Microsoft Technology Licensing, LLC

Emily Yang

Mark D Schwesinger

Oscar E Murillo

Christian Klein

Jay P Kapur

Richard S Bailey

Sergio Paolantonio

Joseph Wheeler

Xiaoji Chen

Adam M Smith

Rita Yu

Eric Sanderson

Robert Mitchell Smith

Jordan Dale Andersen

David Bastien

Calvin Kent Carter

Tim Franklin

Yang Liu

Fan Yang

Zicheng Liu

Xin Tong

David G Molyneaux

Eric Chang

Hsiang-Tao Wu

Embodiments related to dynamically adjusting a user interface based upon depth information are disclosed. For example, one disclosed embodiment provides a method including receiving depth information of a physical space from a depth camera, locating a user within the physical space from the depth information, determining a distance between the user and a display device from the depth information, and adjusting one or more features of a user interface displayed on the display device based on the distance.

Dynamic adjustment of user interface

Aspects of the subject disclosure are directed towards providing feedback to users of multi-user system that has biometric recognition capabilities, so that a user knows whether the system has correctly associated the user with his or her identity. The feedback may include a display of a current camera view, along with visible identity information that is associated with each user in the view. The feedback may include per-user icons (e.g., tiles, thumbnail images and so on) by which a user visually confirms that he or she is correctly recognized. Any misrecognition may be detected via the feedback and corrected. Feedback may convey other information, such as the current interaction state/capabilities of a user.

Correlated display of biometric identity, feedback and user interaction state

A method to decode natural user input from a human subject. The method includes detection of a gesture and concurrent grip state of the subject. If the grip state is closed during the gesture, then a user-interface (UI) canvas of the computer system is transformed based on the gesture. If the grip state is open during the gesture, then a UI object arranged on the UI canvas is activated based on the gesture.

Gesture library for natural user input

Embodiments are disclosed that relate to providing feedback for a level of completion of a user gesture via a cursor displayed on a user interface. One disclosed embodiment provides a method comprising displaying a cursor having a visual property and moving a screen-space position of the cursor responsive to the user gesture. The method further comprises changing the visual property of the cursor in proportion to a level of completion of the user gesture. In this way, the level of completion of the user gesture may be presented to the user in a location to which the attention of the user is directed during performance of the gesture.

Visual feedback for level of gesture completion

Users move their hands in a three dimensional ('3D') physical interaction zone ('PHIZ') to control a cursor in a user interface (&#x201c;UI&#x201d;) shown on a computer-coupled 2D display such as a television or monitor. The PHIZ is shaped, sized, and positioned relative to the user to ergonomically match the user's natural range of motions so that cursor control is intuitive and comfortable over the entire region on the UI that supports cursor interaction. A motion capture system tracks the user's hand so that the user's 3D motions within the PHIZ can be mapped to the 2D UI. Accordingly, when the user moves his or her hands in the PHIZ, the cursor correspondingly moves on the display. Movement in the z direction (i.e., back and forth) in the PHIZ allows for additional interactions to be performed such as pressing, zooming, 3D manipulations, or other forms of input to the UI.

Ergonomic physical interaction zone cursor mapping

A method for constructing an avatar of a human subject includes acquiring a depth map of the subject, obtaining a virtual skeleton of the subject based on the depth map, and harvesting from the virtual skeleton a set of characteristic metrics. Such metrics correspond to distances between predetermined points of the virtual skeleton. In this example method, the characteristic metrics are provided as input to an algorithm trained using machine learning. The algorithm may be trained using a human model in a range of poses, and a range of human models in a single pose, to output a virtual body mesh as a function of the characteristic metrics. The method also includes constructing a virtual head mesh distinct from the virtual body mesh, with facial features resembling those of the subject, and connecting the virtual body mesh to the virtual head mesh.

Avatar construction using depth camera

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Seattle, WA, United States of America

Emily Yang