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    Hand gesture recognition based on detected wrist muscular movements
    22.
    发明授权
    Hand gesture recognition based on detected wrist muscular movements 有权

    公开(公告)号：US11592908B2

    公开(公告)日：2023-02-28

    申请号：US17249966

    申请日：2021-03-19

    Applicant: GOOGLE LLC

    Inventor： Dongeek Shin ,  Shahram Izadi ,  David Kim ,  Sofien Bouaziz ,  Steven Benjamin Goldberg ,  Ivan Poupyrev ,  Shwetak N. Patel

    IPC: G06F3/01 ,  G06F1/16 ,  G06V40/20

    Abstract: Techniques of identifying gestures include detecting and classifying inner-wrist muscle motions at a user's wrist using micron-resolution radar sensors. For example, a user of an AR system may wear a band around their wrist. When the user makes a gesture to manipulate a virtual object in the AR system as seen in a head-mounted display (HMD), muscles and ligaments in the user's wrist make small movements on the order of 1-3 mm. The band contains a small radar device that has a transmitter and a number of receivers (e.g., three) of electromagnetic (EM) radiation on a chip (e.g., a Soli chip. This radiation reflects off the wrist muscles and ligaments and is received by the receivers on the chip in the band. The received reflected signal, or signal samples, are then sent to processing circuitry for classification to identify the wrist movement as a gesture.

    METHODS, SYSTEMS, AND MEDIA FOR RELIGHTING IMAGES USING PREDICTED DEEP REFLECTANCE FIELDS
    25.
    发明申请
    METHODS, SYSTEMS, AND MEDIA FOR RELIGHTING IMAGES USING PREDICTED DEEP REFLECTANCE FIELDS 审中-公开

    公开(公告)号：US20200372284A1

    公开(公告)日：2020-11-26

    申请号：US16616235

    申请日：2019-10-16

    Applicant: Google LLC

    Inventor： Christoph Rhemann ,  Abhimitra Meka ,  Matthew Whalen ,  Jessica Lynn Busch ,  Sofien Bouaziz ,  Geoffrey Douglas Harvey ,  Andrea Tagliasacchi ,  Jonathan Taylor ,  Paul Debevec ,  Peter Joseph Denny ,  Sean Ryan Francesco Fanello ,  Graham Fyffe ,  Jason Angelo Dourgarian ,  Xueming Yu ,  Adarsh Prakash Murthy Kowdle ,  Julien Pascal Christophe Valentin ,  Peter Christopher Lincoln ,  Rohit Kumar Pandey ,  Christian Häne ,  Shahram Izadi

    IPC: G06K9/46 ,  G06T15/50 ,  G06K9/62 ,  G06N3/08 ,  G06T15/20

    Abstract: Methods, systems, and media for relighting images using predicted deep reflectance fields are provided. In some embodiments, the method comprises: identifying a group of training samples, wherein each training sample includes (i) a group of one-light-at-a-time (OLAT) images that have each been captured when one light of a plurality of lights arranged on a lighting structure has been activated, (ii) a group of spherical color gradient images that have each been captured when the plurality of lights arranged on the lighting structure have been activated to each emit a particular color, and (iii) a lighting direction, wherein each image in the group of OLAT images and each of the spherical color gradient images are an image of a subject, and wherein the lighting direction indicates a relative orientation of a light to the subject; training a convolutional neural network using the group of training samples, wherein training the convolutional neural network comprises: for each training iteration in a series of training iterations and for each training sample in the group of training samples: generating an output predicted image, wherein the output predicted image is a representation of the subject associated with the training sample with lighting from the lighting direction associated with the training sample; identifying a ground-truth OLAT image included in the group of OLAT images for the training sample that corresponds to the lighting direction for the training sample; calculating a loss that indicates a perceptual difference between the output predicted image and the identified ground-truth OLAT image; and updating parameters of the convolutional neural network based on the calculated loss; identifying a test sample that includes a second group of spherical color gradient images and a second lighting direction; and generating a relit image of the subject included in each of the second group of spherical color gradient images with lighting from the second lighting direction using the trained convolutional neural network.

    DEFORMABLE NEURAL RADIANCE FIELDS
    30.
    发明公开
    DEFORMABLE NEURAL RADIANCE FIELDS 审中-公开

    公开(公告)号：US20240005590A1

    公开(公告)日：2024-01-04

    申请号：US18251995

    申请日：2021-01-14

    Applicant: GOOGLE LLC

    Inventor： Ricardo Martin Brualla ,  Keunhong Park ,  Utkarsh Sinha ,  Sofien Bouaziz ,  Daniel Goldman ,  Jonathan Tilton Barron ,  Steven Maxwell Seitz

    IPC: G06T15/20 ,  G06T15/55 ,  G06T15/04

    CPC classification number: G06T15/20 ,  G06T15/55 ,  G06T15/04

    Abstract: Techniques of image synthesis using a neural radiance field (NeRF) includes generating a deformation model of movement experienced by a subject in a non-rigidly deforming scene. For example, when an image synthesis system uses NeRFs, the system takes as input multiple poses of subjects for training data. In contrast to conventional NeRFs, the technical solution first expresses the positions of the subjects from various perspectives in an observation frame. The technical solution then involves deriving a deformation model, i.e., a mapping between the observation frame and a canonical frame in which the subject's movements are taken into account. This mapping is accomplished using latent deformation codes for each pose that are determined using a multilayer perceptron (MLP). A NeRF is then derived from positions and casted ray directions in the canonical frame using another MLP. New poses for the subject may then be derived using the NeRF.

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