Evolution of neural networks for gait animation
In this thesis, we describe efforts to create a system that automatically produces realistic real-time animations of walking figures. In traditional computer graphics, the animator is forced to use intuition about the physical world in specifying the motions of objects in a scene, but manual control techniques have generally proven to be unsatisfactory for realism. The use of dynamics greatly improves motion realism and shifts control of the animation from specifying absolute positions of objects to applying forces and torques to the objects in the scene. This is a much more difficult task for a human to control, whereas a neural network is well suited for this task. Through the use of a genetic algorithm, one can determine the performance of a neural network as a whole, and select for whatever behavior is desired. Animator control is then directed through model design and behavior choices.
Bibliography: p. 58-61
Hickey, C. (2004). Evolution of neural networks for gait animation (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/20219