Collaborating Authors

basketball animation richness

Local Motion Phases Technique Boosts Basketball Animation Richness and Realism


Researchers from the University of Edinburgh School of Informatics and video game company Electronic Arts have proposed a novel framework that learns fast and dynamic character interactions. Trained on an unstructured basketball motion capture database, the model can animate multiple contacts between a player and the ball and other players and the environment. The team's modular and stable framework for data-driven character animation includes data processing, network training and runtime control; and was developed using Unity, Tensor flow, and PyTorch. The approach can perform complex and realistic animations of bipeds or quadrupeds engaged in sports and beyond. Enabling characters to perform a wide variety of dynamic fast-paced and quickly changing movements is a key challenge in character animation.