论文标题
艺术任意风格转移
Artistic Arbitrary Style Transfer
论文作者
论文摘要
任意样式转移是一种用于从两个图像中产生新图像的技术：内容图像和样式图像。新生产的图像是看不见的，是由算法本身生成的。平衡结构和样式组件一直是其他最先进的算法试图解决的主要挑战。尽管做出了所有努力，但要运用最初在内容图像结构之上创建的艺术风格的同时，在保持一致性的同时，仍然是一个重大挑战。在这项工作中，我们通过使用卷积神经网络的深度学习方法解决了这些问题。我们的实现将首先使用从内容图像中的预训练的检测2模型从背景中提取前景，然后应用壁室中使用的任意样式转移技术。一旦拥有两个样式的图像，我们将在样式转移过程后为完整的最终作品缝制两个图像。
Arbitrary Style Transfer is a technique used to produce a new image from two images: a content image, and a style image. The newly produced image is unseen and is generated from the algorithm itself. Balancing the structure and style components has been the major challenge that other state-of-the-art algorithms have tried to solve. Despite all the efforts, it's still a major challenge to apply the artistic style that was originally created on top of the structure of the content image while maintaining consistency. In this work, we solved these problems by using a Deep Learning approach using Convolutional Neural Networks. Our implementation will first extract foreground from the background using the pre-trained Detectron 2 model from the content image, and then apply the Arbitrary Style Transfer technique that is used in SANet. Once we have the two styled images, we will stitch the two chunks of images after the process of style transfer for the complete end piece.
