Comparer des méthodes

	Capture de mouvement sans marqueurs	Cinématique directe
Domaine	Biomécanique	Biomécanique
Famille	Process / pipeline	Process / pipeline
Année d'origine	2017	1986
Auteur d'origine	Zhe Cao	John Craig
Type	Deep learning pipeline	Computational geometric 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).	Craig, J. J. (2005). Introduction to Robotics: Mechanics and Control (3rd ed.). Pearson.
Alias	Marker-free tracking, Vision-based motion capture, Deep learning pose estimation	FK, Kinematic chain, Anatomical chain
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.	Forward kinematics is the calculation of the position and orientation of a distal body segment (such as the hand) based on the joint angles of proximal segments. Originally formalized in robotics by John Craig and adapted to biomechanics, it allows practitioners to predict endpoint location from known joint configuration.