Method and apparatus for training pose recognition model, and method and apparatus for image recognition

A computer device, comprising:

• a memory, configured to store executable instructions;
• and
• a processor, configured to perform, when executing the executable instructions stored in the memory, a plurality of operations including:
• processing, by using a feature map model comprised in a pose recognition model, a sample image labeled with human body key points, to obtain a feature map of the sample image;
• processing the feature map of the sample image by using a two-dimensional (2D) model comprised in the pose recognition model, to obtain 2D key point parameters used for representing a 2D human body pose;
• processing, by using a three-dimensional (3D) model comprised in the pose recognition model, a target human body feature map cropped from the feature map and the 2D key point parameters, to obtain 3D pose parameters used for representing a 3D human body pose;
• constructing a target loss function based on the 2D key point parameters and the 3D pose parameters;
• and
• updating the pose recognition model based on the target loss function.

A non-transitory computer-readable storage medium, storing executable instructions that, when executed by a processor of a computer device, cause the computer device to perform a plurality of operations including:

• processing, by using a feature map model comprised in a pose recognition model, a sample image labeled with human body key points, to obtain a feature map of the sample image;
• processing the feature map of the sample image by using a two-dimensional (2D) model comprised in the pose recognition model, to obtain 2D key point parameters used for representing a 2D human body pose;
• processing, by using a three-dimensional (3D) model comprised in the pose recognition model, a target human body feature map cropped from the feature map and the 2D key point parameters, to obtain 3D pose parameters used for representing a 3D human body pose;
• constructing a target loss function based on the 2D key point parameters and the 3D pose parameters;
• and
• updating the pose recognition model based on the target loss function.