Image analogies
- Aaron Hertzmann, C. Jacobs, Nuria Oliver, B. Curless, D. Salesin
- ArtInternational Conference on Computer Graphics and…
- 1 August 2001
This paper describes a new framework for processing images by example, called “image analogies,” based on a simple multi-scale autoregression, inspired primarily by recent results in texture synthesis.
Recovering non-rigid 3D shape from image streams
- C. Bregler, Aaron Hertzmann, H. Biermann
- Computer ScienceProceedings IEEE Conference on Computer Vision…
- 13 June 2000
This paper proposes a novel technique based on a non-rigid model, where the 3D shape in each frame is a linear combination of a set of basis shapes, and can be factored in a three-step process to yield pose, configuration and shape.
GANSpace: Discovering Interpretable GAN Controls
- Erik Härkönen, Aaron Hertzmann, J. Lehtinen, Sylvain Paris
- Computer ScienceNeural Information Processing Systems
- 6 April 2020
This paper describes a simple technique to analyze Generative Adversarial Networks and create interpretable controls for image synthesis, and shows that BigGAN can be controlled with layer-wise inputs in a StyleGAN-like manner.
Gaussian Process Dynamical Models for Human Motion
- Jack M. Wang, David J. Fleet, Aaron Hertzmann
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 1 February 2008
This work marginalize out the model parameters in closed form by using Gaussian process priors for both the dynamical and the observation mappings, which results in a nonparametric model for dynamical systems that accounts for uncertainty in the model.
Removing camera shake from a single photograph
- R. Fergus, Barun Singh, Aaron Hertzmann, S. Roweis, W. Freeman
- MathematicsInternational Conference on Computer Graphics and…
- 1 July 2006
This work introduces a method to remove the effects of camera shake from seriously blurred images, which assumes a uniform camera blur over the image and negligible in-plane camera rotation.
Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors
- L. Torresani, Aaron Hertzmann, C. Bregler
- EngineeringIEEE Transactions on Pattern Analysis and Machine…
- 1 May 2008
A reconstruction method using a Probabilistic Principal Components Analysis shape model and an estimation algorithm that simultaneously estimates 3D shape and motion for each instant, learns the PPCA model parameters, and robustly fills-in missing data points is proposed.
Gaussian Process Dynamical Models
- Jack M. Wang, David J. Fleet, Aaron Hertzmann
- Computer ScienceNIPS
- 5 December 2005
This paper marginalize out the model parameters in closed-form, using Gaussian Process (GP) priors for both the dynamics and the observation mappings, resulting in a nonparametric model for dynamical systems that accounts for uncertainty in the model.
Learning 3D mesh segmentation and labeling
- E. Kalogerakis, Aaron Hertzmann, Karan Singh
- Computer ScienceACM Transactions on Graphics
- 26 July 2010
This paper presents a data-driven approach to simultaneous segmentation and labeling of parts in 3D meshes, formulated as a Conditional Random Field model, with terms assessing the consistency of faces with labels, and terms between labels of neighboring faces.
Painterly rendering with curved brush strokes of multiple sizes
- Aaron Hertzmann
- ArtInternational Conference on Computer Graphics and…
- 24 July 1998
This work presents a new method for creating an image with a handpainted appearance from a photograph, and a new approach to designing styles of illustration, and presents a framework for describing a wide range of visual styles.
Illustrating smooth surfaces
- Aaron Hertzmann, D. Zorin
- Computer ScienceInternational Conference on Computer Graphics and…
- 1 July 2000
An efficient, deterministic algorithm for finding silhouettes based on geometric duality, and an algorithm for segmenting the silhouette curves into smooth parts with constant visibility can be used to find all silhouettes in real time in software.
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