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High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
TLDR
A new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs) is presented, which significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.
Multimodal Unsupervised Image-to-Image Translation
TLDR
A Multimodal Unsupervised Image-to-image Translation (MUNIT) framework that assumes that the image representation can be decomposed into a content code that is domain-invariant, and a style code that captures domain-specific properties.
Unsupervised Image-to-Image Translation Networks
TLDR
This work makes a shared-latent space assumption and proposes an unsupervised image-to-image translation framework based on Coupled GANs that achieves state-of-the-art performance on benchmark datasets.
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
TLDR
PWC-Net has been designed according to simple and well-established principles: pyramidal processing, warping, and the use of a cost volume, and outperforms all published optical flow methods on the MPI Sintel final pass and KITTI 2015 benchmarks.
Semantic Image Synthesis With Spatially-Adaptive Normalization
TLDR
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.
Entropy rate superpixel segmentation
TLDR
An efficient greedy algorithm for superpixel segmentation is developed by exploiting submodular and mono-tonic properties of the objective function and proving an approximation bound of ½ for the optimality of the solution.
MoCoGAN: Decomposing Motion and Content for Video Generation
TLDR
This work introduces a novel adversarial learning scheme utilizing both image and video discriminators and shows that MoCoGAN allows one to generate videos with same content but different motion as well as videos with different content and same motion.
Coupled Generative Adversarial Networks
TLDR
This work proposes coupled generative adversarial network (CoGAN), which can learn a joint distribution without any tuple of corresponding images, and applies it to several joint distribution learning tasks, and demonstrates its applications to domain adaptation and image transformation.
Video-to-Video Synthesis
TLDR
This paper proposes a novel video-to-video synthesis approach under the generative adversarial learning framework, capable of synthesizing 2K resolution videos of street scenes up to 30 seconds long, which significantly advances the state-of-the-art of video synthesis.
A Closed-form Solution to Photorealistic Image Stylization
TLDR
The results show that the proposed method generates photorealistic stylization outputs that are more preferred by human subjects as compared to those by the competing methods while running much faster.
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