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公开(公告)号：US12236635B1
公开(公告)日：2025-02-25
申请号：US18809315
申请日：2024-08-19
Inventor： Huapeng Sima , Hao Jiang , Hongwei Fan , Qixun Qu , Jiabin Li , Jintai Luan
Abstract: This application provides a digital person training method and system, and a digital person driving system. According to the method, human-body pose estimation data in training data is extracted, and the human-body pose estimation data is input into an optimized pose estimation network to obtain human-body pose optimization data. Generation losses of position optimization data and acceleration optimization data in the human-body pose optimization data are calculated based on a loss function of the optimized pose estimation network, so as to minimize errors between position estimation data and acceleration estimation data and a real value. In this way, the optimized pose estimation network is driven to update a network parameter to obtain an optimal driving model that is based on the optimized pose estimation network. The errors between the position estimation data and the acceleration estimation data and the real value are minimized.
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2.发明授权公开(公告)号：US11854306B1
公开(公告)日：2023-12-26
申请号：US18343334
申请日：2023-06-28
Inventor： Huapeng Sima , Hao Jiang , Hongwei Fan , Qixun Qu , Jintai Luan , Jiabin Li
IPC: G06V40/20 , G06V10/77 , G06V10/764
CPC classification number: G06V40/23 , G06V10/764 , G06V10/7715 , G06V2201/12
Abstract: A model including an information extraction layer that obtains image information of a training object in a depth image; a pixel point positioning layer that performs position estimation on a three-dimensional coordinate of human-body key points, defines a body part of the training object as a body component, and calibrates a three-dimensional coordinate of all human-body key points corresponding to the body component; a feature extraction layer that extracts a key-point position feature, a body moving speed feature, and a key-point moving speed feature for action recognition; a vector dimensionality reduction layer that combines the key-point position feature, the body moving speed feature, and the key-point moving speed feature as a multidimensional feature vector, and performs dimensionality reduction on the multidimensional feature vector; and a feature vector classification layer that classifies the multidimensional feature vector that is performed with dimensionality reduction, to recognize a fitness action of the training object.
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公开(公告)号：US20250086826A1
公开(公告)日：2025-03-13
申请号：US18809315
申请日：2024-08-19
Inventor： Huapeng Sima , Hao Jiang , Hongwei Fan , Qixun Qu , Jiabin Li , Jintai Luan
Abstract: This application provides a digital person training method and system, and a digital person driving system. According to the method, human-body pose estimation data in training data is extracted, and the human-body pose estimation data is input into an optimized pose estimation network to obtain human-body pose optimization data. Generation losses of position optimization data and acceleration optimization data in the human-body pose optimization data are calculated based on a loss function of the optimized pose estimation network, so as to minimize errors between position estimation data and acceleration estimation data and a real value. In this way, the optimized pose estimation network is driven to update a network parameter to obtain an optimal driving model that is based on the optimized pose estimation network. The errors between the position estimation data and the acceleration estimation data and the real value are minimized.
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公开(公告)号：US12094046B1
公开(公告)日：2024-09-17
申请号：US18419759
申请日：2024-01-23
Inventor： Huapeng Sima , Jintai Luan , Hongwei Fan , Jiabin Li , Hao Jiang , Qixun Qu
CPC classification number: G06T13/40 , G06T7/80 , G06T19/00 , G06T2207/10016 , G06T2207/30196
Abstract: A digital human driving method and apparatus are provided which relate to computer and image processing, and can solve the problem of shaking, joint rotation malposition and partial loss of a digital human during a driving process. The solution includes: capturing video data from multiple angles of view in a real three-dimensional space by multiple video capture devices; determining a first coordinate of a key point of the target human; determining a mapping relationship based on the first coordinate; calculating a second coordinate based on the mapping relationship and the first coordinate; processing the second coordinate according to a key point rotation model to obtain rotation value of the virtual key point in the virtual three-dimensional space; and driving the digital human to move based on the rotation value of the virtual key point in the virtual three-dimensional space.
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