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Disney Research

Visual Computing

Saliency Filters: Contrast Based Filtering for Salient Region Detection

In this paper, we reconsider some of the design choices of previous methods and propose a conceptually clear and intuitive algorithm for contrast-based saliency estimation.

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Smart Scribbles for Sketch Segmentation

We present Smart Scribble, a new scribble-based interface for user-guided segmentation of digital sketchy drawings.

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BetweenIT: An Interactive Tool for Tight Inbetweening

We present a novel technique for stroke interpolation from only two keys which combines a stroke motion constructed from logarithmic spiral vertex trajectories with a stroke deformation based on curvature averaging and twisting warps.

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Topology-Driven Vectorization of Line Drawings

We propose a vectorization algorithm specialized for clean line drawings that analyzes the drawing’s topology in order to overcome junction ambiguities.

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OverCoat: An Implicit Canvas for 3D Painting

We present a technique to generalize the 2D painting metaphor to 3D that allows the artist to treat the full 3D space as a canvas.

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Computing and Fabricating Multilayer Models

We present a method for automatically converting a digital 3D model into a multilayer model: a parallel stack of high-resolution 2D images embedded within a semi-transparent medium.

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Practical Non-linear Photometric Projector Compensation

We propose a novel approach to generate a high quality photometric compensated projection which, to our knowledge, is the first one, which does not require a radiometrical pre-calibration of cameras or projectors.

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Fast and Stable Color Balancing for Images and Augmented Reality

This paper addresses the problem of globally balancing colors between images. The input to our algorithm is a sparse set of desired color correspondences between a source and a target image.

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Cache-Efficient Graph Cuts on Structured Grids

We propose a compact data structure with cache-efficient memory layout for the representation of graph instances that are based on regular N-D grids with topologically identical neighborhood systems.

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Domain Adaptation for Structured Regression

We propose an effective and efficient semi-supervised domain adaptation (SSDA) approach for addressing such more severe biases in the data.

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