no code implementations • ECCV 2020 • Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, Aaron Hertzmann
We present a method for projecting an input image into the space of a class-conditional generative neural network.
1 code implementation • 24 Nov 2023 • Manuel Ladron De Guevara, Matthew Fisher, Aaron Hertzmann
We introduce a novel image-to-painting method that facilitates the creation of large-scale, high-fidelity paintings with human-like quality and stylistic variation.
no code implementations • 7 Jun 2023 • Ziv Epstein, Aaron Hertzmann, Laura Herman, Robert Mahari, Morgan R. Frank, Matthew Groh, Hope Schroeder, Amy Smith, Memo Akten, Jessica Fjeld, Hany Farid, Neil Leach, Alex Pentland, Olga Russakovsky
A new class of tools, colloquially called generative AI, can produce high-quality artistic media for visual arts, concept art, music, fiction, literature, video, and animation.
no code implementations • 23 Jun 2022 • Zoya Bylinskii, Laura Herman, Aaron Hertzmann, Stefanie Hutka, Yile Zhang
We discuss foundational user research methods (e. g., needfinding) that are presently underutilized in computer vision and graphics research, but can provide valuable project direction.
no code implementations • 3 May 2022 • Aaron Hertzmann
This paper proposes a framework for computational modeling of artistic painting algorithms, inspired by human creative practices.
1 code implementation • ICCV 2021 • Difan Liu, Matthew Fisher, Aaron Hertzmann, Evangelos Kalogerakis
We show that, in contrast to previous image-based methods, the use of a geometric representation of 3D shape and 2D strokes allows the model to transfer important aspects of shape and texture style while preserving contours.
no code implementations • ICCV 2021 • Ruben Villegas, Duygu Ceylan, Aaron Hertzmann, Jimei Yang, Jun Saito
Self-contacts, such as when hands touch each other or the torso or the head, are important attributes of human body language and dynamics, yet existing methods do not model or preserve these contacts.
1 code implementation • ICCV 2021 • Davis Rempe, Tolga Birdal, Aaron Hertzmann, Jimei Yang, Srinath Sridhar, Leonidas J. Guibas
We introduce HuMoR: a 3D Human Motion Model for Robust Estimation of temporal pose and shape.
no code implementations • 22 Jan 2021 • Aaron Hertzmann
It has often been conjectured that the effectiveness of line drawings can be explained by the similarity of edge images to line drawings.
no code implementations • 21 Aug 2020 • Xi Wang, Zoya Bylinskii, Aaron Hertzmann, Robert Pepperell
It has long been hypothesized that perceptual ambiguities play an important role in aesthetic experience: a work with some ambiguity engages a viewer more than one that does not.
no code implementations • 7 Aug 2020 • Camilo Fosco, Vincent Casser, Amish Kumar Bedi, Peter O'Donovan, Aaron Hertzmann, Zoya Bylinskii
This paper introduces a Unified Model of Saliency and Importance (UMSI), which learns to predict visual importance in input graphic designs, and saliency in natural images, along with a new dataset and applications.
1 code implementation • ECCV 2020 • Davis Rempe, Leonidas J. Guibas, Aaron Hertzmann, Bryan Russell, Ruben Villegas, Jimei Yang
Existing deep models predict 2D and 3D kinematic poses from video that are approximately accurate, but contain visible errors that violate physical constraints, such as feet penetrating the ground and bodies leaning at extreme angles.
2 code implementations • 4 May 2020 • Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, Aaron Hertzmann
We present a method for projecting an input image into the space of a class-conditional generative neural network.
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.
1 code implementation • CVPR 2020 • Difan Liu, Mohamed Nabail, Aaron Hertzmann, Evangelos Kalogerakis
This paper introduces a method for learning to generate line drawings from 3D models.
no code implementations • 14 Feb 2020 • Aaron Hertzmann
Why is it that we can recognize object identity and 3D shape from line drawings, even though they do not exist in the natural world?
no code implementations • 10 Oct 2019 • Aaron Hertzmann
This paper explores visual indeterminacy as a description for artwork created with Generative Adversarial Networks (GANs).
1 code implementation • ICLR 2019 • Jianan Li, Tingfa Xu, Jianming Zhang, Aaron Hertzmann, Jimei Yang
Layouts are important for graphic design and scene generation.
1 code implementation • CVPR 2019 • Yijun Li, Chen Fang, Aaron Hertzmann, Eli Shechtman, Ming-Hsuan Yang
We propose a high-quality photo-to-pencil translation method with fine-grained control over the drawing style.
no code implementations • 13 Mar 2019 • Aaron Hertzmann
This paper proposes a way to understand neural network artworks as juxtapositions of natural image cues.
1 code implementation • 21 Jan 2019 • Jianan Li, Jimei Yang, Aaron Hertzmann, Jianming Zhang, Tingfa Xu
Layout is important for graphic design and scene generation.
no code implementations • 19 Nov 2018 • Shuhui Jiang, Zhaowen Wang, Aaron Hertzmann, Hailin Jin, Yun Fu
Third, font pairing is an asymmetric problem in that the roles played by header and body fonts are not interchangeable.
1 code implementation • 25 May 2018 • Zheng Xu, Michael Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin
We propose a fast feed-forward network for arbitrary style transfer, which can generate stylized image for previously unseen content and style image pairs.
no code implementations • 13 Jan 2018 • Aaron Hertzmann
It is then speculated about whether it could ever happen that AI systems could be credited with authorship of artwork.
1 code implementation • 8 Aug 2017 • Zoya Bylinskii, Nam Wook Kim, Peter O'Donovan, Sami Alsheikh, Spandan Madan, Hanspeter Pfister, Fredo Durand, Bryan Russell, Aaron Hertzmann
Our models are neural networks trained on human clicks and importance annotations on hundreds of designs.
no code implementations • ICCV 2017 • Michael J. Wilber, Chen Fang, Hailin Jin, Aaron Hertzmann, John Collomosse, Serge Belongie
Furthermore, we carry out baseline experiments to show the value of this dataset for artistic style prediction, for improving the generality of existing object classifiers, and for the study of visual domain adaptation.
6 code implementations • CVPR 2017 • Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann, Eli Shechtman
Neural Style Transfer has shown very exciting results enabling new forms of image manipulation.
7 code implementations • 19 Jun 2016 • Leon A. Gatys, Matthias Bethge, Aaron Hertzmann, Eli Shechtman
This note presents an extension to the neural artistic style transfer algorithm (Gatys et al.).
no code implementations • 5 May 2015 • Babak Saleh, Mira Dontcheva, Aaron Hertzmann, Zhicheng Liu
Infographics are complex graphic designs integrating text, images, charts and sketches.
no code implementations • 25 Nov 2014 • Hamid Izadinia, Ali Farhadi, Aaron Hertzmann, Matthew D. Hoffman
This paper proposes direct learning of image classification from user-supplied tags, without filtering.
no code implementations • ACM Transactions on Graphics 2014 • Peter O'Donovan, Jānis Lībeks, Aseem Agarwala, Aaron Hertzmann
These tools are complementary; a user may search for "graceful" fonts, select a reasonable one, and then refine the results from a list of fonts similar to the selection.
no code implementations • CVPR 2014 • Jonathan Taylor, Richard Stebbing, Varun Ramakrishna, Cem Keskin, Jamie Shotton, Shahram Izadi, Aaron Hertzmann, Andrew Fitzgibbon
We focus on modeling the human hand, and assume that a single rough template model is available.
1 code implementation • 15 Nov 2013 • Sergey Karayev, Matthew Trentacoste, Helen Han, Aseem Agarwala, Trevor Darrell, Aaron Hertzmann, Holger Winnemoeller
The style of an image plays a significant role in how it is viewed, but style has received little attention in computer vision research.
no code implementations • NeurIPS 2013 • Yanshuai Cao, Marcus A. Brubaker, David J. Fleet, Aaron Hertzmann
We propose an efficient optimization algorithm for selecting a subset of training data to induce sparsity for Gaussian process regression.