Fine-Grained Control of Artistic Styles in Image Generation
This addresses the need for more nuanced artistic style manipulation in generative models, which is incremental as it builds on existing GAN frameworks like StyleGAN.
The paper tackles the problem of achieving fine-grained control over artistic styles in image generation by embedding artwork examples into a continuous style space, resulting in precise style control and improved image quality as measured by FID scores.
Recent advances in generative models and adversarial training have enabled artificially generating artworks in various artistic styles. It is highly desirable to gain more control over the generated style in practice. However, artistic styles are unlike object categories -- there are a continuous spectrum of styles distinguished by subtle differences. Few works have been explored to capture the continuous spectrum of styles and apply it to a style generation task. In this paper, we propose to achieve this by embedding original artwork examples into a continuous style space. The style vectors are fed to the generator and discriminator to achieve fine-grained control. Our method can be used with common generative adversarial networks (such as StyleGAN). Experiments show that our method not only precisely controls the fine-grained artistic style but also improves image quality over vanilla StyleGAN as measured by FID.