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.

翻译：本论文提出了一种新颖的图像到图像对象风格迁移方法，该方法利用单深度卷积神经网络实现。所提出的方法采用YOLOv8（You Only Look Once version 8）分割模型及其骨干网络进行风格迁移。主要目标是在保留原始对象特征的同时，通过无缝迁移艺术风格来增强图像中对象的视觉吸引力。该方法的新颖之处在于将分割与风格迁移整合至单一深度卷积神经网络中，从而省去多阶段或多模型的需求，简化了模型在实际应用中的训练与部署。通过将不同风格图像应用于两幅内容图像，展示了该方法的成效。论文同时证明了在同一图像中对多个对象进行风格迁移的能力。