CALM: Conditional Adversarial Latent Models for Directable Virtual Characters

Chen Tessler; Yoni Kasten; Yunrong Guo; Shie Mannor; Gal Chechik; Xue Bin Peng

doi:10.1145/3588432.3591541

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arXiv · 2305.02195

CALM: Conditional Adversarial Latent Models for Directable Virtual Characters

Chen Tessler·Yoni Kasten·Yunrong Guo·Shie Mannor·Gal Chechik·Xue Bin Peng

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Abstract

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.

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Chen Tessler, Yoni Kasten, Yunrong Guo, Shie Mannor, Gal Chechik, Xue Bin Peng. 2023-05-02. CALM: Conditional Adversarial Latent Models for Directable Virtual Characters. https://doi.org/10.1145/3588432.3591541

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