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Disney Research

Computer Vision

Parameterizing Object Detectors in the Continuous Pose Space

In this paper, we propose a structured kernel machine approach to treat object detection and pose estimation jointly in a mutually beneficial way.

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Social Roles in Hierarchical Models for Human Activity Recognition

We present a hierarchical model for human activity recognition in entire multi-person scenes.

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Human Context: Modeling Human-Human Interactions for Monocular 3D Pose Estimation

Automatic recovery of 3d pose of multiple interacting subjects from unconstrained monocular image sequence is a challenging and largely unaddressed problem.

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Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines

We present a type of Temporal Restricted Boltzmann Machine that defines a probability distribution over an output sequence conditional on an input sequence.

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3D Reconstruction of a Moving Point from a Series of 2D Projections

This paper presents a linear solution for reconstructing the 3D trajectory of a moving point from its correspondence in a collection of 2D perspective images, given the 3D spatial pose and time of capture of the cameras that produced each image.

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Representing Team Behaviours from Noisy Data using Player Role

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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De-An Huang

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Extending the Performance of Human Classifiers using a Viewpoint Specific Approach

This paper describes human classifiers that are 'viewpoint specific', meaning specific to subjects being observed by a particular camera in a particular scene.

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From Subcategories to Visual Composites: A Multi-Level Framework for Object Detection

We propose a weakly-supervised framework for object detection where we discover subcategories and the composites automatically with only traditional object-level category labels as input.

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Aparna Taneja

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