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.

在计算机动画中，使用逼真的动作来驱动模拟角色具有挑战性。为了解决这些问题，我们引入了 RACon: 检索增强的模拟角色运动控制。我们的端到端层次强化学习方法利用了检索器和运动控制器。检索器以任务导向的方式从用户指定的数据库中搜索运动专家，从而增强对用户控制的响应能力。然后，选定的运动专家和操纵信号传递给控制器以驱动模拟角色。此外，设计了一个检索增强的鉴别器以稳定训练过程。我们的方法在运动控制的质量和数量上都超过了现有技术，在我们的实证研究中得到了证明。此外，通过切换广泛的检索数据库，它可以在运行时适应不同的运动类型。