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2. CALM: Conditional Adversarial Latent Models for Directable Virtual Characters
 
 # CALM: Conditional Adversarial Latent Models for Directable Virtual Characters

  ![](/sites/default/files/styles/wide/public/publications/calm_teaser%20%281%29.jpg?itok=_mjSW0Nu)

 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.



 ## Authors



[Chen Tessler](/index.php/person/chen-tessler)

[Yoni Kasten](/index.php/person/yoni-kasten)

Yunrong Guo (NVIDIA)

[Shie Mannor](/index.php/person/shie-mannor)

[Gal Chechik](/index.php/person/gal-chechik)

Xue Bin Peng (NVIDIA)

 

 

 ## Publication Date



Tuesday, May 2, 2023

 

 ## Published in



[SIGGRAPH 2023](https://s2023.siggraph.org/)

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/index.php/research-area/machine-learning-artificial-intelligence)

[Computer Graphics](/index.php/research-area/computer-graphics)

[Generative AI](/index.php/research-area/generative-ai)

 

 

 ## External Links



[Paper overview](https://research.nvidia.com/labs/par/calm/)

 

 

 ## Uploaded Files



[Paper](https://d1qx31qr3h6wln.cloudfront.net/publications/SIGGRAPH2023_CALM.pdf "Open file in new window")33.17 MB