Improved Object-Based Style Transfer with Single Deep Network
Harshmohan Kulkarni Om Khare Ninad Barve Sunil Mane
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.