Spatially-Adaptive Pixelwise Networks for Fast Image Translation
CVPR 2021
Tamar Rott Shaham
Michaël Gharbi
Richard Zhang
Eli Shechtman
Tomer Michaeli

Our novel model, designed with A Spatially Adaptive Pixelwise Network (ASAPNet) enables generating high-resolution images at significantly lower runtimes than existing methods, while maintaining high visual quality. Particularly, as seen in the plot our model is 2-18x faster than baselines, depending on resolution.


We introduce a new generator architecture, aimed at fast and efficient high-resolution image-to-image translation. We design the generator to be an extremely lightweight function of the full-resolution image. In fact, we use pixel-wise networks; that is, each pixel is processed independently of others, through a composition of simple affine transformations and nonlinearities. We take three important steps to equip such a seemingly simple function with adequate expressivity. First, the parameters of the pixel-wise networks are spatially varying, so they can represent a broader function class than simple 1x1 convolutions. Second, these parameters are predicted by a fast convolutional network that processes an aggressively low-resolution representation of the input. Third, we augment the input image by concatenating a sinusoidal encoding of spatial coordinates, which provides an effective inductive bias for generating realistic novel high-frequency image content. As a result, our model is up to 18x faster than state-of-the-art baselines. We achieve this speedup while generating comparable visual quality across different image resolutions and translation domains.

5-Minutes Video



Our model first processes the input at very low-resolution xl, to produce a tensor of weights and biases φp. These are upsampled back to full-resolution, where they parameterize pixelwise, spatially-varying MLPs fp that compute the final output y from the high-resolution input x.


T. Rott Shaham, M. Gharbi, R. Zhang,
E. Shechtman, T. Michaeli
Spatially-Adaptive Pixelwise Networks for Fast Image Translation
CVPR 2021
[ArXiv] [CVF] [Supplementals] [Bibtex]


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Taesung Park, Ming-Yu Liu, Ting-Chun Wang and Jun-Yan Zhu, Semantic Image Synthesis with Spatially-Adaptive Normalization, CVPR 2019

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Xiaojuan Qi, Qifeng Chen, Jiaya Jia, and Vladlen Koltun, Semi-parametric Image Synthesis, CVPR 2018

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