handtracking

Handtracking refers to the process of determining the position, orientation, and configuration of one or more hands in real time using visual or sensor data. It aims to estimate hand pose, which can include joint angles, finger postures, and in some systems a detailed 3D surface mesh.

Most handtracking systems are markerless and rely on cameras or depth sensors. They detect hand landmarks, commonly 21 joints per hand, and reconstruct a pose in two or three dimensions. Output formats range from sparse landmark coordinates to full 3D skeletons or reconstructed hand meshes. Depth information from infrared or structured-light sensors improves robustness to distance and perspective, while fusion with RGB data helps maintain accuracy in challenging scenes.

Technologies and frameworks frequently employ machine learning to detect hand landmarks, followed by geometric fitting or learning-based pose estimation. Notable examples include frameworks and models such as MediaPipe Hands and OpenPose Hands. In consumer devices, hand tracking is enabled by forward-facing cameras, depth sensors, or mixed-reality devices, enabling controller-free interaction with software and hardware.

Applications span virtual reality and augmented reality interfaces, gesture-based control for computers and mobile devices, sign language recognition research, robotics teleoperation, gaming, and rehabilitation. Handtracking enables natural, hands-on interaction without physical controllers but must contend with real-world variability.

Challenges include occlusion where fingers occlude each other, rapid motion causing blur, variation in hand size, skin tone, lighting, and background clutter. Latency and accuracy trade-offs, cross-device generalization, and privacy considerations also influence design choices and deployment. Ongoing work seeks to improve robustness, efficiency, and on-device processing.