Markerless Motion Capture

Markerless motion capture aims to reconstruct 3D human movement from video data without the need for physical markers, offering a more accessible and cost-effective alternative to traditional methods. Current research focuses on improving accuracy and robustness using deep learning architectures like transformers and convolutional neural networks, often incorporating biomechanical models and synthetic training data to enhance performance. This technology has significant implications for various fields, including sports biomechanics, clinical gait analysis, and animation, by providing more efficient and potentially more widely applicable tools for movement analysis.