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    PREDICTING A POSITION OF AN OBJECT OVER TIME
    1.
    发明申请
    PREDICTING A POSITION OF AN OBJECT OVER TIME 有权

    公开(公告)号：US20250117959A1

    公开(公告)日：2025-04-10

    申请号：US18480665

    申请日：2023-10-04

    Applicant: Shanghai United Imaging Intelligence Co., Ltd.

    Inventor： Benjamin Planche ,  Ziyan Wu ,  Meng Zheng ,  Zhongpai Gao ,  Abhishek Sharma

    IPC: G06T7/70

    Abstract: Multiple predictions about the position of an object during a time period may each indicate the position of the object at a respective time during the time period. Respective validity indications corresponding to the multiple predictions may each indicate an accuracy of the corresponding prediction. Whether a change has occurred in a distribution of the predictions from a first subset of predictions to a second subset of predictions during the time period may be determined. If the change has occurred, a prediction from the first subset of predictions or the second subset of predictions may be selected, based on the validity of the predictions and/or the detection of a motion, as a best indication of the position of the object.

    Human model recovery using deep learning techniques
    3.
    发明授权
    Human model recovery using deep learning techniques 有权

    公开(公告)号：US12136235B2

    公开(公告)日：2024-11-05

    申请号：US17559364

    申请日：2021-12-22

    Applicant: Shanghai United Imaging Intelligence Co., Ltd.

    Inventor： Meng Zheng ,  Srikrishna Karanam ,  Ziyan Wu

    IPC: G06T7/73 ,  G06N3/045 ,  G06T7/50 ,  G06V40/10

    Abstract: Human model recovery may be realized utilizing pre-trained artificially neural networks. A first neural network may be trained to determine body keypoints of a person based on image(s) of a person. A second neural network may be trained to predict pose parameters associated with the person based on the body keypoints. A third neural network may be trained to predict shape parameters associated with the person based on depth image(s) of the person. A 3D human model may then be generated based on the pose and shape parameters respectively predicted by the second and third neural networks. The training of the second neural network may be conducted using synthetically generated body keypoints and the training of the third neural network may be conducted using normal maps. The pose and shape parameters predicted by the second and third neural networks may be further optimized through an iterative optimization process.

    SYSTEMS AND METHODS FOR PERSONALIZED PATIENT BODY MODELING
    8.
    发明申请
    SYSTEMS AND METHODS FOR PERSONALIZED PATIENT BODY MODELING 有权

    公开(公告)号：US20230132479A1

    公开(公告)日：2023-05-04

    申请号：US17513392

    申请日：2021-10-28

    Applicant: Shanghai United Imaging Intelligence Co., Ltd.

    Inventor： Srikrishna Karanam ,  Meng Zheng ,  Ziyan Wu

    IPC: G06T13/40 ,  G06T17/20

    Abstract: A three-dimensional (3D) model of a person may be obtained using a pre-trained neural network based on one or more images of the person. Such a model may be subject to estimation bias and/or other types of defects or errors. Described herein are systems, methods, and instrumentalities for refining the 3D model and/or the neural network used to generate the 3D model. The proposed techniques may extract information such as key body locations and/or a body shape from the images and refine the 3D model and/or the neural network using the extracted information. In examples, the 3D model and/or the neural network may be refined by minimizing a difference between the key body locations and/or body shape extracted from the images and corresponding key body locations and/or body shape determined from the 3D model. The refinement may be performed in an iterative and alternating manner.

    Systems and methods for human model recovery
    10.
    发明授权
    Systems and methods for human model recovery 有权

    公开(公告)号：US12183019B2

    公开(公告)日：2024-12-31

    申请号：US17994696

    申请日：2022-11-28

    Applicant: Shanghai United Imaging Intelligence Co., Ltd.

    Inventor： Srikrishna Karanam ,  Meng Zheng ,  Ziyan Wu

    IPC: G06T7/50 ,  G06N3/02

    Abstract: A human model such as a 3D human mesh may be generated for a person in a medical environment based on one or more images of the person. The images may be captured using a sensing device that may be attached to an existing medical device such as a medical scanner in the medical environment. Such an arrangement may ensure that unblocked views of the person (e.g., body keypoints of the person) may be obtained and used to generate the human model. The position of the medical device in the medical environment may be determined and used to facilitate the human model construction such that the pose and body shape of the person in the medical environment may be accurately represented by the human model.

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