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CALM: Conditional Adversarial Latent Models for Directable Virtual Characters
- Publication Year :
- 2023
-
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.<br />Comment: Accepted to SIGGRAPH 2023
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2305.02195
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1145/3588432.3591541