Semi-Parametric Image Synthesis

Xiaojuan Qi, Qifeng Chen, Jiaya Jia, Vladlen Koltun; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 8808-8816

Abstract


We present a semi-parametric approach to photographic image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. The nonparametric component is a memory bank of image segments constructed from a training set of images. Given a novel semantic layout at test time, the memory bank is used to retrieve photographic references that are provided as source material to a deep network. The synthesis is performed by a deep network that draws on the provided photographic material. Experiments on multiple semantic segmentation datasets show that the presented approach yields considerably more realistic images than recent purely parametric techniques.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Qi_2018_CVPR,
author = {Qi, Xiaojuan and Chen, Qifeng and Jia, Jiaya and Koltun, Vladlen},
title = {Semi-Parametric Image Synthesis},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}