基于生成对抗网络的异质人脸图像合成:进展与挑战

Heterogeneous face synthesis via generative adversarial networks: progresses and challenges

异质人脸图像合成旨在生成逼真、可识别的多种视觉形态的人脸肖像,包括画像、漫画等多种模态.异质人脸图像合成在公共安全和数字娱乐领域具有广泛的应用前景和重要的研究价值,已成为当前研究热点之一.近年来,随着生成对抗网络的发展以及其在多种图像风格转换任务中的成功,研究人员利用生成对抗网络构建了多种异质人脸图像合成的新方法.本文简要回顾了异质人脸图像合成的发展历史,并从异质人脸图像合成的应用进展、模型结构、性能评估、数据集和定性分析等方面综述了该领域最新的关键技术的发展情况,展望了异质人脸图像合成面临的挑战以及其关键技术的发展趋势.

Heterogeneous face synthesis aims at generating visually realistic and identity-preserving portraits of different modality,such as sketches,caricatures,etc.Heterogeneous face synthesis is of great significance for both public security and digital entertainment,and has attracted numerous attention.Recently,inspired by the dramatic progress in generative adversarial networks(GANs)and their great successes in image-to-image translation tasks,researchers have proposed a number of new heterogeneous face synthesis methods based on GANs.In this paper,we briefly introduce the development of heterogeneous face synthesis,and detailed recent progresses in terms of developments of applications,architectures of GANs,performance evaluation approaches,datasets,and qualitative analysis.Finally,we summarize the challenges and some prospects of heterogeneous face synthesis.