Fast Terrain-Adaptive Motion Generation using Deep Neural Networks

We propose a fast motion adaptation framework using deep neural networks. Traditionally, motion adaptation is performed via iterative numerical optimization. We adopted deep neural networks and replaced the iterative process with the feed-forward inference consisting of simple matrix multiplications. For efficient mapping from contact constraints to character motion, the proposed system is composed of two types of networks: trajectory and pose generators. The networks are trained using augmented motion capture data and are fine-tuned using the inverse kinematics loss. In experiments, our system successfully generates multi-contact motions of a hundred of characters in real-time, and the result motions contain the naturalness existing in the motion capture data.