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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. Expand
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. Expand
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. Expand
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. Expand
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. Expand
Pruning Convolutional Neural Networks for Resource Efficient Inference
TLDR
It is shown that pruning can lead to more than 10x theoretical (5x practical) reduction in adapted 3D-convolutional filters with a small drop in accuracy in a recurrent gesture classifier. Expand
Exposure Fusion: A Simple and Practical Alternative to High Dynamic Range Photography
TLDR
This work proposes a technique for fusing a bracketed exposure sequence into a high quality image, without converting to High dynamic range (HDR) first, which avoids camera response curve calibration and is computationally efficient. Expand
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
TLDR
This work proposes an end-to-end convolutional neural network for variable-length multi-frame video interpolation, where the motion interpretation and occlusion reasoning are jointly modeled. Expand
Precomputed radiance transfer for real-time rendering in dynamic, low-frequency lighting environments
TLDR
A new, real-time method for rendering diffuse and glossy objects in low-frequency lighting environments that captures soft shadows, interreflections, and caustics and introduces functions for radiance transfer from a dynamic lighting environment through a preprocessed object to neighboring points in space. Expand
Exposure Fusion
TLDR
This work proposes a technique for fusing a bracketed exposure sequence into a high quality image, without converting to HDR first, which avoids camera response curve calibration and is computationally efficient. Expand
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