Microsoft Technology Licensing, LLC

Gregory Nelson Snook

Stephen G Latta

Relja Markovic

Kevin Geisner

Arthur Charles Tomlin

Kudo Tsunoda

Johnny Hsienchow Lee

Darren Alexander Bennett

Justin McBride

Kathryn Stone Perez

Darren Bennett

Alex A Kipman

Adam Jethro Langridge

John Clavin

Kyungsuk David Lee

Christopher Vuchetich

Mark Mihelich

Peter Glenn Sarrett

David Jason Christopher Horbach

Matthew Jon Puls

Relia Markovic

Adam Jethro Lanygridge

Stephen Gilchrist Latta

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

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

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

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

Systems, methods and computer readable media are disclosed for gesture shortcuts. A user's movement or body position is captured by a capture device of a system, and is used as input to control the system. For a system-recognized gesture, there may be a full version of the gesture and a shortcut of the gesture. Where the system recognizes that either the full version of the gesture or the shortcut of the gesture has been performed, it sends an indication that the system-recognized gesture was observed to a corresponding application. Where the shortcut comprises a subset of the full version of the gesture, and both the shortcut and the full version of the gesture are recognized as the user performs the full version of the gesture, the system recognizes that only a single performance of the gesture has occurred, and indicates to the application as such.

Gesture shortcuts

A capture device may capture a user's motion and a display device may display a model that maps to the user's motion, including gestures that are applicable for control. A user may be unfamiliar with a system that maps the user's motions or not know what gestures are applicable for an executing application. A user may not understand or know how to perform gestures that are applicable for the executing application. Providing visual feedback representing instructional gesture data to the user can teach the user how to properly gesture. The visual feedback may be provided in any number of suitable ways. For example, visual feedback may be provided via ghosted images, player avatars, or skeletal representations. The system can process prerecorded or live content for displaying visual feedback representing instructional gesture data. The feedback can portray the deltas between the user's actual position and the ideal gesture position.

Show body position

Systems, methods and computer readable media are disclosed for a gesture recognizer system architecture. A recognizer engine is provided, which receives user motion data and provides that data to a plurality of filters. A filter corresponds to a gesture, that may then be tuned by an application receiving information from the gesture recognizer so that the specific parameters of the gesture&#x2014;such as an arm acceleration for a throwing gesture&#x2014;may be set on a per-application level, or multiple times within a single application. Each filter may output to an application using it a confidence level that the corresponding gesture occurred, as well as further details about the user motion data.

Gesture recognizer system architecture

Systems, methods and computer readable media are disclosed for a gesture tool. A capture device captures user movement and provides corresponding data to a gesture recognizer engine and an application. From that, the data is parsed to determine whether it satisfies one or more gesture filters, each filter corresponding to user-performed gesture. The data and the information about the filters is also sent to a gesture tool, which displays aspects of the data and filters. In response to user input corresponding to a change in a filter, the gesture tool sends an indication of such to the gesture recognizer engine and application, where that change occurs.

Growing community of inventors

Sammamish, WA, United States of America

Gregory Nelson Snook