Deep Image Style Transfer from Freeform Text
This addresses the challenge of making style transfer more accessible and user-friendly for creative applications, though it appears incremental by combining existing models.
The paper tackles the problem of generating stylized images from freeform text descriptions by creating a pipeline that uses a language model to produce style images from text, which are then fed into a style transfer model. The result is output images with similar losses and improved quality compared to baseline style transfer methods.
This paper creates a novel method of deep neural style transfer by generating style images from freeform user text input. The language model and style transfer model form a seamless pipeline that can create output images with similar losses and improved quality when compared to baseline style transfer methods. The language model returns a closely matching image given a style text and description input, which is then passed to the style transfer model with an input content image to create a final output. A proof-of-concept tool is also developed to integrate the models and demonstrate the effectiveness of deep image style transfer from freeform text.