Vyas, BharatO'Sullivan, CarolLiu, LingjieAverkiou, Melinos2024-04-302024-04-302024978-3-03868-239-41017-4656https://doi.org/10.2312/egp.20241042https://diglib.eg.org/handle/10.2312/egp20241042Computer animation of realistic human characters remains a significant challenge. This work used deep reinforcement learning to generate physics-based characters with diverse body shapes. We aimed to replicate reference motions like walking or jogging while considering individual variations in body shape and mass. Reference motions served as training targets, accounting for differences in shape parameters to accommodate mass variations. This method produced animations that accurately capture human motion details, leading to diverse and lifelike character performances.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Physical simulation; Procedural animation; Motion captureComputing methodologies → Physical simulationProcedural animationMotion capture10.2312/egp.202410422 pages