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Splice is a method for semantic appearance transfer, as described in Splicing ViT Features for Semantic Appearance Transfer (link to paper).
Given two input images—a source structure image and a target appearance image–our method generates a new image in which
the structure of the source image is preserved, while the visual appearance of the target image is transferred in a semantically aware manner.
That is, objects in the structure image are “painted” with the visual appearance of semantically related objects in the appearance image.
Our method leverages a self-supervised, pre-trained ViT model as an external semantic prior. This allows us to train our generator only on
a single input image pair, without any additional information (e.g., segmentation/correspondences), and without adversarial training. Thus,
our framework can work across a variety of objects and scenes, and can generate high quality results in high resolution (e.g., HD).
Intermediate results will be saved to <dataroot>/out/output.png during optimization. The frequency of saving intermediate results is indicated in the save_epoch_freq flag of the configuration.
Sample Results
Citation
@inproceedings{tumanyan2022splicing,
title={Splicing ViT Features for Semantic Appearance Transfer},
author={Tumanyan, Narek and Bar-Tal, Omer and Bagon, Shai and Dekel, Tali},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={10748--10757},
year={2022}
}
About
Official Pytorch Implementation for "Splicing ViT Features for Semantic Appearance Transfer" presenting "Splice" (CVPR 2022 Oral)