AbstractWe present a method for automatically generating reduced marker layouts for marker‐based optical motion capture of human hands. The employed motion reconstruction method is based on subspace‐constrained inverse kinematics, which allows for the recovery of realistic hand movements even from sparse input data. We additionally present a user‐specific hand model calibration procedure that fits an articulated hand model to point cloud data of the user's hand. Our marker layout optimization is sensitive to the kinematic structure and the subspace representations of hand articulations utilized in the reconstruction method, in order to generate sparse marker configurations that are optimal for solving the constrained inverse kinematics problem. We propose specific quality criteria for reduced marker sets that combine numerical stability with geometric feasibility of the resulting layout. These criteria are combined in an objective function that is minimized using a specialized surface‐constrained particle swarm optimization scheme, which generates marker layouts bound to the surface of an animated hand model. Our method provides a principled way for determining reduced marker layouts based on subspace representations of hand articulations. We demonstrate the effectiveness of our motion reconstruction and model calibration methods in a thorough evaluation.