# Semantic Image Synthesis with Spatially-Adaptive Normalization

**Authors:** Taesung Park, Ming-Yu Liu, Ting-Chun Wang, Jun-Yan Zhu  
**arXiv:** [1903.07291](https://arxiv.org/abs/1903.07291)  
**Date:** 2019-03-18  
**Summary page:** https://www.summarizepaper.com/en/paper/1903.07291v1/

## Key points

- Taesung Park, Ming-Yu Liu, Ting-Chun Wang and Jun-Yan Zhu propose a novel layer for synthesizing photorealistic images from an input semantic layout.
- Previous methods have fed the semantic layout directly into deep networks, but this approach is suboptimal as normalization layers tend to "wash away" semantic information.
- The authors introduce SPADE (Spatially-Adaptive Normalization), which modulates activations in normalization layers through a spatially-adaptive learned transformation.
- SPADE improves visual fidelity and alignment with input layouts compared to existing approaches while also allowing for user control over both semantic and style when synthesizing images.
- The authors demonstrate the effectiveness of their approach on several challenging datasets.
- The code will be available on GitHub at https://github.com/NVlabs/SPADE.
- This work was accepted as an oral paper at CVPR 2019.

## Summary

In their paper "Semantic Image Synthesis with Spatially-Adaptive Normalization," Taesung Park, Ming-Yu Liu, Ting-Chun Wang and Jun-Yan Zhu propose a novel layer for synthesizing photorealistic images from an input semantic layout. Previous methods have fed the semantic layout directly into deep networks, but this approach is suboptimal as normalization layers tend to "wash away" semantic information. To address this issue, the authors introduce SPADE (Spatially-Adaptive Normalization), which modulates activations in normalization layers through a spatially-adaptive learned transformation. This improves visual fidelity and alignment with input layouts compared to existing approaches while also allowing for user control over both semantic and style when synthesizing images. The authors demonstrate the effectiveness of their approach on several challenging datasets and will make the code available on GitHub at https://github.com/NVlabs/SPADE. This work was accepted as an oral paper at CVPR 2019.