Papers › Bridging Unpaired Facial Photos And Sketches By Line-drawings
Bridging Unpaired Facial Photos And Sketches By Line-drawings
Meimei Shang, Fei Gao, Xiang Li, Jingjie Zhu, Lingna Dai
In this paper, we propose a novel method to learn face sketch synthesis models by using unpaired data. Our main idea is bridging the photo domain 𝒳 and the sketch domain Y by using the line-drawing domain 𝒵. Specially, we map both photos and sketches to line-drawings by using a neural style transfer method, i.e. F: 𝒳/𝒴 ↦𝒵. Consequently, we obtain \textit{pseudo paired data} (𝒵, 𝒴), and can learn the mapping G:𝒵 ↦𝒴 in a supervised learning manner. In the inference stage, given a facial photo, we can first transfer it to a line-drawing and then to a sketch by G ∘F. Additionally, we propose a novel stroke loss for generating different types of strokes. Our method, termed sRender, accords well with human artists' rendering process. Experimental results demonstrate that sRender can generate multi-style sketches, and significantly outperforms existing unpaired image-to-image translation methods.
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