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Image Stylization

5 papers with code · Computer Vision
Subtask of Style Transfer

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Greatest papers with code

A Closed-form Solution to Photorealistic Image Stylization

ECCV 2018 NVIDIA/FastPhotoStyle

Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic. The proposed method consists of a stylization step and a smoothing step.


Instance Normalization: The Missing Ingredient for Fast Stylization

27 Jul 2016lengstrom/fast-style-transfer

It this paper we revisit the fast stylization method introduced in Ulyanov et. We show how a small change in the stylization architecture results in a significant qualitative improvement in the generated images.


Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis

CVPR 2017 DmitryUlyanov/texture_nets

The recent work of Gatys et al., who characterized the style of an image by the statistics of convolutional neural network filters, ignited a renewed interest in the texture generation and image stylization problems. While their image generation technique uses a slow optimization process, recently several authors have proposed to learn generator neural networks that can produce similar outputs in one quick forward pass.


A Style-Aware Content Loss for Real-time HD Style Transfer

ECCV 2018 CompVis/adaptive-style-transfer

Recently, style transfer has received a lot of attention. These and our qualitative results ranging from small image patches to megapixel stylistic images and videos show that our approach better captures the subtle nature in which a style affects content.


Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization

22 Nov 2018zalandoresearch/famos

Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization. However, learning complicated image representations requires compute-intense models parametrized by a huge number of weights, which in turn requires large datasets to make learning successful.