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Disney Research Pittsburgh

What is a Good Day for Outdoor Photometric Stereo?

In this paper, we explore the stability of surface normals reconstructed outdoors.

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No Bias Left Behind: Covariate Shift Adaptation for Discriminative 3D Pose Estimation

Discriminative, or (structured) prediction, methods have proved effective for variety of problems in computer vision; a notable example is 3D monocular pose estimation.

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Recognizing Team Activities from Noisy Data

In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections.

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3D Printing Pneumatic Device Controls with Variable Activation Force Capabilities

We explore 3D printing physical controls whose tactile response can be manipulated programmatically through pneumatic actuation.

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Acoustruments: Passive, Acoustically-Driven Interactive Controls for Hand Held Devices

We introduce Acoustruments: low-cost, passive, and powerless mechanisms, made from plastic, that can bring rich, tangible functionality to handheld devices.

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A Mouth Full of Words: Visually Consistent Acoustic Redubbing

This paper introduces a method for automatic redubbing of video that exploits the many-to-many mapping of phoneme sequences to lip movements modeled as dynamic visemes [1].

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A Layered Fabric 3D Printer for Soft Interactive Objects

We present a new type of 3D printer that can form precise, but soft and deformable 3D objects from layers of off-the- shelf fabric.

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IDSense: A Human Object Interaction Detection System Based on Passive UHF RFID

In order to enable unobtrusive human object interaction detection, we propose a minimalistic approach to instrumenting everyday objects with passive (i.e. battery-free) UHF RFID tags.

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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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Detecting and Tracking Sports Players with Random Forests and Context-Conditioned Motion Models

Player movements in team sports are often complex and highly correlated with both nearby and distant players. A single motion model would require many degrees of freedom to represent the full motion diversity of each player and could be difficult to use in practice. I

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