Microsoft Technology Licensing, LLC

Relja Markovic

Stephen G Latta

Kevin Geisner

Darren Bennett

Gregory Nelson Snook

Brian Murphy

Ryan Lucas Hastings

Joel Deaguero

Christopher Harley Willoughby

Arthur Charles Tomlin

Shawn Crispin Wright

Kudo Tsunoda

Daniel Joseph McCulloch

Jason Bradley Scott

Alex A Kipman

Jeffrey Jesus Evertt

David C Haley, Jr

Jonathan Steed

Michael Scavezze

Mike Scavezze

Justin Avram Clark

Samuel Z Glassenberg

Ramanujan Srinivasan

Kathryn Stone Perez

Alex Aben-Athar Kipman

Ben J Sugden

Cameron Graeme Brown

Robert L Crocco, Jr

Nicholas Ferianc Kamuda

Peter Tobias Kinnebrew

Laura K Massey

Brian E Keane

Nicholas Fajt

Tony Ambrus

Darren Alexander Bennett

Daniel Deptford

John Bevis

Thomas Salter

Jamie Bryant Kirschenbaum

Christopher Vuchetich

Oscar Omar Garza Santos

Matt Coohill

Joe Sola

Zachary Quarles

Michael Klucher

Justin McBride

Pedro Perez

Johnny Hsienchow Lee

Andrew John Fuller

Jeffrey Neil Margolis

Kyungsuk David Lee

Sheridan Martin Small

Adam Jethro Langridge

Amos Dylan Vance

David M Hill

Peter Glenn Sarrett

Mark Mihelich

A Dylan Vance

Dylan Vance

David Galloway

Mark James Finocchio

Benjamin Isaac Vaught

R Stephen Polzin

John Clavin

Bradley Robert Pettit

Eric Soldan

David Seymour

Daniel Lee Osborn

William Axel Olsen

William Paul Giese

Darrin Adam Brown

Matthew Monson

Matthew James Searcy

David Jason Christopher Horbach

Matthew Jon Puls

Greg Snook

Gregory G Snook

Rhett Mathis

David Gierok

Cam McRae

Jonathan T Steed

Stephen Gilchrist Latta

Kevin A Geisner

Amos D Vance

Systems, methods and computer readable media are disclosed for gesture input beyond skeletal. A user's movement or body position is captured by a capture device of a system. Further, non-user-position data is received by the system, such as controller input by the user, an item that the user is wearing, a prop under the control of the user, or a second user's movement or body position. The system incorporates both the user-position data and the non-user-position data to determine one or more inputs the user made to the system.

Combining gestures beyond skeletal

To digitize an object, a camera captures images of different sides of the object with color and depth data. At least two different sides of the object are identified from the images, and constructions are created of the sides of the object from the images. Points of the constructions to connect to one another are determined and used to align the constructions. The construction are merged to generate a rendition of the object. Various techniques are applied to extrapolate edges, remove seams, extend color intelligently, filter noise, apply skeletal structure to the object, and optimize the digitization further. The rendition of the object can be provided for display as a digital representation of the object and potentially used in different applications (e.g., games, Web, etc.).

Object digitization

The technology provides contextual personal information by a mixed reality display device system being worn by a user. A user inputs person selection criteria, and the display system sends a request for data identifying at least one person in a location of the user who satisfy the person selection criteria to a cloud based application with access to user profile data for multiple users. Upon receiving data identifying the at least one person, the display system outputs data identifying the person if he or she is within the field of view. An identifier and a position indicator of the person in the location is output if not. Directional sensors on the display device may also be used for determining a position of the person. Cloud based executing software can identify and track the positions of people based on image and non-image data from display devices in the location.

Providing location occupancy analysis via a mixed reality device

Digitizing objects in a picture is discussed herein. A user presents the object to a camera, which captures the image comprising color and depth data for the front and back of the object. For both front and back images, the closest point to the camera is determined by analyzing the depth data. From the closest points, edges of the object are found by noting large differences in depth data. The depth data is also used to construct point cloud constructions of the front and back of the object. Various techniques are applied to extrapolate edges, remove seams, extend color intelligently, filter noise, apply skeletal structure to the object, and optimize the digitization further. Eventually, a digital representation is presented to the user and potentially used in different applications (e.g., games, Web, etc.).

Systems, methods and computer readable media are disclosed for controlling perspective of a camera-controlled computer. A capture device captures user gestures and sends corresponding data to a recognizer engine. The recognizer engine analyzes the data with a plurality of filters, each filter corresponding to a gesture. Based on the output of those filters, a perspective control is determined, and a display device displays a new perspective corresponding to the perspective control.

Method to control perspective for a camera-controlled computer

Implementations for identifying, capturing, and presenting high-quality photo-representations of acts occurring during play of a game that employs motion tracking input technology are disclosed. As one example, a method is disclosed that includes capturing via an optical interface, a plurality of photographs of a player in a capture volume during play of the electronic game. The method further includes for each captured photograph of the plurality of captured photographs, comparing an event-based scoring parameter to an event depicted by or corresponding to the captured photograph. The method further includes assigning respective scores to the plurality of captured photographs based, at least in part, on the comparison to the even-based scoring parameter. The method further includes associating the captured photographs at an electronic storage media with the respective scores assigned to the captured photographs.

Intelligent gameplay photo capture

In applications that display a representation of a user, it may be reasonable to insert a pre-canned animation rather than animating a user's captured motion. For example, in a tennis swing, the ball toss and take back in a serve could be a pre-canned animation, whereas the actual forward swing may be mapped from the user's gestures. An animation of a user's gestures can be chained together into sequences with pre-canned animations, where animation blending techniques can provide for a smoother transition between the animation types. Techniques for blending animations, that may comprise determining boundaries and transition points between pre-canned animations and animations based on captured motion, may improve animation efficiency. Gesture history, including joint position, velocity, and acceleration, can be used to determine user intent, seed parameters for subsequent animations and game control, and determine the subsequent gestures to initiate.

Chaining animations

Depth-image analysis is performed with a device that analyzes a human target within an observed scene by capturing depth-images that include depth information from the observed scene. The human target is modeled with a virtual skeleton including a plurality of joints. The virtual skeleton is used as an input for controlling a driving simulation.

Driving simulator control with virtual skeleton

Embodiments are disclosed that relate to operating a user interface on an augmented reality computing device comprising a see-through display system. For example, one disclosed embodiment includes identifying one or more objects located outside a field of view of a user, and for each object of the one or more objects, providing to the user an indication of positional information associated with the object.

Indicating out-of-view augmented reality images

A system may receive image data and capture motion with respect to a target in a physical space and recognize a gesture from the captured motion. It may be desirable to isolate aspects of captured motion to differentiate random and extraneous motions. For example, a gesture may comprise motion of a user's right arm, and it may be desirable to isolate the motion of the user's right arm and exclude an interpretation of any other motion. Thus, the isolated aspect may be the focus of the received data for gesture recognition. Alternately, the isolated aspects may be an aspect of the captured motion that is removed from consideration when identifying a gesture from the captured motion. For example, gesture filters may be modified to correspond to the user's natural lean to eliminate the effect the lean has on the registry of a motion with a gesture filter.

Isolate extraneous motions

Disclosed herein are systems and methods for altering a view perspective within a display environment. For example, gesture data corresponding to a plurality of inputs may be stored. The input may be input into a game or application implemented by a computing device. Images of a user of the game or application may be captured. For example, a suitable capture device may capture several images of the user over a period of time. The images may be analyzed and processed for detecting a user's gesture. Aspects of the user's gesture may be compared to the stored gesture data for determining an intended gesture input for the user. The comparison may be part of an analysis for determining inputs corresponding to the gesture data, where one or more of the inputs are input into the game or application and cause a view perspective within the display environment to be altered.

Altering a view perspective within a display environment

A virtual skeleton includes a plurality of joints and provides a machine readable representation of a human target observed with a three-dimensional depth camera. A relative position of a hand joint of the virtual skeleton is translated as a gestured control, and a three-dimensional virtual world is controlled responsive to the gestured control.

Skeletal control of three-dimensional virtual world

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Seattle, WA, United States of America

Relja Markovic