Comparer des méthodes

	Capture de mouvement sans marqueurs	Dynamique Inverse
Domaine	Biomécanique	Biomécanique
Famille	Process / pipeline	Process / pipeline
Année d'origine	2017	1990
Auteur d'origine	Zhe Cao	David Winter
Type	Deep learning pipeline	Computational analysis pipeline
Source fondatrice	Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).	Winter, D. A. (1990). Biomechanics and Motor Control of Human Movement. Wiley-Interscience.
Alias	Marker-free tracking, Vision-based motion capture, Deep learning pose estimation	Inverse problem, Biomechanical inverse dynamics
Apparentées	3	3
Résumé	Markerless motion capture infers the 3D positions and joint angles of a moving subject from video sequences using computer vision and machine learning. Pioneered by deep learning approaches such as OpenPose and MediaPipe, it eliminates the need for reflective markers or inertial sensors, making motion capture accessible and practical for real-world applications.	Inverse dynamics is a biomechanical analysis technique that estimates the forces and moments acting on joints during movement by working backward from observed motion and ground reaction forces. Introduced by David Winter in the early 1990s, it is fundamental to understanding how muscles and joints generate and control human motion.