Search Results for author: Tero Karras

Found 24 papers, 19 papers with code

Analyzing and Improving the Training Dynamics of Diffusion Models

2 code implementations5 Dec 2023 Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, Samuli Laine

Diffusion models currently dominate the field of data-driven image synthesis with their unparalleled scaling to large datasets.

Image Generation Philosophy

StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis

1 code implementation23 Jan 2023 Axel Sauer, Tero Karras, Samuli Laine, Andreas Geiger, Timo Aila

Text-to-image synthesis has recently seen significant progress thanks to large pretrained language models, large-scale training data, and the introduction of scalable model families such as diffusion and autoregressive models.

Simulator-Based Self-Supervision for Learned 3D Tomography Reconstruction

no code implementations14 Dec 2022 Onni Kosomaa, Samuli Laine, Tero Karras, Miika Aittala, Jaakko Lehtinen

We propose a deep learning method for 3D volumetric reconstruction in low-dose helical cone-beam computed tomography.

3D Volumetric Reconstruction

eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

2 code implementations2 Nov 2022 Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Qinsheng Zhang, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, Ming-Yu Liu

Therefore, in contrast to existing works, we propose to train an ensemble of text-to-image diffusion models specialized for different synthesis stages.

Generating Long Videos of Dynamic Scenes

1 code implementation7 Jun 2022 Tim Brooks, Janne Hellsten, Miika Aittala, Ting-Chun Wang, Timo Aila, Jaakko Lehtinen, Ming-Yu Liu, Alexei A. Efros, Tero Karras

Existing video generation methods often fail to produce new content as a function of time while maintaining consistencies expected in real environments, such as plausible dynamics and object persistence.

MORPH Video Generation

Elucidating the Design Space of Diffusion-Based Generative Models

10 code implementations1 Jun 2022 Tero Karras, Miika Aittala, Timo Aila, Samuli Laine

We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices.

Image Generation

The Role of ImageNet Classes in Fréchet Inception Distance

1 code implementation11 Mar 2022 Tuomas Kynkäänniemi, Tero Karras, Miika Aittala, Timo Aila, Jaakko Lehtinen

Fr\'echet Inception Distance (FID) is the primary metric for ranking models in data-driven generative modeling.

Alias-Free Generative Adversarial Networks

7 code implementations NeurIPS 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.

Attribute Inverse Rendering +1

Analyzing and Improving the Image Quality of StyleGAN

121 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.

Attribute Conditional Image Generation

Few-Shot Unsupervised Image-to-Image Translation

10 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.

Test Translation +1

Improved Precision and Recall Metric for Assessing Generative Models

8 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 Vocal Bursts Intensity Prediction

A Style-Based Generator Architecture for Generative Adversarial Networks

83 code implementations CVPR 2019 Tero Karras, Samuli Laine, Timo Aila

We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.

Disentanglement Image Generation

Noise2Noise: Learning Image Restoration without Clean Data

21 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.

BIG-bench Machine Learning Image Restoration +1

Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion

no code implementations SIGGRAPH 2017 Tero Karras, Timo Aila, Samuli Laine, Antti Herva, Jaakko Lehtinen

Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone.

Face Model

Pruning Convolutional Neural Networks for Resource Efficient Inference

9 code implementations19 Nov 2016 Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, Jan Kautz

We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters.

Transfer Learning

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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