Towards the High-quality Anime Characters Generation with Generative Adversarial Networks

By Yanghua Jin, Jiakai Zhang, Minjun Li, Yingtao Tian, Huachun Zhu. 2017

Subfields: Generative Models

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Generative Models

1. Yujiu Yang
2. Weihao Xia
3. Jing-Hao Xue
4. Yujun Shen
5. Bolei Zhou
6. Daniel Cohen-Or
7. Yulun Zhang
8. Ming-Hsuan Yang

Citations in the corpus

• GAN Inversion: A Survey (2021, background): “In addition, some GAN inversion studies also use other datasets [50], [62], [77], [88], [89], [90] in their experiments, such as DeepFashion [91], [92], [93], AnimeFaces [79], and StreetScapes [94].”
• Closed-Form Factorization of Latent Semantics in GANs (2020, background): “They are trained on diverse datasets, including human faces (CelebA-HQ [15] and FF-HQ [16]), anime faces [14], scenes and objects (LSUN [26]), streetscapes [19], and ImageNet [5].”
• Unsupervised Discovery of Interpretable Directions in the GAN Latent Space (2020, unknown): “AnimeFaces dataset (Jin et al., 2017), containing 64 × 64 images.”
• Copyright in generative deep learning (2021, unknown): “With respect to GDL, it typically concerns the use of generated images (Tilson and Gelowitz, 2019), characters (Jin et al., 2017), or soundtracks 10 inside games; in these cases, all the conclusions drawn until now are still valid, and no additional considerations are required.”