Title: Flexible human motion transition via hybrid deep neural network and quadruple-like structure learning

Authors: Shu-Juan Peng; Liang-Yu Zhang; Xin Liu

Addresses: College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China ' Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University, Xiamen, 361021, China; Fujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen, 361021, China ' Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University, Xiamen, 361021, China; Fujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen, 361021, China

Abstract: Skeletal motion transition is of crucial importance to the animation creation. In this paper, we propose a hybrid deep learning framework that allows for efficient human motion transition. First, we integrate a convolutional restricted Boltzmann machine with deep belief network to extract the spatio-temporal features of each motion style, featuring on appropriate detection of transition points. Then, a quadruples-like data structure is exploited for motion graph building, motion splitting and indexing. Accordingly, the similar frames fulfilling the transition segments can be efficiently retrieved. Meanwhile, the transition length is reasonably computed according to the average speed of the motion joints. As a result, different kinds of diverse motions can be well transited with satisfactory performance. The experimental results show that the proposed transition approach brings substantial improvements over the state-of-the-art methods.

Keywords: skeletal motion transition; hybrid deep learning; convolutional restricted Boltzmann machine; quadruples-like data structure.

DOI: 10.1504/IJCSE.2021.115100

International Journal of Computational Science and Engineering, 2021 Vol.24 No.2, pp.136 - 146

Received: 16 Jun 2020
Accepted: 08 Sep 2020

Published online: 12 May 2021 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article