Face Sketch Synthesis via Semantic-Driven Generative Adversarial Network | |
Qi, Xingqun1; Sun, Muyi2; Wang, Weining2![]() ![]() | |
2021-08-04 | |
会议名称 | International Joint Conference on Biometrics |
会议日期 | 2021.08.04-2021.08.07 |
会议地点 | Shenzhen, China |
摘要 | Face sketch synthesis has made significant progress with the development of deep neural networks in these years. The delicate depiction of sketch portraits facilitates a wide range of applications like digital entertainment and law enforcement. However, accurate and realistic face sketch generation is still a challenging task due to the illumination variations and complex backgrounds in the real scenes. To tackle these challenges, we propose a novel Semantic-Driven Generative Adversarial Network (SDGAN) which embeds global structure-level style injection and local class-level knowledge re-weighting. Specifically, we conduct facial saliency detection on the input face photos to provide overall facial texture structure, which could be used as a global type of prior information. In addition, we exploit face parsing layouts as the semantic-level spatial prior to enforce globally structural style injection in the generator of SDGAN. Furthermore, to enhance the realistic effect of the details, we propose a novel Adaptive Re-weighting Loss (ARLoss) which dedicates to balance the contributions of different semantic classes. Experimentally, our extensive experiments on CUFS and CUFSF datasets show that our proposed algorithm achieves state-of-the-art performance. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51611 |
专题 | 模式识别实验室 |
通讯作者 | Wang, Weining; Shan, Caifeng |
作者单位 | 1.北京邮电大学 2.中国科学院自动化研究所 3.山东科技大学 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Qi, Xingqun,Sun, Muyi,Wang, Weining,et al. Face Sketch Synthesis via Semantic-Driven Generative Adversarial Network[C],2021. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Face_Sketch_Synthesi(5755KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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