Semantic Image Synthesis With Spatially-Adaptive Normalization
TL
TL;DR
S spatially-adaptive normalization is proposed, a simple but effective layer for synthesizing photorealistic images given an input semantic layout that allows users to easily control the style and content of image synthesis results as well as create multi-modal results.
Abstract
We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as input to the network, forcing the network to memorize the information throughout all the layers. Instead, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned affine transformation. Experiments on several challenging datasets demonstrate the superiority of our method compared to existing approaches, regarding both visual fidelity and alignment with input layouts. Finally, our model allows users to easily control the style and content of image synthesis results as well as create multi-modal results. Code is available upon publication.
References66 items
1
A Style-Based Generator Architecture for Generative Adversarial Networks
2
Image Generation From Layout
3
Large Scale GAN Training for High Fidelity Natural Image Synthesis
4
On Self Modulation for Generative Adversarial Networks
5