Aesthetic Style Transfer through Text-to-image Synthesis and Image-to-image Translation
Megumi Kotera, Ren Togo, Takahiro Ogawa, Miki Haseyama · 2019
This paper presents a style transfer method combining generative adversarial networks and style transfer networks. In the previous style transfer methods, transformation from one image to another has been proposed. On the other hand, our method enables style transfer from a text to an image. This will be helpful when there are no images that represent the desired style. Experimental results show the effectiveness of our method.