  We present a method for semantically transferring the visual appearance of
one natural image to another. Specifically, our goal is to generate an image in
which objects in a source structure image are "painted" with the visual
appearance of their semantically related objects in a target appearance image.
To integrate semantic information into our framework, our key idea is to
leverage a pre-trained and fixed Vision Transformer (ViT) model. Specifically,
we derive novel disentangled representations of structure and appearance
extracted from deep ViT features. We then establish an objective function that
splices the desired structure and appearance representations, interweaving them
together in the space of ViT features. Based on our objective function, we
propose two frameworks of semantic appearance transfer -- "Splice", which works
by training a generator on a single and arbitrary pair of structure-appearance
images, and "SpliceNet", a feed-forward real-time appearance transfer model
trained on a dataset of images from a specific domain. Our frameworks do not
involve adversarial training, nor do they require any additional input
information such as semantic segmentation or correspondences. We demonstrate
high-resolution results on a variety of in-the-wild image pairs, under
significant variations in the number of objects, pose, and appearance. Code and
supplementary material are available in our project page: splice-vit.github.io.
