Machine learning model-based two-dimensional matrix computation model for human motion and dance recovery
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ORIGINAL ARTICLE
Machine learning model‑based two‑dimensional matrix computation model for human motion and dance recovery Yi Zhang1 · Mengni Zhang1 Received: 10 February 2020 / Accepted: 6 August 2020 © The Author(s) 2020
Abstract Many regions of human movement capturing are commonly used. Still, it includes a complicated capturing method, and the obtained information contains missing information invariably due to the human’s body or clothing structure. Recovery of motion that aims to recover from degraded observation and the underlying complete sequence of motion is still a difficult task, because the nonlinear structure and the filming property is integrated into the movements. Machine learning model based two-dimensional matrix computation (MM-TDMC) approach demonstrates promising performance in short-term motion recovery problems. However, the theoretical guarantee for the recovery of nonlinear movement information lacks in the two-dimensional matrix computation model developed for linear information. To overcome this drawback, this study proposes MM-TDMC for human motion and dance recovery. The advantages of the machine learning-based Two-dimensional matrix computation model for human motion and dance recovery shows extensive experimental results and comparisons with auto-conditioned recurrent neural network, multimodal corpus, low-rank matrix completion, and kinect sensors methods. Keywords Computation model · Machine learning · Human motion · Two-dimensional matrix · Neural
Introduction Interactive dance has drawn growing attention on the new form of performing arts for choreographers, composers, visual artists, computers, and engineers. This concern has its roots not only in their liberty to communicate with and regulate audio and visual feedback for choreographers and dancers but also in their difficulties of creating feedback motors capable of responding quickly to the dancer’s motion [1, 2]. This interest is built mainly on the free responses to the choreographer and dancers, not on the challenges facing composers and visual performers to develop feedback engines that respond quickly to the dancer movement [3, 4]. However, computer scientists find it more difficult to establish robust signal processing and model recognition algorithms based on movement data that dancers use to
* Yi Zhang [email protected] Mengni Zhang [email protected] 1
College of Music, Hubei University of Science and Technology, Xianning 450046, China
communicate with feedback engines to regulate audio and visual feedback [5]. For sensing and analyzing human motion, interactive dance represents distinctive difficulties. The exactness of movement analysis, like the use of costume markers, the presence of various dancers, and complicated dance movement, can be determined by many factors in dance performance. Appropriate sensing methods and interactive signals must be chosen, and robust movement analysis algorithms must be developed to allow interactive dance [6, 7]. One of the most efficient methods of digitizing human motio
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