Microsoft Technology Licensing, LLC

Other

Mark James Finocchio

Ryan M Geiss

Alexandru Octavian Balan

Jeffrey Neil Margolis

Kyungsuk David Lee

Johnny Hsienchow Lee

Jamie Daniel Joseph Shotton

Craig C Peeper

Tommer Leyvand

Alex A Kipman

Robert Matthew Craig

Momin Mahmoud Al-Ghosien

Oliver M Williams

Matt Bronder

Stephen G Latta

Christopher Douglas Edmonds

Kathryn Stone Perez

Kudo Tsunoda

Richard E Moore

R Stephen Polzin

Nicholas M Kramer

Darren Bennett

Jeffrey Lawrence Bogdan

Namita Gupta

Constantin Dulu

Kevin Geisner

Alex Aben-Athar Kipman

Daniel Joseph McCulloch

Ben J Sugden

Brian James Mount

Robert L Crocco, Jr

Nathan Ackerman

Michael John Hillberg

Brian E Keane

Jamie Shotton

Rudy Jacobus Poot

Kathryn Stone-Perez

Michael Bleyer

Adam James Muff

Avi Bar-Zeev

Rod G Fleck

John Allen Tardif

Relja Markovic

Andrew William Fitzgibbon

Jason Bradley Scott

Andrew Bert Hodge

Jason S Flaks

Jonathan Steed

Susan Elizabeth Carrie

Zsolt Mathe

Johnny Chung Lee

Charles Claudius Marais

Shao Liu

Michael Scott Fenton

Britta Silke Hummel

Samuel Watkins Bent

Sheridan Martin Small

Benjamin Franklin Carter

Darren Alexander Bennett

Robert E Sinclair, Ii

Erroll William Wood

Duncan Paul Robertson

Peter Francis Ostertag

Roger Sebastian Kevin Sylvan

Mihai-Dan Budiu

Arnulfo Zepeda Navratil

Michael Edward Dulac Winser

Gregory G Snook

John Albert Judnich

Randy Crane

Jamie D J Shotton

Benjamin Franklin Wang Jen-Shong Carter

A method to identify one or more depth-image segments that correspond to a predetermined object type is enacted in a depth-imaging controller operatively coupled to an optical time-of-flight (ToF) camera; it comprises: receiving depth-image data from the optical ToF camera, the depth-image data exhibiting an aliasing uncertainty, such that a coordinate (X, Y) of the depth-image data maps to a periodic series of depth values {Z}; and labeling, as corresponding to the object type, one or more coordinates of the depth-image data exhibiting the aliasing uncertainty.

Subject tracking with aliased time-of-flight data

Attribute state classification techniques are described. In one or more implementations, one or more pixels of an image are classified by a computing device as having one or several states for one or more attributes that do not identify corresponding body parts of a user. A gesture is recognized by the computing device that is operable to initiate one or more operations of the computing device based at least in part of the state classifications of the one or more pixels of one or more attributes.

Attribute state classification

Systems are provided that include a wireless hand-held inertial controller with passive optical and inertial tracking in a slim form-factor. These systems are configured for use with a head mounted virtual or augmented reality display device (HMD) that operates with six degrees of freedom by fusing (i) data related to the position of the controller derived from a forward-facing optical sensor located in the HMD with (ii) data relating to the orientation of the controller derived from an inertial measurement unit located in the controller.

Passive optical and inertial tracking in slim form-factor

A modular holding fixture for selectively coupling to a wireless hand-held inertial controller to provide passive optical and inertial tracking in a slim form-factor for use with a head mounted display that operates with six degrees of freedom by fusing (i) data related to the position of the controller derived from a forward-facing depth camera located in the head mounted display with (ii) data relating to the orientation of the controller derived from an inertial measurement unit located in the controller.

Modular extension of inertial controller for six DOF mixed reality input

Apparatus and systems directed to a wireless hand-held inertial controller with passive optical and inertial tracking in a slim form-factor, for use with a head mounted virtual or augmented reality display device (HMD), that operates with six degrees of freedom by fusing (i) data related to the position of the controller derived from a forward-facing optical sensor located in the HMD with (ii) data relating to the orientation of the controller derived from an inertial measurement unit located in the controller.

A system recognizes human beings in their natural environment, without special sensing devices attached to the subjects, uniquely identifies them and tracks them in three dimensional space. The resulting representation is presented directly to applications as a multi-point skeletal model delivered in real-time. The device efficiently tracks humans and their natural movements by understanding the natural mechanics and capabilities of the human muscular-skeletal system. The device also uniquely recognizes individuals in order to allow multiple people to interact with the system via natural movements of their limbs and body as well as voice commands/responses.

Device for identifying and tracking multiple humans over time

Two-handed interactions with a natural user interface are disclosed. For example, one embodiment provides a method comprising detecting via image data received by the computing device a context-setting input performed by a first hand of a user, and sending to a display a user interface positioned based on a virtual interaction coordinate system, the virtual coordinate system being positioned based upon a position of the first hand of the user. The method further includes detecting via image data received by the computing device an action input performed by a second hand of the user, the action input performed while the first hand of the user is performing the context-setting input, and sending to the display a response based on the context-setting input and an interaction between the action input and the virtual interaction coordinate system.

Two-hand interaction with natural user interface

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

Embodiments are disclosed that relate to processing image pixels. For example, one disclosed embodiment provides a system for classifying pixels comprising retrieval logic; a pixel storage allocation including a plurality of pixel slots, each pixel slot being associated individually with a pixel, where the retrieval logic is configured to cause the pixels to be allocated into the pixel slots in an input sequence; pipelined processing logic configured to output, for each of the pixels, classification information associated with the pixel; and scheduling logic configured to control dispatches from the pixel slots to the pipelined processing logic, where the scheduling logic and pipelined processing logic are configured to act in concert to generate the classification information for the pixels in an output sequence that differs from and is independent of the input sequence.

System and method for classifying pixels

An augmented reality submission includes a hologram to virtually augment a world space object and a compensation offer for presenting the hologram to a viewer of the world space object. The augmented reality submission is selected as a winning submission if the submission satisfies a selection criteria.

Augmented reality auction platform

Methods for recognizing gestures within a near-field environment are described. In some embodiments, a mobile device, such as a head-mounted display device (HMD), may capture a first image of an environment while illuminating the environment using an IR light source with a first range (e.g., due to the exponential decay of light intensity) and capture a second image of the environment without illumination. The mobile device may generate a difference image based on the first image and the second image in order to eliminate background noise due to other sources of IR light within the environment (e.g., due to sunlight or artificial light sources). In some cases, object and gesture recognition techniques may be applied to the difference image in order to detect the performance of hand and/or finger gestures by an end user of the mobile device within a near-field environment of the mobile device.

Near-plane segmentation using pulsed light source

A media feed interface may be provided that may be used to extract a media frame from a media feed. The media feed interface may access a capture device, a file, and/or a network resource. Upon accessing the capture device, file, and/or network resource, the media feed interface may populate buffers with data and then may create a media feed from the buffers. Upon request, the media feed interface may isolate a media frame within the media feed. For example, the media feed interface analyze media frames in the media feed to determine whether a media frame includes information associated with, for example, the request. If the media frame includes the requested information, the media feed interface may isolate the media frame associated with the information and may provide access to the isolated media frame.

Protocol and format for communicating an image from a camera to a computing environment

Technology is described for displaying a collision between objects by an augmented reality display device system. A collision between a real object and a virtual object is identified based on three dimensional space position data of the objects. At least one effect on at least one physical property of the real object is determined based on physical properties of the real object, like a change in surface shape, and physical interaction characteristics of the collision. Simulation image data is generated and displayed simulating the effect on the real object by the augmented reality display. Virtual objects under control of different executing applications can also interact with one another in collisions.

Displaying a collision between real and virtual objects

A depth image of a scene may be received, observed, or captured by a device. The depth image may include a human target that may have, for example, a portion thereof non-visible or occluded. For example, a user may be turned such that a body part may not be visible to the device, may have one or more body parts partially outside a field of view of the device, may have a body part or a portion of a body part behind another body part or object, or the like such that the human target associated with the user may also have a portion body part or a body part non-visible or occluded in the depth image. A position or location of the non-visible or occluded portion or body part of the human target associated with the user may then be estimated.

Systems and methods for estimating a non-visible or occluded body part

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Redmond, WA, United States of America

Mark James Finocchio