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arXiv · 2212.06868

Deep Image Style Transfer from Freeform Text

Tejas Santanam·Mengyang Liu·Jiangyue Yu·Zhaodong Yang
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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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Tejas Santanam, Mengyang Liu, Jiangyue Yu, Zhaodong Yang. 2022-12-13. Deep Image Style Transfer from Freeform Text. https://arxiv.org/abs/2212.06868

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