摘要：We present an inverse kinematics solver based on Gaussian process latent variable models (GP-LVM). Because of the high-dimension of motion capture data, Analyzing them directly is a very hard work. We map the motion capture data from higher-dimensional observation space to two-dimensional latent space based on GP-LVM, then, find out the representative poses of virtual character by clustering the motion capture data in latent space. Finally, weight the representative poses and optimize the weights, combined with constraints on the end effectors, in order to synthesize the optimized pose. The experiments show that our method obtains satisfying effects.
