Microsoft Technology Licensing, LLC

Tommer Leyvand

Craig C Peeper

Szymon Piotr Stachniak

Johnny Hsienchow Lee

Jinyu Li

Shao Liu

Johnny Chung Lee

Ryan M Geiss

Jamie Daniel Joseph Shotton

Mark James Finocchio

Robert Matthew Craig

Casey Leon Meekhof

Momin Mahmoud Al-Ghosien

Oliver M Williams

Mitchell Stephen Dernis

Matt Bronder

Tim Keosababian

Jian Qiao Sun

Yichen Wei

Simon Stachniak

Mark Plagge

Bhaven P Dedhia

Christian Klein

Vijay Baiyya

Spencer Dean Perreault

Ronald Forbes

Timothy Milton Keosababian

Dawson Yee

Eddie L Mays

Charles Claudius Marais

Prafulla Jinendra Masalkar

Joseph C Bertolami

Richard Henry Irving

Nazeeh Amin Eldirghami

Russ Glaser

Parham Mohadjer

Ryan Gunn

Jon D Pulsipher

Patrick Orville Cook

Toan Huynh

Wayne Richard Bisson

Timothy Gerken

Alan Stuart

James Waletzky

Ajay Ramjee

Susan Shuchen Song

James Chadon Foster

Michael Taylor Janney

Kien Toan Huynh

Robert John Fitzgerald

Zhengyou Zhang

Michael Anthony Hall

Baining Guo

Xin Tong

Eyal Krupka

Mark D Schwesinger

Jason S Flaks

Fang Wen

Zsolt Mathe

Ali M Vassigh

Igor Kviatkovsky

Xiang Cao

Feng Xu

Takaaki Shiratori

Leonardo G Del Castillo

Michael C Johnson

Zhuowen Tu

Robert Mitchell Smith

Dax H Hawkins

Ke Deng

Igor Abramovski

Emad Barsoum

Wei Shen

Bryon Ashley

Duncan James McKay

Thomas Gersten

John Michael Link

Ronald Omega Forbes, Jr

Andrew Jesse Milluzzi

Leonardo Del Castillo

John Link

An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may then be discarded to isolate one or more voxels associated with a foreground object such as a human target and the isolated voxels associated with the foreground object may be processed.

Systems and methods for removing a background of an image

An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may also be removed to isolate one or more voxels associated with a foreground object such as a human target. A location or position of one or more extremities of the isolated human target may then be determined.

Methods and systems for determining and tracking extremities of a target

An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may also be removed to isolate one or more voxels associated with a foreground object such as a human target. A location or position of one or more extremities of the isolated human target may be determined and a model may be adjusted based on the location or position of the one or more extremities.

Human tracking system

A method to identify a targeted object based on eye tracking and gesture recognition. The method is enacted in a compute system controlled by a user and operatively coupled to a machine vision system. In this method, the compute system receives, from the machine vision system, video imaging a head and pointer of the user. Based on the video, the compute system computes a geometric line of sight of the user, which is partly occluded by the pointer. Then, with reference to position data for one or more objects, the compute system identifies the targeted object, situated along the geometric line of sight.

Pointer projection for natural user input

Techniques for automatically selecting from amongst multiple camera views available in a multi-camera system. In an aspect, one or more metrics are calculated for multiple camera views. The metrics may be used to identify one or more of the camera views as fulfilling a condition, in response to which a particular one of the camera views may be selected for display. For example, in a 'select-better-view' strategy, the camera view associated with a more optimal image (e.g., better lighting, or absence of occlusion) is selected for display. In a 'flag-diagnostic-view' strategy, the camera view associated with a less optimal image is displayed, e.g., to indicate that a particular camera may require user attention. Specific techniques for calculating metrics are further disclosed.

Multi-camera view selection based on calculated metric

A computing system such as a game console maintains and updates a biometric profile of a user. In one aspect, biometric data of the user is continuously obtained from a sensor such as an infrared and visible light camera, and used to update the biometric profile using a machine learning process. In another aspect, a user is prompted to confirm his or her identify when multiple users are detected at the same time and/or when the user is detected with a confidence level which is below a threshold. A real-time image of the user being identified can be displayed on a user interface with user images associated with one or more accounts. In another aspect, the biometric profile is managed by a shell on the computing system, where the shell makes the biometric profile available to any of a number of applications on the computing system.

Managed biometric identity

An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A model may be adjusted based on a location or position of one or more extremities estimated or determined for a human target in the grid of voxels. The model may also be adjusted based on a default location or position of the model in a default pose such as a T-pose, a DaVinci pose, and/or a natural pose.

Systems and methods for tracking a model

A method comprises receiving one or more depth images from a depth camera, the depth images indicating a depth of a surface imaged by each pixel of the depth images. The method may further comprise identifying a human subject imaged by the depth images and recognizing within one or more of the depth images a beacon emitted from a control device. A position of the control device may be assessed in three dimensions, and the control device may be associated with the human subject based on a proximity of the control device to the human subject or other parameter of the control device with relation to the human subject.

Associating an object with a subject

A method for biometric identification for use with a computing device is provided herein. The method includes capturing a temporal sequence of images of the face of a user at different locations within a three-dimensional interaction space. The method further includes extracting one or more face descriptors from the images and generating a biometric template compiling the face descriptors.

Biometric recognition

A control device includes a housing. The control device also includes a wireless communicator interior the housing. The wireless communicator wirelessly sends commands for controlling an electronic device, such as a game console. The control device also includes a reflector positioned to reflect light directed at the housing.

Control device with passive reflector

A method of tracking a subject includes receiving from a source a depth image of a scene including the subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that image the subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the subject as a model including a plurality of shapes.

Pose tracking pipeline

An optical flow of depth video of a depth camera imaging a human subject is recognized. An energy field created by motion of the human subject is generated as a function of the optical flow and specified rules of a physical simulation of the virtual environment. The energy field is mapped to a virtual position in the virtual environment. A property of a virtual object in the virtual environment is adjusted based on a plurality of energy elements of the energy field in response to the virtual object interacting with the virtual position of the energy field.

Motion control of a virtual environment

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Tommer Leyvand