Recording and quantifying hand and finger movement is essential for understanding the neuromechanical control of the hand. Typically, kinematics are collected through marker-based optoelectronic motion capture systems. However, marker-based systems are time-consuming to setup, expensive, and cumbersome, especially for finger tracking. Advances in markerless systems have potential to overcome these limitations, as demonstrated by recent applications in lower extremity biomechanics research. In this work, we aimed to integrate markerless systems for hand biomechanics research by combining open source markerless motion capture pipelines (MediaPipe and Anipose) and investigating the number of cameras required for tracking single finger flexion–extension movements. Finger movements were recorded at three different speeds (0.50, 0.75, 1 Hz) for each of the instructed fingers (index, middle, ring, little) using 4 webcams. Finger joint angles were compared when using all 4 webcams for triangulating 3D hand key points versus all 2- and 3-camera subset combinations. The number of cameras was found to affect joint angles, with differences up to 20° when using 2 or 3 cameras compared to using all 4 cameras. However, we found some 2-camera orientations had minimal differences compared to using all 4 cameras (< 4° difference for the sum of finger [metacarpal, proximal interphalangeal, and distal phalangeal] joint angles). Thus, there can be little to no benefit of adding more than 2 cameras for 3D markerless tracking of the hand during single finger flexion–extension with optimal camera placement.