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Adaptive Tearing and Cracking of Thin Sheets
Tobias Pfaff, Rahul Narain, Juan Miguel de Joya, and James O'Brien

Exposing Photo Manipulation from ... [more] Adaptive Tearing and Cracking of Thin Sheets
Tobias Pfaff, Rahul Narain, Juan Miguel de Joya, and James O'Brien

Exposing Photo Manipulation from Shading and Shadows
Eric Kee, James O'Brien, and Hany Farid

Self-Refining Games using Player Analytics
Matt Stanton, Ben Humberston, Brandon Kase, James O'Brien, Kayvon Fatahalian, and Adrien Treuille

Factored Axis-Aligned Filtering for Rendering Multiple Distribution Effects
Soham Mehta, Ravi Ramamoorthi, and Fredo Durand

Eyeglasses-free Display: Towards Correcting Visual Aberrations with Computational Light Field Displays
Fu-Chung Huang, Gordon Wetzstein, Brian A. Barsky, and Ramesh Raskar

High-Order Similarity Relations in Radiative Transfer
Shuang Zhao, Ravi Ramamoorthi, and Kavita Bala

Discrete Stochastic Microfacet Models
Wenzel Jakob, Milos Hasan, Ling-Qi Yan, Jason Lawrence, Ravi Ramamoorthi, and Steve Marschner

Rendering Glints on High-Resolution Normal-Mapped Specular Surfaces
Ling-Qi Yan, Milos Hasan, Wenzel Jakob, Jason Lawrence, Steve Marschner, and Ravi Ramamoorthi

3D Object Manipulation in a Single Image using Stock 3D Models
Natasha Kholgade, Tomas Simon, Alexei Efros, and Yaser Sheikh


2014 Papers
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We propose a novel method to extract hierarchies of vortex filaments from given three-dimensional flow velocity fields. We call these collections of filaments Hierarchical Vorticity Skeletons (HVS). They extract multi-scale information from the input velocity field, which is not possible with any previous filament extraction approach. Once computed, these HVSs provide a powerful mechanism for data compression and a very natural way for modifying flows. The data compression rates for all presented examples are above 99%. Employing our skeletons for flow modification has several advantages over traditional approaches. Most importantly, they reduce the complexity of three-dimensional fields to one-dimensional lines and, make complex fluid data more accessible for changing defining features of a flow. The strongly reduced HVS dataset still carries the main characteristics of the flow. Through the hierarchy we can capture the main features of different scales in the flow and by that provide a level of detail control. In contrast to previous work, we present a fully automated pipeline to robustly decompose dense velocities into filaments.

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