Existing neural style transfer methods require reference style images to
transfer texture information of style images to content images. However, in
many practical situations, users may not have reference style images but still
be interested in transferring styles by just imagining them. In order to deal
with such applications, we propose a new framework that enables a style
transfer `without' a style image, but only with a text description of the
desired style. Using the pre-trained text-image embedding model of CLIP, we
demonstrate the modulation of the style of content images only with a single
text condition. Specifically, we propose a patch-wise text-image matching loss
with multiview augmentations for realistic texture transfer. Extensive
experimental results confirmed the successful image style transfer with
realistic textures that reflect semantic query texts.

提出了一种全新的神经风格迁移框架，使用户能够使用文本描述而非参考图像来实现图像的风格迁移，在实验中证实了通过使用多视图扩充的补丁式文本 - 图像匹配 Loss 来实现对内容图像的样式转移，从而实现了具有语义相似性的现实纹理风格转移。