@inproceedings{tessler2023calm, author = {Tessler, Chen and Kasten, Yoni and Guo, Yunrong and Mannor, Shie and Chechik, Gal and Peng, Xue Bin}, title = {CALM: Conditional Adversarial Latent Models for Directable Virtual Characters}, year = {2023}, isbn = {9798400701597}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3588432.3591541}, doi = {10.1145/3588432.3591541}, 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.}, booktitle = {ACM SIGGRAPH 2023 Conference Proceedings}, keywords = {reinforcement learning, animated character control, adversarial training, motion capture data}, location = {Los Angeles, CA, USA}, series = {SIGGRAPH '23} }