequences, surpassing
state-of-the-art approaches. Compared to existing methods, ACT-VAE improves
model accuracy and preserves diversity.
