Ultrahaptics IP Two Limited

Apple Inc.

Leap Motion, Inc.

Other

Raffi A Bedikian

David S Holz

Gabriel Archacki Hare

Jonathan Marsden

Hua Yang

Kevin A Horowitz

Maxwell Andrew Sills

Matias Perez

Keith Mertens

Adrian Gasinski

Ian M Richter

Gutemberg B Guerra Filho

Thomas Gebauer

Ryan Julian

Daniel R Kurz

Mohamed Selim Ben Himane

Li Jia

Branko Petljanski

Pedro Manuel Da Silva Quelhas

Moinul H Khan

Hongyuan (Jimmy) He

Katharina Buckl

Jim Chen Chou

Tobias Eble

Michele Stoppa

Sai Harsha Jandhyala

Julien Monat Rodier

During control of a user interface via free-space motions of a hand or other suitable control object, switching between control modes can be facilitated by tracking the control object's movements relative to, and its penetration of, a virtual control construct (such as a virtual surface construct). The technology disclosed includes determining from the motion information whether a motion of the control object with respect to the virtual control construct is an engagement gesture, such as a virtual mouse click or other control device operation. The position of the virtual control construct can be updated, continuously or from time to time, based on the control object's location.

Free-space user interface and control using virtual constructs

The technology disclosed relates to relates to providing command input to a machine under control. It further relates to gesturally interacting with the machine. The technology disclosed also relates to providing monitoring information about a process under control. The technology disclosed further relates to providing biometric information about an individual. The technology disclosed yet further relates to providing abstract features information (pose, grab strength, pinch strength, confidence, and so forth) about an individual.

Systems and methods of tracking moving hands and recognizing gestural interactions

Various implementations disclosed herein include devices, systems, and methods that identify a gesture based on event camera data and frame-based camera data (e.g., for a CGR environment). In some implementations at an electronic device having a processor, event camera data is obtained corresponding to light (e.g., IR light) reflected from a physical environment and received at an event camera. In some implementations, frame-based camera data is obtained corresponding to light (e.g., visible light) reflected from the physical environment and received at a frame-based camera. In some implementations, a subset of the event camera data is identified based on the frame-based camera data, and a gesture (e.g., of a person in the physical environment) is identified based on the subset of event camera data. In some implementations, a path (e.g., of a hand) by tracking a grouping of blocks of event camera events in the subset of event camera data.

Gesture tracking system

The technology disclosed relates to simplifying updating of a predictive model using clustering observed points. In particular, it relates to observing a set of points in 3D sensory space, determining surface normal directions from the points, clustering the points by their surface normal directions and adjacency, accessing a predictive model of a hand, refining positions of segments of the predictive model, matching the clusters of the points to the segments, and using the matched clusters to refine the positions of the matched segments. It also relates to distinguishing between alternative motions between two observed locations of a control object in a 3D sensory space by accessing first and second positions of a segment of a predictive model of a control object such that motion between the first position and the second position was at least partially occluded from observation in a 3D sensory space.

Predictive information for free space gesture control and communication

The technology disclosed also initializes a new hand that enters the field of view of a gesture recognition system using a parallax detection module. The parallax detection module determines candidate regions of interest (ROI) for a given input hand image and computes depth, rotation and position information for the candidate ROI. Then, for each of the candidate ROI, an ImagePatch, which includes the hand, is extracted from the original input hand image to minimize processing of low-information pixels. Further, a hand classifier neural network is used to determine which ImagePatch most resembles a hand. For the qualified, most-hand like ImagePatch, a 3D virtual hand is initialized with depth, rotation and position matching that of the qualified ImagePatch.

Hand initialization for machine learning based gesture recognition

The technology disclosed performs hand pose estimation on a so-called 'joint-by-joint' basis. So, when a plurality of estimates for the 28 hand joints are received from a plurality of expert networks (and from master experts in some high-confidence scenarios), the estimates are analyzed at a joint level and a final location for each joint is calculated based on the plurality of estimates for a particular joint. This is a novel solution discovered by the technology disclosed because nothing in the field of art determines hand pose estimates at such granularity and precision. Regarding granularity and precision, because hand pose estimates are computed on a joint-by-joint basis, this allows the technology disclosed to detect in real time even the minutest and most subtle hand movements, such a bend/yaw/tilt/roll of a segment of a finger or a tilt an occluded finger, as demonstrated supra in the Experimental Results section of this application.

Hand pose estimation for machine learning based gesture recognition

The technology disclosed introduces two types of neural networks: 'master' or “generalists” networks and “expert” or “specialists” networks. Both, master networks and expert networks, are fully connected neural networks that take a feature vector of an input hand image and produce a prediction of the hand pose. Master networks and expert networks differ from each other based on the data on which they are trained. In particular, master networks are trained on the entire data set. In contrast, expert networks are trained only on a subset of the entire dataset. In regards to the hand poses, master networks are trained on the input image data representing all available hand poses comprising the training data (including both real and simulated hand images).

Machine learning based gesture recognition

Embodiments of display control based on dynamic user interactions generally include capturing a plurality of temporally sequential images of the user, or a body part or other control object manipulated by the user, and computationally analyzing the images to recognize a gesture performed by the user. In some embodiments, the gesture is identified as an engagement gesture, and compared with reference gestures from a library of reference gestures. In some embodiments, a degree of completion of the recognized engagement gesture is determined, and the display contents are modified in accordance therewith. In some embodiments, a dominant gesture is computationally determined from among a plurality of user gestures, and an action displayed on the device is based on the dominant gesture.

Dynamic, free-space user interactions for machine control

The technology disclosed relates to manipulating a virtual object. In particular, it relates to detecting a hand in a three-dimensional (3D) sensory space and generating a predictive model of the hand, and using the predictive model to track motion of the hand. The predictive model includes positions of calculation points of fingers, thumb and palm of the hand. The technology disclosed relates to dynamically selecting at least one manipulation point proximate to a virtual object based on the motion tracked by the predictive model and positions of one or more of the calculation points, and manipulating the virtual object by interaction between at least some of the calculation points of the predictive model and the dynamically selected manipulation point.

Interactions with virtual objects for machine control

The technology disclosed relates to providing simplified manipulation of virtual objects by detected hand motions. In particular, it relates to a detecting hand motion and positions of the calculation points relative to a virtual object to be manipulated, dynamically selecting at least one manipulation point proximate to the virtual object based on the detected hand motion and positions of one or more of the calculation points, and manipulating the virtual object by interaction between the detected hand motion and positions of one or more of the calculation points and the dynamically selected manipulation point.

Virtual interactions for machine control

In one implementation, a method includes emitting light with modulating intensity from a plurality of light sources towards an eye of a user. The method includes receiving light intensity data indicative of an intensity of the plurality of glints reflected by the eye of the user in the form of a plurality of glints. The method includes determining an eye tracking characteristic of the user based on the light intensity data. In one implementation, a method includes generating, using an event camera comprising a plurality of light sensors at a plurality of respective locations, a plurality of event messages, each of the plurality of event messages being generated in response to a particular light sensor detecting a change in intensity of light and indicating a particular location of the particular light sensor. The method includes determining an eye tracking characteristic of a user based on the plurality of event messages.

Method and device for eye tracking using event camera data

In some implementations, a method includes: identifying a plurality of subsets associated with a physical environment; determining a set of spatial characteristics for each of the plurality of subsets, wherein a first set of spatial characteristics characterizes dimensions of a first subset and a second set of spatial characteristics characterizes dimensions of a second subset; generating an adapted first extended reality (XR) content portion for the first subset based at least in part on the first set of spatial characteristics; generating an adapted second XR content portion for the second subset based at least in part on the second set of spatial characteristics; and generating one or more navigation options that allow a user to traverse between the first and second subsets based on the first and second sets of spatial characteristics.

Generating adapted virtual content to spatial characteristics of a physical setting

During control of a user interface via free-space motions of a hand or other suitable control object, switching between control modes can be facilitated by tracking the control object's movements relative to, and its penetration of, a virtual control construct (such as a virtual surface construct). The position of the virtual control construct can be updated, continuously or from time to time, based on the control object's location.

Growing community of inventors

Mountain View, CA, United States of America

Raffi A Bedikian