Examplar-based object posture super-resolution using manifold learning

Abstract

This paper proposes a learning-based approach to increase the temporal resolutions of human motion sequences. Given a set of high resolution motion sequences, our idea is first to learn the motion tendency from this learning dataset and then synthesize new postures for the low-resolution sequence according to the learned motion tendency. We summarize the proposed framework in the following steps: (1) Each motion sequence is first projected into a low-dimension manifold space, where the local distance between postures could be better preserved. We then represent each of the projected motion sequences as a motion trajectory. (2) Next, motion priors learned from the HR training sequences are used to reconstruct the motion trajectory for the input sequence. (3) Finally, we use the reconstructed motion trajectory combined with object inpainting technique to generate the final result. Our experimental results demonstrate the effectiveness of the proposed method, and also show its outperformance over existing approaches.