Probabilistic and constraint based articulated model fitting

Abstract

A depth sensor obtains images of articulated portions of a user's body such as the hand. A predefined model of the articulated body portions is provided. Representative attract points of the model are matched to centroids of the depth sensor data, and a rigid transform of the model is performed, in an initial, relatively coarse matching process. This matching process is then refined in a non-rigid transform of the model, using attract point-to-centroid matching. In a further refinement, an iterative process rasterizes the model to provide depth pixels of the model, and compares the depth pixels of the model to the depth pixels of the depth sensor. The refinement is guided by whether the depth pixels of the model are overlapping or non-overlapping with the depth pixels of the depth sensor. Collision, distance and angle constraints are also imposed on the model.