A high-precision correction method in non-rigid 3D motion poses reconstruction

Abstract

Occlusion, rotation and other factors affect human motion structure because of the incomplete acquired image sequence, resulting in poor performance of non-rigid three-dimensional (3D) motion pose reconstruction. A non-rigid 3D reconstruction and high-precision correction method for motion pose are studied in this paper. A non-rigid imaging model is designed to obtain 3D moving images. According to the frame difference and morphological processing, the background of image is separated and denoised. Combined with motion analysis, 3D motion pose features are extracted as identification of non-rigid 3D motion error actions in a hybrid Convolution Neural Network-Hidden Markov Model to train the correction coefficients, which are used to adjust the pose in 3D motion reconstruction and realise correction. Experimental results show that this method has high precision reconstruction and correction of non-rigid 3D motion pose.

KEYWORDS:

• Non-rigid
• 3D motion
• pose correction
• motion reconstruction
• correction coefficient

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 62207012]; Natural Science Foundation of Hunan Province [grant numbers 2020JJ4434,2020JJ5368]; National Social Science Fundation of China [grant number AEA200013]; Key Scientific Research Projects of Education Department of Hunan Province [grant number 22A0058]; Key Research Project on Degree and Graduate Education Reform of Hunan Province [grant number 2020JGZD025].