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basketball animation richness


Local Motion Phases Technique Boosts Basketball Animation Richness and Realism

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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.