Motion retrival using low-rank decomposition of Fundamental Ratios

Abstract

This paper proposes a novel framework for efficient retrieval of motion capture data. The method uses Fundamental Ratios to convert action sequences into compact representations of the action, greatly reducing the spatiotemporal dimensionality of the sequences. We propose a low-rank decomposition scheme that allows for converting the motion sequence volumes into compact lower dimensional representations, without losing the nonlinear dynamics of the motion manifold, and the proposed method performs well even when interclass differences are small or intra-class differences are large. We evaluate the performance of our retrieval framework on the CMU mocap dataset and Microsoft Kinect dataset, which demonstrate satisfying retrieval rates.