Inferring both 3D structure andmotion of nonrigid obje cts from monocular images is an important problem in computational vision. The challenges stem not only fr om the absenc e o f p oint corresp ondences but also from the structur eambiguity. In this paper, a hierarchical method which integrates both local patch analysis and global shap e descriptions is devise d to solve the dual problem of structur e andnonrigid motion recovery by using an elastic geometric model { Extended Sup er quadrics (ESQ). The nonrigid object of interest is segmented into many small areas and local analysis is performed t o r ecover small details for each small area, assuming that each small area is undergoing similar nonrigid motion. Then, a recursive algorithm is propose dto guide and regularize local analysis with glob al information by using an appropriate global ESQ model. This local-global hierarchy enables us to captur eboth local and global deformations accurately and robustly. Experimental results on both simulation and real data are p r esente d to validate and evaluate the e e ctiveness and robustness of the prop osed a p p r oach.
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