Directable Animation

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Animation is a creative endeavor that relies heavily on available tools and workflows, with keyframing being at the heart of much of traditional animation and computer animation. Recently, new methods are increasingly leveraging machine learning and physics to create animated movement via predictive modeling. However, many of these approaches some from the machine learning community and focus on automated solutions, with little attention being paid to giving animators sufficient control over the motion. In this project, we seek to develop methods that exploit the benefits of physics and data-driven predictive methods, while also allowing for animator directability.We pursue three specific approaches. First, we will investigate how keyframing can leverage passive physics-based simulation in order to easily produce natural contact and collision behaviors, as applied to characters interacting with their environment. Second, we explore and extend data-driven solutions to keyframing-based motion completion problems. Third, we investigate methods that enable animators to better leverage deep reinforcement learning to produce physics-based characters with controllable motion styles and motion variations. Taken together, these three tackle promising way of integrating some of the creative demands of artists and animators with the capabilities afforded by predictive models.