QMEDNet: A quaternion-based multi-order differential encoder-decoder model for 3D human motion prediction.

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Abstract

In order to deal with the sequence information in the task of 3D human motion prediction effectively, many previous methods seek to predict the motion state of the next moment using the traditional recurrent neural network in Euclidean space. However human motion representation in Euclidean space has high distortion and shows a weak semantic expression when using deep learning models. In this work, we try to process human motion by mapping Euclidean space into a Hypercomplex vector space. We propose a novel model based on quaternion to predict the three-dimensional motion of a human body. The core idea of this study is to use the fusion information to understand and process the human motion state in quaternion space. The multi-order differential information is fused both in the encoder and decoder of feature extraction and mapped to the quaternion space, respectively. The encoder takes graph convolution as the basic unit and the decoder adopts gated recurrent units. Numerous experiments have been carried out to prove that the multi-order information in quaternion space can help build a more reasonable description for 3D human motion. The performance of the proposed QMEDNet is superior to most of the advanced short and long-term motion prediction methods in both public datasets, Human 3.6M and CMU Mocap.Copyright © 2022 Elsevier Ltd. All rights reserved.