J
Jan Kautz
Researcher at Nvidia
Publications - 445
Citations - 42457
Jan Kautz is an academic researcher from Nvidia. The author has contributed to research in topics: Rendering (computer graphics) & Computer science. The author has an hindex of 79, co-authored 416 publications receiving 30253 citations. Previous affiliations of Jan Kautz include Carnegie Mellon University & Beijing Institute of Technology.
Papers
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Proceedings ArticleDOI
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
TL;DR: In this paper, a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs) is presented.
Proceedings ArticleDOI
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
TL;DR: PWC-Net as discussed by the authors uses the current optical flow estimate to warp the CNN features of the second image, which is processed by a CNN to estimate the optical flow, and achieves state-of-the-art performance on the MPI Sintel final pass and KITTI 2015 benchmarks.
Book ChapterDOI
Multimodal Unsupervised Image-to-Image Translation
TL;DR: In this article, the authors propose a multimodal unsupervised image-to-image (MUNIT) framework, where the image representation can be decomposed into a content code that is domain-invariant and a style code that captures domain-specific properties.
Journal ArticleDOI
Loss Functions for Image Restoration With Neural Networks
TL;DR: It is shown that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged, and a novel, differentiable error function is proposed.
Proceedings Article
Unsupervised Image-to-Image Translation Networks
TL;DR: 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.