运动捕捉与深度学习入门：原理、陷阱和前景
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives
摘要：
非侵入性地从视频中提取行为测量是一个困难的计算问题。深度学习的最新进展极大地促进了直接从视频中预测姿势，这迅速影响了神经科学和生物学。在这篇入门文章中，我们回顾了使用深度学习进行运动捕捉的新兴领域。特别是，我们将讨论这些新算法的原理，强调它们的潜力以及实验主义者的陷阱，并提供对未来的一瞥。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
