Abstract: A video application executing on a client device allows a user to modify video data captured by the client device. The video application allows the user to transmit the video data to a viewing client device for presentation to a viewing user. For example, the video application transmits the video data to an online system, which transmits the video data to the viewing client device. The viewing user provides a request to modify the video data to the viewing client device, which transmits the request to the online system. Based on the request from the viewing client device, the online system identifies a modification to the video data to the client device, which modifies the captured video data accordingly. The modified video data is provided from the client device to the viewing client device via the online system.

Abstract: An image processing system transforms content images into the style of another reference style image. For example, the system applies a noise mask to generate noisy versions of the content images. The system then recomposes a content image in the style of a reference image by applying computer models to the noisy version of the content image, which reduces artifacts in the stylized image compared to that of a stylized image generated by applying the computer models to the original content image. When the content images are part of a video sequence, the image processing system may adjust the noise mask applied in a subsequent frame such that it tracks the movement of the client device from the current frame to the subsequent frame. This allows the system to reduce artifacts while stylizing the frames of the video in a consistent manner.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may then be discarded to isolate one or more voxels associated with a foreground object such as a human target and the isolated voxels associated with the foreground object may be processed.

Abstract: A video application executing on a client device allows a user to modify video data captured by the client device. The video application allows the user to transmit the video data to a viewing client device for presentation to a viewing user. For example, the video application transmits the video data to an online system, which transmits the video data to the viewing client device. The viewing user provides a request to modify the video data to the viewing client device, which transmits the request to the online system. Based on the request from the viewing client device, the online system identifies a modification to the video data to the client device, which modifies the captured video data accordingly. The modified video data is provided from the client device to the viewing client device via the online system.

Abstract: An image processing system transforms content images into the style of another reference style image. For example, the system applies a noise mask to generate noisy versions of the content images. The system then recomposes a content image in the style of a reference image by applying computer models to the noisy version of the content image, which reduces artifacts in the stylized image compared to that of a stylized image generated by applying the computer models to the original content image. When the content images are part of a video sequence, the image processing system may adjust the noise mask applied in a subsequent frame such that it tracks the movement of the client device from the current frame to the subsequent frame. This allows the system to reduce artifacts while stylizing the frames of the video in a consistent manner.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may also be removed to isolate one or more voxels associated with a foreground object such as a human target. A location or position of one or more extremities of the isolated human target may then be determined.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may also be removed to isolate one or more voxels associated with a foreground object such as a human target. A location or position of one or more extremities of the isolated human target may be determined and a model may be adjusted based on the location or position of the one or more extremities.

Abstract: A method to identify a targeted object based on eye tracking and gesture recognition. The method is enacted in a compute system controlled by a user and operatively coupled to a machine vision system. In this method, the compute system receives, from the machine vision system, video imaging a head and pointer of the user. Based on the video, the compute system computes a geometric line of sight of the user, which is partly occluded by the pointer. Then, with reference to position data for one or more objects, the compute system identifies the targeted object, situated along the geometric line of sight.

Abstract: Techniques for automatically selecting from amongst multiple camera views available in a multi-camera system. In an aspect, one or more metrics are calculated for multiple camera views. The metrics may be used to identify one or more of the camera views as fulfilling a condition, in response to which a particular one of the camera views may be selected for display. For example, in a “select-better-view” strategy, the camera view associated with a more optimal image (e.g., better lighting, or absence of occlusion) is selected for display. In a “flag-diagnostic-view” strategy, the camera view associated with a less optimal image is displayed, e.g., to indicate that a particular camera may require user attention. Specific techniques for calculating metrics are further disclosed.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may also be removed to isolate one or more voxels associated with a foreground object such as a human target. A location or position of one or more extremities of the isolated human target may then be determined.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may then be discarded to isolate one or more voxels associated with a foreground object such as a human target and the isolated voxels associated with the foreground object may be processed.

Abstract: A computing system such as a game console maintains and updates a biometric profile of a user. In one aspect, biometric data of the user is continuously obtained from a sensor such as an infrared and visible light camera, and used to update the biometric profile using a machine learning process. In another aspect, a user is prompted to confirm his or her identify when multiple users are detected at the same time and/or when the user is detected with a confidence level which is below a threshold. A real-time image of the user being identified can be displayed on a user interface with user images associated with one or more accounts. In another aspect, the biometric profile is managed by a shell on the computing system, where the shell makes the biometric profile available to any of a number of applications on the computing system.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may then be discarded to isolate one or more voxels associated with a foreground object such as a human target and the isolated voxels associated with the foreground object may be processed.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may also be removed to isolate one or more voxels associated with a foreground object such as a human target. A location or position of one or more extremities of the isolated human target may then be determined.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A model may be adjusted based on a location or position of one or more extremities estimated or determined for a human target in the grid of voxels. The model may also be adjusted based on a default location or position of the model in a default pose such as a T-pose, a DaVinci pose, and/or a natural pose.

Abstract: A method comprises receiving one or more depth images from a depth camera, the depth images indicating a depth of a surface imaged by each pixel of the depth images. The method may further comprise identifying a human subject imaged by the depth images and recognizing within one or more of the depth images a beacon emitted from a control device. A position of the control device may be assessed in three dimensions, and the control device may be associated with the human subject based on a proximity of the control device to the human subject or other parameter of the control device with relation to the human subject.

Abstract: A method for biometric identification for use with a computing device is provided herein. The method includes capturing a temporal sequence of images of the face of a user at different locations within a three-dimensional interaction space. The method further includes extracting one or more face descriptors from the images and generating a biometric template compiling the face descriptors.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may also be removed to isolate one or more voxels associated with a foreground object such as a human target. A location or position of one or more extremities of the isolated human target may be determined and a model may be adjusted based on the location or position of the one or more extremities.

Abstract: A control device includes a housing. The control device also includes a wireless communicator interior the housing. The wireless communicator wirelessly sends commands for controlling an electronic device, such as a game console. The control device also includes a reflector positioned to reflect light directed at the housing.

Abstract: 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.