Keyframe Extraction for Human Motion Capture Data Based on Joint Kernel Sparse Representation

Human motion capture data, which are used to animate animation characters, have been widely used in many areas. To satisfy the high-precision requirement, human motion data are captured with a high frequency (120 frames/s) by a high-precision capture system. However, the high frequency and nonlinear structure make the storage, retrieval, and browsing of motion data challenging problems, which can be solved by keyframe extraction. Current keyframe extraction methods do not properly model two important characteristics of motion data, i.e., sparseness and Riemannian manifold structure. Therefore, we propose a new model called joint kernel sparse representation (SR), which is in marked contrast to all current keyframe extraction methods for motion data and can simultaneously model the sparseness and the Riemannian manifold structure. The proposed model completes the SR in a kernel-induced space with a geodesic exponential kernel, whereas the traditional SR cannot model the nonlinear structure of motion data in the Euclidean space. Meanwhile, because of several important modifications to traditional SR, our model can also exploit the relations between joints and solve two problems, i.e., the unreasonable distribution and redundancy of extracted keyframes, which current methods do not solve. Extensive experiments demonstrate the effectiveness of the proposed method.