论文标题
自由形式文本的深层图像样式转移
Deep Image Style Transfer from Freeform Text
论文作者
论文摘要
本文通过从Freeform用户文本输入中生成样式图像来创建一种新颖的神经风格转移方法。语言模型和样式传输模型形成了无缝管道，与基线样式转移方法相比，可以创建具有相似损失和提高质量的输出图像。语言模型给定样式的文本和描述输入返回一个紧密匹配的图像，然后将其传递给带有输入内容图像的样式传输模型以创建最终输出。还开发了一种概念验证工具来整合模型并证明了自由图书中深图像样式转移的有效性。
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.
