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.
Highlights
Interactive dance has drawn growing attention on the new form of performing arts for choreographers, composers, visual artists, computers, and engineers
In Ref. [12], the authors introduced a method in real-time for the synthesis of highly complex human movements using a new training system, called the auto-conditioned recurrent neural network
It is given a better accuracy rate when compared to auto-conditioned recurrent neural network (acRNN), MMC, low-rank matrix completion (LRMC), and kinect sensors (KS) methods
Summary
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 in their liberty to communicate with and regulate audio and visual feedback for choreographers and dancers and in their difficulties of creating feedback motors capable of responding quickly to the dancer’s motion [1, 2]. Computer scientists find it more difficult to establish robust signal processing and model recognition algorithms based on movement data that dancers use to. Simple activities are using single-layered methods in a sequential and space–time manner In this manuscript, section “Literature survey” discusses the literature review, and section “Machine learning model based two‐dimensional matrix computation (MM‐TDMC)” explains the importance of the two-dimensional matrix computation model and description of motion and dance recovery. Section “Experimental results and discussion” briefs about the numerical analysis, and section “Conclusion” concludes the research with future extension
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