Aaron Hertzmann

TL;DR: 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.

TL;DR: 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.

TL;DR: 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.

TL;DR: 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.

TL;DR: This work approaches the problem of stylistic motion synthesis by learning motion patterns from a highly varied set of motion capture sequences, and identifies common choreographic elements across sequences, the different styles in which each element is performed, and a small number of styling degrees of freedom which span the many variations in the dataset.