Abstract
This research paper proposes a novel methodology for image-to-image style transfer on objects utilizing a single deep convolutional neural network. The proposed approach leverages the You Only Look Once version 8 (YOLOv8) segmentation model and the backbone neural network of YOLOv8 for style transfer. The primary objective is to enhance the visual appeal of objects in images by seamlessly transferring artistic styles while preserving the original object characteristics. The proposed approach's novelty lies in combining segmentation and style transfer in a single deep convolutional neural network. This approach omits the need for multiple stages or models, thus resulting in simpler training and deployment of the model for practical applications. The results of this approach are shown on two content images by applying different style images. The paper also demonstrates the ability to apply style transfer on multiple objects in the same image.
Abstract (translated)
本文提出了一种利用单个深度卷积神经网络进行图像到图像风格迁移的新方法来处理物体。所提出的方法利用了You Only Look Once版本8（YOLOv8）分割模型和YOLOv8的骨干网络来进行风格迁移。主要目标是通过无缝转移艺术风格来增强图像中物体的视觉吸引力，同时保留原始物体的特征。该方法的创新之处在于将分割和风格迁移结合在一个单深的卷积神经网络中。这种方法省略了多个阶段或模型，因此简化了模型的训练和部署，为实际应用提供了更简单的模型。通过在两个内容图像上应用不同的风格图像，展示了这种方法的效果。本文还展示了在同一图像中应用风格迁移处理多个物体的能力。
URL
https://arxiv.org/abs/2404.09461
