Search Results for author: Jaakko Lehtinen

Found 15 papers, 15 papers with code

Alias-Free Generative Adversarial Networks

6 code implementations23 Jun 2021 Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, Timo Aila

We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner.

Image Generation

Modular Primitives for High-Performance Differentiable Rendering

1 code implementation6 Nov 2020 Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, Timo Aila

We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines.

GANSpace: Discovering Interpretable GAN Controls

2 code implementations NeurIPS 2020 Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, Sylvain Paris

This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging, lighting, and time of day.

Image Generation

Analyzing and Improving the Image Quality of StyleGAN

104 code implementations CVPR 2020 Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila

Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

Image Generation

Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer

1 code implementation NeurIPS 2019 Wenzheng Chen, Jun Gao, Huan Ling, Edward J. Smith, Jaakko Lehtinen, Alec Jacobson, Sanja Fidler

Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering.

E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles

1 code implementation10 Jun 2019 Markus Kettunen, Erik Härkönen, Jaakko Lehtinen

It has been recently shown that the hidden variables of convolutional neural networks make for an efficient perceptual similarity metric that accurately predicts human judgment on relative image similarity assessment.

Image Similarity Search

Few-Shot Unsupervised Image-to-Image Translation

9 code implementations ICCV 2019 Ming-Yu Liu, Xun Huang, Arun Mallya, Tero Karras, Timo Aila, Jaakko Lehtinen, Jan Kautz

Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images.

Translation Unsupervised Image-To-Image Translation

Improved Precision and Recall Metric for Assessing Generative Models

4 code implementations NeurIPS 2019 Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, Timo Aila

The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research.

Image Generation

High-Quality Self-Supervised Deep Image Denoising

2 code implementations NeurIPS 2019 Samuli Laine, Tero Karras, Jaakko Lehtinen, Timo Aila

We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images.

Image Denoising

Differentiable Monte Carlo Ray Tracing through Edge Sampling

1 code implementation SIGGRAPH 2018 Tzu-Mao Li, Miika Aittala, Frédo Durand, Jaakko Lehtinen

We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters.

Noise2Noise: Learning Image Restoration without Clean Data

18 code implementations ICML 2018 Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, Timo Aila

We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption.

Image Restoration Salt-And-Pepper Noise Removal

Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks

1 code implementation21 Sep 2016 Samuli Laine, Tero Karras, Timo Aila, Antti Herva, Shunsuke Saito, Ronald Yu, Hao Li, Jaakko Lehtinen

We present a real-time deep learning framework for video-based facial performance capture -- the dense 3D tracking of an actor's face given a monocular video.

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