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Markerless motion capture has become an active field of research in computervision in recent years. Its extensive applications are known in a great varietyof fields, including computer animation, human motion analysis, biomedicalresearch, virtual reality, and sports science. Estimating human posture hasrecently gained increasing attention in the computer vision community, but dueto the depth of uncertainty and the lack of the synthetic datasets, it is achallenging task. Various approaches have recently been proposed to solve thisproblem, many of which are based on deep learning. They are primarily focusedon improving the performance of existing benchmarks with significant advances,especially 2D images. Based on powerful deep learning techniques and recentlycollected real-world datasets, we explored a model that can predict theskeleton of an animation based solely on 2D images. Frames generated fromdifferent real-world datasets with synthesized poses using different bodyshapes from simple to complex. The implementation process uses DeepLabCut onits own dataset to perform many necessary steps, then use the input frames totrain the model. The output is an animated skeleton for human movement. Thecomposite dataset and other results are the "ground truth" of the deep model.

A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets