Deep Image Style Transfer from Freeform Text

• URL: http://arxiv.org/abs/2212.06868v1
• Date: Tue, 13 Dec 2022 19:24:08 GMT
• Title: Deep Image Style Transfer from Freeform Text
• Authors: Tejas Santanam, Mengyang Liu, Jiangyue Yu, Zhaodong Yang
• Abstract summary: 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.
• Score: 4.186575888568896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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