[SIGGRAPH 2020] Local Motion Phases for Learning Multi-Contact Character Movements

SIGGRAPH 2020

[SIGGRAPH 2020] Local Motion Phases for Learning Multi-Contact Character Movements

Jan 06, 2021
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Abstract: Controlling characters to perform a large variety of dynamic, fast-paced and quickly changing movements is a key challenge in character animation. In this research, we present a deep learning framework to interactively synthesize such animations in high quality, both from unstructured motion data and without any manual labeling. We introduce the concept of local motion phases, and show our system being able to produce various motion skills, such as ball dribbling and professional maneuvers in basketball plays, shooting, catching, avoidance, multiple locomotion modes as well as different character and object interactions, all generated under a unified framework. Authors: Sebastian Starke, Yiwei Zhao, Taku Komura, Kazi Zaman (University of Edinburgh and Electronic Arts)

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