@inproceedings{tan2021diverse,
author = {Tan, Zhentao and Chai, Menglei and Chen, Dongdong and Liao, Jing and Chu, Qi and Liu, Bin and Hua, Gang and Yu, Nenghai},
title = {Diverse Semantic Image Synthesis via Probability Distribution Modeling},
booktitle = {CVPR 2021},
year = {2021},
month = {June},
abstract = {Semantic image synthesis, translating semantic layouts to photo-realistic images, is a one-to-many mapping problem. Though impressive progress has been recently made, diverse semantic synthesis that can efficiently produce semantic-level multimodal results, still remains a challenge. In this paper, we propose a novel diverse semantic image synthesis framework from the perspective of semantic class distributions, which naturally supports diverse generation at semantic or even instance level. We achieve this by modeling class-level conditional modulation parameters as continuous probability distributions instead of discrete values, and sampling per-instance modulation parameters through instance-adaptive stochastic sampling that is consistent across the network. Moreover, we propose prior noise remapping, through linear perturbation parameters encoded from paired references, to facilitate supervised training and exemplar-based instance style control at test time. Extensive experiments on multiple datasets show that our method can achieve superior diversity and comparable quality compared to state-of-the-art methods. Code will be available on GitHub.},
url = {http://approjects.co.za/?big=en-us/research/publication/diverse-semantic-image-synthesis-via-probability-distribution-modeling/},
}