RACon: Retrieval-Augmented Simulated Character Locomotion Control
This addresses the problem of unresponsive control in character animation for animators and game developers, representing an incremental improvement over existing generative models.
The paper tackles the challenge of driving simulated characters with lifelike motion by introducing RACon, a retrieval-augmented hierarchical reinforcement learning method that improves responsiveness to user control, surpassing existing techniques in quality and quantity for locomotion control.
In computer animation, driving a simulated character with lifelike motion is challenging. Current generative models, though able to generalize to diverse motions, often pose challenges to the responsiveness of end-user control. To address these issues, we introduce RACon: Retrieval-Augmented Simulated Character Locomotion Control. Our end-to-end hierarchical reinforcement learning method utilizes a retriever and a motion controller. The retriever searches motion experts from a user-specified database in a task-oriented fashion, which boosts the responsiveness to the user's control. The selected motion experts and the manipulation signal are then transferred to the controller to drive the simulated character. In addition, a retrieval-augmented discriminator is designed to stabilize the training process. Our method surpasses existing techniques in both quality and quantity in locomotion control, as demonstrated in our empirical study. Moreover, by switching extensive databases for retrieval, it can adapt to distinctive motion types at run time.