Nanjing Silicon Intelligence Technology Co., Ltd.

Raycan Technology Co., Ltd (su Zhou)

Hao Jiang

Huapeng Sima

Qixun Qu

Jiabin Li

Hongwei Fan

Jintai Luan

Qingguo Xie

Li Lin

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.

Digital human driving method and apparatus, and storage medium

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.

Fitness action recognition model, method of training model, and method of recognizing fitness action

A combined scintillation crystal includes: at least one scintillation crystal A module and a scintillation crystal B module. The scintillation crystal A module and the scintillation crystal B module are scintillation crystal modules with different performances. The scintillation crystal A module comprises at least one scintillation crystal A, and the scintillation crystal B module comprises at least one scintillation crystal B. The sensitivity of the scintillation crystal A is lower than the sensitivity of the scintillation crystal B, and the light output ability of the scintillation crystal A is higher than the light output ability of the scintillation crystal B. The scintillation crystal B module includes a ray incidence plane for receiving rays, and the at least one scintillation crystal module A is arranged at the outer side of the ray incidence plane of the scintillation crystal B module.

Combined scintillation crystal, combined scintillation detector and radiation detection device

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Jiangsu, China

Hao Jiang