@inproceedings{08421230d2974395b88ae8f2ac1dab78,
title = "CALM: Conditional Adversarial Latent Models for Directable Virtual Characters",
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.",
keywords = "adversarial training, animated character control, motion capture data, reinforcement learning",
author = "Chen Tessler and Yoni Kasten and Yunrong Guo and Shie Mannor and Gal Chechik and Peng, {Xue Bin}",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 2023 Special Interest Group on Computer Graphics and Interactive Techniques Conference, SIGGRAPH 2023 ; Conference date: 06-08-2023 Through 10-08-2023",
year = "2023",
month = jul,
day = "23",
doi = "https://doi.org/10.1145/3588432.3591541",
language = "الإنجليزيّة",
series = "Proceedings - SIGGRAPH 2023 Conference Papers",
editor = "Spencer, {Stephen N.}",
booktitle = "Proceedings - SIGGRAPH 2023 Conference Papers",
}