  In this work, we present Conditional Adversarial Latent Models (CALM), an
approach for generating diverse and directable behaviors for user-controlled
interactive virtual characters. Using imitation learning, CALM learns a
representation of movement that captures the complexity and diversity of human
motion, and enables direct control over character movements. The approach
jointly learns a control policy and a motion encoder that reconstructs key
characteristics of a given motion without merely replicating it. The results
show that CALM learns a semantic motion representation, enabling control over
the generated motions and style-conditioning for higher-level task training.
Once trained, the character can be controlled using intuitive interfaces, akin
to those found in video games.
