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Enhancing the Realism of Sketch and Painted Portraits With Adaptable Patches
Author(s) -
Lee YinHsuan,
Chang YuKai,
Chang YuLun,
Lin IChen,
Wang YuShuen,
Lin WenChieh
Publication year - 2018
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13261
Subject(s) - computer science , image stitching , image warping , artificial intelligence , painting , computer vision , sketch , computer graphics (images) , portrait , art , visual arts , algorithm
Realizing unrealistic faces is a complicated task that requires a rich imagination and comprehension of facial structures. When face matching, warping or stitching techniques are applied, existing methods are generally incapable of capturing detailed personal characteristics, are disturbed by block boundary artefacts, or require painting‐photo pairs for training. This paper presents a data‐driven framework to enhance the realism of sketch and portrait paintings based only on photo samples. It retrieves the optimal patches of adaptable shapes and numbers according to the content of the input portrait and collected photos. These patches are then seamlessly stitched by chromatic gain and offset compensation and multi‐level blending. Experiments and user evaluations show that the proposed method is able to generate realistic and novel results for a moderately sized photo collection.