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Optimization is an appealing way to compute the motion of an animated character because it allows the user to specify the desired motion in a sparse, intuitive way. The difficulty of solving this problem for complex characters such as humans is due in part to the high dimensionality of the search space. The dimensionality is an artifact of the problem representation because most dynamic human behaviors are intrinsically low dimensional with, for example, legs and arms operating in a coordinated way. We describe a method that exploits this observation to create an optimization problem that is easier to solve. Our method utilizes an existing motion capture database to find a low-dimensional space that captures the properties of the desired behavior. We show that when the optimization problem is solved within this low-dimensional subspace, a sparse sketch can be used as an initial guess and full physics constraints can be enabled. We demonstrate the power of our approach with examples of forward, vertical, and turning jumps; with running and walking; and with several acrobatic flips.

Optimization is an appealing way to compute the motion of an animated character because it allows the user to specify the desired motion in a sparse, intuitive way. The difficulty of solving this problem for complex characters such as humans is due in part to the high dimensionality of the search space. The dimensionality is an artifact of the problem representation because most dynamic human behaviors are intrinsically low dimensional with, for example, legs and arms operating in a coordinated way. We describe a method that exploits this observation to create an optimization problem that is easier to solve. Our method utilizes an existing motion capture database to find a low-dimensional space that captures the properties of the desired behavior. We show that when the optimization problem is solved within this low-dimensional subspace, a sparse sketch can be used as an initial guess and full physics constraints can be enabled. We demonstrate the power of our approach with examples of forward, vertical, and turning jumps; with running and walking; and with several acrobatic flips.

This paper describes an algorithm for automatically adapting existing simulated behaviors to new characters. Animating a new character is difficult because a control system tuned for one character will not, in general, work on a character with different limb lengths, masses, or moments of inertia. The algorithm presented here adapts the control system to a new character in two stages. First, the control system parameters are scaled based on the sizes, masses, and moments of inertia of the new and the original characters. Then a subset of the parameters is fine-tuned using a search process based on simulated annealing. To demonstrate the effectiveness of this approach, we animate the running motion of a woman, child, and imaginary character by modifying the control system for a man. We also animate the bicycling motion of a second imaginary character by modifying the control system for a man. We evaluate the results of this approach by comparing the motion of the simulated human runners with video of an actual child and with data for men, women, and children in the literature. In addition to adapting a control system for a new model, this approach can also be used to adapt the control system in an on-line fashion to produce a physically realistic metamorphosis from the original to the new model while the morphing character is performing the behavior. We demonstrate this on-line adaptation with a morph from a man to a woman over a period of twenty seconds.