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.
@article{arxiv.2305.02195,
title = {CALM: Conditional Adversarial Latent Models for Directable Virtual Characters},
author = {Chen Tessler and Yoni Kasten and Yunrong Guo and Shie Mannor and Gal Chechik and Xue Bin Peng},
journal= {arXiv preprint arXiv:2305.02195},
year = {2023}
}