A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives [article]

Alexander Mathis and Steffen Schneider and Jessy Lauer and Mackenzie W. Mathis
2020 arXiv   pre-print
Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem. Recent advances in deep learning have tremendously advanced predicting posture from videos directly, which quickly impacted neuroscience and biology more broadly. In this primer we review the budding field of motion capture with deep learning. In particular, we will discuss the principles of those novel algorithms, highlight their potential as well as pitfalls for experimentalists, and provide a glimpse into the future.
arXiv:2009.00564v2 fatcat:w22iv453cbaa5fidf5hwemcxeu