Search Results for author: Aaron Hertzmann

Found 30 papers, 14 papers with code

Toward Modeling Creative Processes for Algorithmic Painting

no code implementations3 May 2022 Aaron Hertzmann

This paper proposes a framework for computational modeling of artistic painting algorithms, inspired by human creative practices.

Neural Strokes: Stylized Line Drawing of 3D Shapes

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.

Contact-Aware Retargeting of Skinned Motion

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.

Motion Estimation motion retargeting

The Role of Edges in Line Drawing Perception

no code implementations22 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.

Toward Quantifying Ambiguities in Artistic Images

no code implementations21 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.

Predicting Visual Importance Across Graphic Design Types

no code implementations7 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.

Contact and Human Dynamics from Monocular Video

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.

Human Dynamics Pose Estimation

Transforming and Projecting Images into Class-conditional Generative Networks

2 code implementations4 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.


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

Why Do Line Drawings Work? A Realism Hypothesis

no code implementations14 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?

Visual Indeterminacy in GAN Art

no code implementations10 Oct 2019 Aaron Hertzmann

This paper explores visual indeterminacy as a description for artwork created with Generative Adversarial Networks (GANs).

Image Generation

Im2Pencil: Controllable Pencil Illustration from Photographs

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.


Aesthetics of Neural Network Art

no code implementations13 Mar 2019 Aaron Hertzmann

This paper proposes a way to understand neural network artworks as juxtapositions of natural image cues.

Image Stylization

Visual Font Pairing

no code implementations19 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.

Metric Learning

Learning from Multi-domain Artistic Images for Arbitrary Style Transfer

1 code implementation25 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.

Style Transfer

Can Computers Create Art?

no code implementations13 Jan 2018 Aaron Hertzmann

It is then speculated about whether it could ever happen that AI systems could be credited with authorship of artwork.

BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

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.

Domain Adaptation

Preserving Color in Neural Artistic Style Transfer

7 code implementations19 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.).

Style Transfer

Learning Style Similarity for Searching Infographics

no code implementations5 May 2015 Babak Saleh, Mira Dontcheva, Aaron Hertzmann, Zhicheng Liu

Infographics are complex graphic designs integrating text, images, charts and sketches.

Image Retrieval

Recognizing Image Style

1 code implementation15 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.

Image Retrieval TAG

Efficient Optimization for Sparse Gaussian Process Regression

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.

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