Abstract
Cartoons are an important art style, which not only has a unique drawing effect but also reflects the character itself, which is gradually loved by people. With the development of image processing technology, people's research on image research is no longer limited to image recognition, target detection, and tracking, but also images In this paper, we use deep learning based image processing to generate cartoon caricatures of human faces. Therefore, this paper investigates the use of deep learning-based methods to learn face features and convert image styles while preserving the original content features, to automatically generate natural cartoon avatars. In this paper, we study a face cartoon generation method based on content invariance. In the task of image style conversion, the content is fused with different style features based on the invariance of content information, to achieve the style conversion.
Publisher
Academy and Industry Research Collaboration Center (AIRCC)
Reference16 articles.
1. [1] Leon A. Gatys, Alexander S. Ecker, Matthias Bethge; Image Style Transfer Using Convolutional Neural Networks; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2414-2423
2. [2] Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros; Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2223-2232
3. [3] Ruizheng Wu, Xiaodong Gu, Xin Tao, Xiaoyong Shen, Yu-Wing Tai, J iaya Jia; Landmark Assisted CycleGAN for Cartoon Face Generation
4. [4] Kaidi Cao, Jing Liao, Lu Yuan; CariGANs:Unpaired Photo-to-Caricature Translation; ACM Transactions on Graphics, Vol. 37, No. 6, Article 244. Publication date: November 2018
5. [5] Chen Yang, Lai YuKun, Liu YongJin. CartoonGAN: Generative Adversarial Networks for Photo Cartoonization [C], IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE Computer Society,2018:9465 - 9474