  Human-motion generation is a long-standing challenging task due to the
requirement of accurately modeling complex and diverse dynamic patterns. Most
existing methods adopt sequence models such as RNN to directly model
transitions in the original action space. Due to high dimensionality and
potential noise, such modeling of action transitions is particularly
challenging. In this paper, we focus on skeleton-based action generation and
propose to model smooth and diverse transitions on a latent space of action
sequences with much lower dimensionality. Conditioned on a latent sequence,
actions are generated by a frame-wise decoder shared by all latent
action-poses. Specifically, an implicit RNN is defined to model smooth latent
sequences, whose randomness (diversity) is controlled by noise from the input.
Different from standard action-prediction methods, our model can generate
action sequences from pure noise without any conditional action poses.
Remarkably, it can also generate unseen actions from mixed classes during
training. Our model is learned with a bi-directional generative-adversarial-net
framework, which not only can generate diverse action sequences of a particular
class or mix classes, but also learns to classify action sequences within the
same model. Experimental results show the superiority of our method in both
diverse action-sequence generation and classification, relative to existing
methods.
