Unconstrained face sketch synthesis via perception-adaptive network and a new benchmark

نویسندگان

چکیده

Face sketch generation has attracted much attention in the field of visual computing. However, existing methods either are limited to constrained conditions or heavily rely on various preprocessing steps deal with in-the-wild cases. In this paper, we argue that accurately perceiving facial region and components is crucial for unconstrained synthesis. To end, propose a novel Perception-Adaptive Network (PANet), which can generate high-quality face sketches under an end-to-end scheme. Specifically, our PANet composed of: i) Fully Convolutional Encoder hierarchical feature extraction, ii) Face-Adaptive Perceiving Decoder extracting potential handling variations, iii) Component-Adaptive Module component aware representation learning. facilitate further researches synthesis, introduce new benchmark termed WildSketch, contains 800 pairs photo-sketch large variations pose, expression, ethnic origin, background, illumination. Extensive experiments demonstrate proposed method capable achieving state-of-the-art performance both conditions. Our source codes WildSketch resealed project page http://lingboliu.com/unconstrained_face_sketch.html.

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ژورنال

عنوان ژورنال: Neurocomputing

سال: 2022

ISSN: ['0925-2312', '1872-8286']

DOI: https://doi.org/10.1016/j.neucom.2022.04.077