Abstract: Systems and methods for detecting tampering with bystander privacy indicators are disclosed. A method performed at a head-wearable device worn by a user includes detecting, via an ambient light sensor of a head-wearable device, a first amount of light in an environment, and detect, via the imaging device of the head-wearable device, a second amount of light in the environment. The method further includes, responsive to user input for capturing image data via the imaging device of the head-wearable device, determining a brightness ratio. The brightness ratio is based on a relationship between the first amount of light and the second amount of light in the environment. The method includes, in accordance with a determination that the brightness ratio satisfies a tamper threshold, capturing image data; and, in accordance with a determination that the brightness ratio does not satisfy the tamper threshold, forgoing capturing image data.

Abstract: Aspects of the present disclosure are directed to obscuring objects and faces in data streams using machine learning. A data stream captured at a client device associated with a user can be processed to recognize objects in the data stream. Based on user preferences that define object sharing rules, one or more of the recognized objects can be obscured from the data stream. For example, when user's data stream is displayed to other users, such as during a video broadcast, objects in the user's data stream can be obscured. User preferences and object sharing rules can be defined using a preview of the data stream and explicit input from the user with respect to recognized objects in the preview. Other example user preferences and sharing rules include rules for recognized faces, rules relative to an object's location in the data stream, rules or objects in motion, etc.

Abstract: An adjustable-focus PV (picture/video) camera in a mixed-reality head-mounted display (HMD) device operates with an auto-focus subsystem that is configured to be triggered based on location and motion of a user's hands to reduce the occurrence of auto-focus hunting during camera operations. The HMD device is equipped with a depth sensor that is configured to capture depth data from the surrounding physical environment to detect and track the user's hand location, movements, and gestures in three-dimensions. The hand tracking data from the depth sensor may be assessed to determine hand characteristics—such as which of the user's hands or part of a hand is detected, its size, motion, speed, etc.—within a particular region of interest (ROI) in the field of view of the PV camera. The auto-focus subsystem uses the assessed hand characteristics as an input to control auto-focus of the PV camera to reduce auto-focus hunting occurrences.

Abstract: An adjustable-focus PV (picture/video) camera in a mixed-reality head-mounted display (HMD) device operates with an auto-focus subsystem that is configured to be triggered based on location and motion of a user's hands to reduce the occurrence of auto-focus hunting during camera operations. The HMD device is equipped with a depth sensor that is configured to capture depth data from the surrounding physical environment to detect and track the user's hand location, movements, and gestures in three-dimensions. The hand tracking data from the depth sensor may be assessed to determine hand characteristics—such as which of the user's hands or part of a hand is detected, its size, motion, speed, etc.—within a particular region of interest (ROI) in the field of view of the PV camera. The auto-focus subsystem uses the assessed hand characteristics as an input to control auto-focus of the PV camera to reduce auto-focus hunting occurrences.

Abstract: An adjustable-focus PV (picture/video) camera in a mixed-reality head-mounted display (HMD) device operates with an auto-focus subsystem that is configured to be triggered based on location and motion of a user's hands to reduce the occurrence of auto-focus hunting during camera operations. The HMD device is equipped with a depth sensor that is configured to capture depth data from the surrounding physical environment to detect and track the user's hand location, movements, and gestures in three-dimensions. The hand tracking data from the depth sensor may be assessed to determine hand characteristics—such as which of the user's hands or part of a hand is detected, its size, motion, speed, etc.—within a particular region of interest (ROI) in the field of view of the PV camera. The auto-focus subsystem uses the assessed hand characteristics as an input to control auto-focus of the PV camera to reduce auto-focus hunting occurrences.

Abstract: An adjustable-focus PV (picture/video) camera in a mixed-reality head-mounted display (HMD) device operates with an auto-focus subsystem that is configured to be triggered based on location and motion of a user's hands to reduce the occurrence of auto-focus hunting during camera operations. The HMD device is equipped with a depth sensor that is configured to capture depth data from the surrounding physical environment to detect and track the user's hand location, movements, and gestures in three-dimensions. The hand tracking data from the depth sensor may be assessed to determine hand characteristics—such as which of the user's hands or part of a hand is detected, its size, motion, speed, etc.—within a particular region of interest (ROI) in the field of view of the PV camera. The auto-focus subsystem uses the assessed hand characteristics as an input to control auto-focus of the PV camera to reduce auto-focus hunting occurrences.

Abstract: An adjustable-focus PV (picture/video) camera in a mixed-reality head-mounted display (HMD) device operates with an auto-focus subsystem that is configured to be triggered based on location and motion of a user's hands to reduce the occurrence of auto-focus hunting during camera operations. The HMD device is equipped with a depth sensor that is configured to capture depth data from the surrounding physical environment to detect and track the user's hand location, movements, and gestures in three-dimensions. The hand tracking data from the depth sensor may be assessed to determine hand characteristics—such as which of the user's hands or part of a hand is detected, its size, motion, speed, etc.—within a particular region of interest (ROI) in the field of view of the PV camera. The auto-focus subsystem uses the assessed hand characteristics as an input to control auto-focus of the PV camera to reduce auto-focus hunting occurrences.