Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Captura de movimiento sin marcadores× | Cinemática directa× | |
|---|---|---|
| Campo | Biomecánica | Biomecánica |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2017 | 1986 |
| Autor original≠ | Zhe Cao | John Craig |
| Tipo≠ | Deep learning pipeline | Computational geometric pipeline |
| Fuente seminal≠ | 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). DOI ↗ | Craig, J. J. (2005). Introduction to Robotics: Mechanics and Control (3rd ed.). Pearson. link ↗ |
| Alias | Marker-free tracking, Vision-based motion capture, Deep learning pose estimation | FK, Kinematic chain, Anatomical chain |
| Relacionados | 3 | 3 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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