Abstract

Human motion plays a key role in the production of films, video games, virtual reality applications, and the control of humanoid robots. Unfortunately, it is hard to generate high quality human motion for character animation either manually or algorithmically. As a result, approaches based on motion capture data have become a central focus of character animation research in recent years. We observe three principal weaknesses in previous work using data-driven approaches for modelling human motion. First, basic balance behaviours and locomotion tasks are currently not well modelled. Second, the ability to produce high quality motion that is responsive to its environment is limited. Third, knowledge about human motor control is not well utilized. This thesis develops several techniques to generalize motion capture character animations to balance and respond. We focus on balance and locomotion tasks, with an emphasis on responding to disturbances, user interaction, and motor control integration. For this purpose, we investigate both kinematic and dynamic models. Kinematic models are intuitive and fast to construct, but have narrow generality, and thus require

Citations