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