Converting directly real-world images into high-quality anime styles using generative adversarial networks is one of the research hotspots in computer vision. The current popular AnimeGAN and WhiteBox anime generative adversarial networks have distortion of image features problem and loss of details on lines and textures problem, respectively. To address these problems, we introduce a new AnimationGAN based on a linear bottleneck residual network and a hybrid attention mechanism. The proposed AnimationGAN can prevent severe detail loss and distortion of image features in AnimeGAN-transferred images. In addition, we adopt optimized normalizations to improve the accuracy and learning rate of the model. The experimental results show that compared with AnimeGAN and Whitebox, the generated animation image significantly improved line texture details and image feature retention, and the network training speed is also faster.

Converting directly real-world images into high-quality anime styles using generative adversarial networks is one of the research hotspots in computer vision. The current popular AnimeGAN and WhiteBox anime generative adversarial networks have distortion of image features problem and loss of details on lines and textures problem, respectively. To address these problems, we introduce a new AnimationGAN based on a linear bottleneck residual network and a hybrid attention mechanism. The proposed AnimationGAN can prevent severe detail loss and distortion of image features in AnimeGAN-transferred images. In addition, we adopt optimized normalizations to improve the accuracy and learning rate of the model. The experimental results show that compared with AnimeGAN and Whitebox, the generated animation image significantly improved line texture details and image feature retention, and the network training speed is also faster.