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Pose prediction is to predict future poses given a window of previous poses.In this paper, we propose a new problem that predicts poses using 3D jointcoordinate sequences. Different from the traditional pose prediction based onMocap frames, this problem is convenient to use in real applications due to itssimple sensors to capture data. We also present a new framework, PISEP^2(Pseudo Image Sequence Evolution based 3D Pose Prediction), to address this newproblem. Specifically, a skeletal representation is proposed by transformingthe joint coordinate sequence into an image sequence, which can model thedifferent correlations of different joints. With this image based skeletalrepresentation, we model the pose prediction as the evolution of imagesequence. Moreover, a novel inference network is proposed to predict all futureposes in one step by decoupling the decoders in a non-recursive manner.Compared with the recursive sequence to sequence model, we can improve thecomputational efficiency and avoid error accumulation significantly. Extensiveexperiments are carried out on two benchmark datasets (e.g. G3D and FNTU). Theproposed method achieves the state-of-the-art performance on both datasets,which demonstrates the effectiveness of our proposed method.

PISEP^2: Pseudo Image Sequence Evolution based 3D Pose Prediction