Visually capturing human hand motion requires estimating the 3D hand global pose as well as its local finger articulations. This is a challenging task that requires a search in a high dimensional space due to the high degrees of freedom that fingers exhibit and the self occlusions caused by global hand motion. We propose a divide and conquer approach to estimate both global and local hand motion. By looking into the palm and extra feature points provided by fingers, the hand pose is determined from the palm using an iterative closed point (ICP) algorithm and factorization method. The hand global pose serves as the base frame for the finger motion capturing. Noticing the natural hand motion constraints, we propose an efficient tracking algorithm based on a sequential Monte Carlo technique for tracking finger motion. To enhance the accuracy, pose estimations and finger articulation tracking are performed in an iterative manner. Our experiments show that our approach is accurate and robust for natural hand movements.