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

arXiv: https://arxiv.org/abs/2201.02503 · HTML: https://mlsanity.com/paper/2201.02503/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 7 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [Recent Advances in Deep Learning for Object Detection](https://mlsanity.com/paper/1908.03673/) (2019)
- [A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives](https://mlsanity.com/paper/2009.00564/) (2020)

## Abstract (arXiv)

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.