近日，AAAI 2019审稿结果已出，实验室一篇论文《SCFont: Structure-guided Chinese Font Generation via Deep Stacked Networks》被接收，AAAI是人工智能领域的顶级会议，将于2019年1月在美国夏威夷召开.
欢迎参与本文实验结果的图灵测试：
http://ask.flexifont.com/testcase/FZZJ-LPYBJW775SCFont
http://ask.flexifont.com/testcase/FZZJ-GBWKJW775SCFont
http://ask.flexifont.com/testcase/FZTLJW775SCFont
http://ask.flexifont.com/testcase/FZYNJW775SCFont
http://ask.flexifont.com/testcase/FZSSBJW775SCFont
http://ask.flexifont.com/testcase/FZJHSXJW775SCFont
论文信息如下：
《SCFont: Structure-guided Chinese Font Generation via Deep Stacked Networks》
Authors:
Yue Jiang, Zhouhui Lian, Yingmin Tang Jianguo Xiao
Abstract:
Automatic generation of Chinese fonts that consist of large numbers of glyphs with complicated structures is now still a challenging and ongoing problem in areas of AI and Computer Graphics (CG). Traditional CG-based methods typically rely heavily on manual interventions, while recently-popularized deep learning-based end-to-end approaches often obtain synthesis results with incorrect structures and/or serious artifacts. To address those problems, this paper proposes a structure-guided Chinese font generation system, SCFont, by using deep stacked networks. The key idea is to integrate the domain knowledge of Chinese characters with deep generative networks to ensure that high-quality glyphs with correct structures can be synthesized. More specifically, we first apply a CNN model to learn how to transfer the writing trajectories with separated strokes in the reference font style into those in the target style. Then, we train another CNN model learning how to recover shape details on the contour for synthesized writing trajectories. Experimental results validate the superiority of the proposed SCFont compared to the state of the art in both visual and quantitative assessments.