FULL BODY POSE ESTIMATION THROUGH FEATURE EXTRACTION FROM MULTIPLE WEARABLE DEVICES

    公开(公告)号:US20230101617A1

    公开(公告)日:2023-03-30

    申请号:US17951943

    申请日:2022-09-23

    Applicant: Apple Inc.

    Abstract: Embodiments are disclosed for full body pose estimation using features extracted from multiple wearable devices. In an embodiment, a method comprises: obtaining point of view (POV) video data and inertial sensor data from multiple wearable devices worn at the same time by a user; obtaining depth data capturing the user's full body; extracting two-dimensional (2D) keypoints from the POV video data; reconstructing a full body 2D skeletal model from the 2D keypoints; generating a three-dimensional (3D) mesh model of the user's full body based on the depth data; merging nodes of the 3D mesh model with the inertial sensor data; aligning respective orientations of the 2D skeletal model and the 3D mesh model in a common reference frame; and predicting, using a machine learning model, classification types based on the aligned 2D skeletal model and 3D mesh model.

    Full body pose estimation through feature extraction from multiple wearable devices

    公开(公告)号:US12154367B2

    公开(公告)日:2024-11-26

    申请号:US17951943

    申请日:2022-09-23

    Applicant: Apple Inc.

    Abstract: Embodiments are disclosed for full body pose estimation using features extracted from multiple wearable devices. In an embodiment, a method comprises: obtaining point of view (POV) video data and inertial sensor data from multiple wearable devices worn at the same time by a user; obtaining depth data capturing the user's full body; extracting two-dimensional (2D) keypoints from the POV video data; reconstructing a full body 2D skeletal model from the 2D keypoints; generating a three-dimensional (3D) mesh model of the user's full body based on the depth data; merging nodes of the 3D mesh model with the inertial sensor data; aligning respective orientations of the 2D skeletal model and the 3D mesh model in a common reference frame; and predicting, using a machine learning model, classification types based on the aligned 2D skeletal model and 3D mesh model.

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