3-D recovery of a non-rigid object from a single camera view by piecewise recovery and synthesis

This paper proposes a novel technique for 3-D recovery of a non-rigid object, such as a human in motion, from a single camera view. To achieve the 3-D recovery, the proposed technique performs segmentation of an object under deformation into respective parts which are regarded as rigid. For high accuracy segmentation, multi-stage learning and local subspace affinity are employed in this stage. Each part recovers its 3-D shape by applying the factorization method to it. This is the initial solution. The shape of each part is then refined by applying a quadratic model to the initial solution. The entire 3-D recovery of the object is finally performed using the common points among the segmented parts. The experiments employing a synthetic non-rigid object and real human motion data show effectiveness of the proposed technique.