Abstract　In this paper , we present a new approach for automatically generating a life2like port rait f rom a f rontal face image. We learn the port raiture f rom a set of real artwork examples. Different f rom previous texture synthesis and image synthesis works that assumed modeling is homogeneous , Inhomo2 geneous Markov Random Field Model is employed as the statistical model , and a non2 paramet ric sam2 pling scheme is used to capture the complex statistical characteristics of face image and corresponding artist drawing in this paper . In our st rategy , only those pixels corresponding to a port rait point are sampled. Such a st rategy is crucial for maintaining facial st ructure and guaranteeing coherence of por2 t rait lines. Experimental result s demonst rate the effectiveness and life 2likeness of our approach.

基于样本学习的肖像画自动生成算法.pdf

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