Unconstrained Face Sketch Synthesis via Perception-Adaptive Network and A New Benchmark
2021
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 to deal with
in-the-wild cases. In this paper, we argue that accurately perceiving facial
region and facial components is crucial for unconstrained sketch synthesis. To
this end, we propose a novel Perception-Adaptive Network (PANet), which can
generate high-quality face sketches under unconstrained conditions in an
end-to-end scheme. Specifically, our PANet is composed of i) a Fully
Convolutional Encoder for hierarchical feature extraction, ii) a Face-Adaptive
Perceiving Decoder for extracting potential facial region and handling face
variations, and iii) a Component-Adaptive Perceiving Module for facial
component aware feature representation learning. To facilitate further
researches of unconstrained face sketch synthesis, we introduce a new benchmark
termed WildSketch, which contains 800 pairs of face photo-sketch with large
variations in pose, expression, ethnic origin, background, and illumination.
Extensive experiments demonstrate that the proposed method is capable of
achieving state-of-the-art performance under both constrained and unconstrained
conditions. Our source codes and the WildSketch benchmark are resealed on the
project page http://lingboliu.com/unconstrained_face_sketch.html.
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