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In this paper, we present a two-level generative model for representing the images and surface depth maps of drapery and clothes. The upper level consists of a number of folds which will generate the high contrast (ridge) areas with a dictionary of shading primitives (for 2D images) and fold primitives (for 3D depth maps). These primitives are represented in parametric forms and are learned in a supervised learning phase using 3D surfaces of clothes acquired through photometric stereo. The lower level consists of the remaining flat areas which fill between the folds with a smoothness prior (Markov random field). We show that the classical ill-posed problem—shape from shading (SFS) can be much improved by this two-level model for its reduced dimensionality and incorporation of middle-level visual knowledge, i.e., the dictionary of primitives. Given an input image, we first infer the folds and compute a sketch graph using a sketch pursuit algorithm as in the primal sketch [10], [11]. The 3D folds are estimated by parameter fitting using the fold dictionary and they form the "skeleton” of the drapery/cloth surfaces. Then, the lower level is computed by conventional SFS method using the fold areas as boundary conditions. The two levels interact at the final stage by optimizing a joint Bayesian posterior probability on the depth map. We show a number of experiments which demonstrate more robust results in comparison with state-of-the-art work. In a broader scope, our representation can be viewed as a two-level inhomogeneous MRF model which is applicable to general shape-from-X problems. Our study is an attempt to revisit Marr's idea [23] of computing the 2{\frac{1}{2}}{\rm D} sketch from primal sketch. In a companion paper [2], we study shape from stereo based on a similar two-level generative sketch representation.
Shape from shading, generate model, shading primitive, sketch graph.
Feng Han, Song-Chun Zhu, "A Two-Level Generative Model for Cloth Representation and Shape from Shading", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. , pp. 1230-1243, July 2007, doi:10.1109/TPAMI.2007.1040
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