Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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—such as an arm acceleration for a throwing gesture—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.

Abstract: 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—such as an arm acceleration for a throwing gesture—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.

Abstract: 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.

Abstract: 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—such as an arm acceleration for a throwing gesture—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.

Abstract: 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.

Abstract: 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—such as an arm acceleration for a throwing gesture—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.

Abstract: 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.

Abstract: 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.

Abstract: 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—such as an arm acceleration for a throwing gesture—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.

Abstract: 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. User motion data and/or outputs of filters corresponding to gestures may be analyzed to determine those cases where assistance to the user on performing the gesture is appropriate.

Abstract: Systems, methods and computer readable media are disclosed for a localized gesture aggregation. In a system where user movement is captured by a capture device to provide gesture input to the system, demographic information regarding users as well as data corresponding to how those users respectively make various gestures is gathered. When a new user begins to use the system, his demographic information is analyzed to determine a most likely way that he will attempt to make or find it easy to make a given gesture. That most likely way is then used to process the new user's gesture input.

Abstract: 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—such as an arm acceleration for a throwing gesture—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.