Sports games are the ideal test-bed for motion-matching and similar machine-learned approaches to interactive animation. Here we see it for basketball, including complex ball motion and interaction.

“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.”

Read the paper here.