A region of space may be monitored for the presence or absence of one or more control objects, and object attributes and changes thereto may be interpreted as control information provided as input to a machine or application. In some embodiments, the region is monitored using a combination of scanning and image-based sensing.

A region of space may be monitored for the presence or absence of one or more control objects, and object attributes and changes thereto may be interpreted as control information provided as input to a machine or application. In some embodiments, the region is monitored using a combination of scanning and image-based sensing.

There is provided a recognition system adaptable to a portable device or a wearable device. The recognition system senses a body heat using a thermal sensor, and performs functions such as the living body recognition, image denoising and body temperature prompting according to detected results.

A gesture analysis method and a device and a non-transitory computer-readable storage medium are provided. The method includes: performing a feature extraction process on an image, and obtaining a first amount of features of finger key points and a second amount of features of palm key points; performing a UV-coordinate regression process and a depth regression process on each of the features of the finger key points and the palm key points, and obtaining a first UV coordinate and a first depth coordinate of each of the finger key points, and a second UV coordinate and a second depth coordinate of each of the palm key points; and performing a gesture analysis on the image according to the first UV coordinate, and the first depth coordinate, the second UV coordinate, and the second depth coordinate, and obtaining a gesture analysis result.

Example systems, devices, media, and methods are described for capturing still images in response to hand gestures detected by an eyewear device that is capturing frames of video data with its camera system. A localization system determines the eyewear location relative to the physical environment. An image processing system detects a hand shape in the video data and determines whether the detected hand shape matches a border gesture or a shutter gesture. In response to a border gesture, the system establishes a border that defines the still image to be captured. In response to a shutter gesture, the system captures a still image from the frames of video data. The system determines a shutter gesture location relative to the physical environment. The captured still image is presented on the display at or near the shutter gesture location, such that the still image appears anchored relative to the physical environment. The captured still image is viewable by other devices that are using the image capture system.

An information processing apparatus includes a processor configured to: acquire a target video showing a target of an instruction, and fingertip information indicating a hand motion of a user; overlay the target video with a fingertip object indicated by the fingertip information; and present the fingertip object while changing a representation of the fingertip object based on input information included in the fingertip information or input by the user.

Configurations are disclosed for presenting virtual reality and augmented reality experiences to users. The system may comprise an image capturing device to capture one or more images, the one or more images corresponding to a field of the view of a user of a head-mounted augmented reality device, and a processor communicatively coupled to the image capturing device to extract a set of map points from the set of images, to identify a set of sparse points and a set of dense points from the extracted set of map points, and to perform a normalization on the set of map points.

Disclosed herein is a detector for gesture detection including an illumination source configured for projecting an illumination pattern including a plurality of illumination features on an area including an object, where the object includes at least partially a human hand.

A generative network may be learned in an adversarial setting with a goal of modifying synthetic data such that a discriminative network may not be able to reliably tell the difference between refined synthetic data and real data. The generative network and discriminative network may work together to learn how to produce more realistic synthetic data with reduced computational cost. The generative network may iteratively learn a function that synthetic data with a goal of generating refined synthetic data that is more difficult for the discriminative network to differentiate from real data, while the discriminative network may be configured to iteratively learn a function that classifies data as either synthetic or real. Over multiple iterations, the generative network may learn to refine the synthetic data to produce refined synthetic data on which other machine learning models may be trained.

A multi-modal emotion recognition system is disclosed. The system includes a data input unit for receiving video data and voice data of a user, a data pre-processing unit including a voice pre-processing unit for generating voice feature data from the voice data and a video pre-processing unit for generating one or more face feature data from the video data, a preliminary inference unit for generating situation determination data as to whether or not the user's situation changes according to a temporal sequence based on the video data. The system further comprises a main inference unit for generating at least one sub feature map based on the voice feature data or the face feature data, and inferring the user's emotion state based on the sub feature map and the situation determination data.