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

1. A method for training a pose recognition model, applicable to a computer device, the method comprising:

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.

2. The method according to claim 1 , wherein before the 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, the method further comprises:

determining the human body key points in a key point set according to a setting; and

labeling the sample image based on the human body key points and with reference to the key point set.

3. The method according to claim 2 , wherein the key point set comprises:

reference key points used for localization of human body parts and extension key points, which, together with the reference key points, represent a plurality of 3D poses of the parts to which the reference key points and the extension key points belong.

4. The method according to claim 1 , wherein the target loss function comprises a first loss function corresponding to the 3D model; and the constructing a target loss function based on the 2D key point parameters and the 3D pose parameters comprises:

determining corresponding 2D key point information based on the 3D pose parameters; and

constructing the first loss function based on the 2D key point parameters and the 2D key point information.

5. The method according to claim 4 , wherein the target loss function further comprises a loss function corresponding to the 2D model and a second loss function corresponding to the 3D model; the 2D key point parameters comprise a part affinity field (PAF) parameter of the human body key points and a heatmap of the human body key points, and the 3D pose parameters comprise a shape parameter and a pose parameter of a human body; and

the constructing a target loss function based on the 2D key point parameters and the 3D pose parameters comprises:

constructing the loss function corresponding to the 2D model based on a difference between a PAF parameter outputted by the 2D model and the PAF parameter of the human body key points in the sample image and a difference between a heatmap outputted by the 2D model and the heatmap of the corresponding human body key points in the sample image; and

constructing the second loss function corresponding to the 3D model based on a difference between a shape parameter outputted by the 3D model and the shape parameter of the corresponding human body in the sample image and a difference between a pose parameter outputted by the 3D model and the pose parameter of the corresponding human body in the sample image.

6. The method according to claim 1 , wherein before the processing a target human body feature map cropped from the feature map and the 2D key point parameters by using a 3D model comprised in the pose recognition model, to obtain a 3D pose parameter used for representing a 3D human body pose, the method further comprises:

determining a target human body in the feature map based on the 2D key point parameters; and

cropping the feature map according to the target human body, to obtain the target human body feature map.

7. The method according to claim 1 , wherein the updating a model parameter of the pose recognition model based on the target loss function comprises:

determining a value of the target loss function based on the 2D key point parameters and the 3D pose parameters;

determining an error signal of the pose recognition model based on the target loss function in a case that the value of the target loss function exceeds a preset threshold; and

back-propagating the error signal in the pose recognition model, and updating a model parameter of each layer during the propagation.

8. The method according to claim 1 , the method comprising:

processing, by using the feature map model comprised in the pose recognition model, a to-be-recognized image comprising a human body, to obtain a feature map of the to-be-recognized image;

processing the feature map of the to-be-recognized image by using the 2D model comprised in the pose recognition model, to obtain 2D key point parameters used for representing a 2D human body pose in the to-be-recognized image; and

processing, by using the 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 in the to-be-recognized image.

9. The method according to claim 8 , further comprising:

recognizing a 2D pose of the human body in the to-be-recognized image based on the 2D key point parameter, the to-be-recognized image being acquired based on an outputted image of a specific human pose;

performing similarity matching between the 2D pose and the specific human pose, to obtain a matching result; and

outputting prompt information used for representing the matching result.

10. The method according to claim 8 , further comprising:

constructing, based on the 3D pose parameter, a 3D human body model corresponding to the target human body; and

controlling the 3D human body model to perform a target action, the target action matching an action performed by the target human body.

11. 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.

12. The computer device according to claim 11 , wherein the plurality of operations further comprise:

before 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:

determining the human body key points in a key point set according to a setting; and

labeling the sample image based on the human body key points and with reference to the key point set.

13. The computer device according to claim 12 , wherein the key point set comprises:

reference key points used for localization of human body parts and extension key points, which, together with the reference key points, represent a plurality of 3D poses of the parts to which the reference key points and the extension key points belong.

14. The computer device according to claim 11 , wherein the target loss function comprises a first loss function corresponding to the 3D model; and the constructing a target loss function based on the 2D key point parameters and the 3D pose parameters comprises:

determining corresponding 2D key point information based on the 3D pose parameters; and

constructing the first loss function based on the 2D key point parameters and the 2D key point information.

15. The computer device according to claim 11 , wherein the plurality of operations further comprise:

before processing a target human body feature map cropped from the feature map and the 2D key point parameters by using a 3D model comprised in the pose recognition model, to obtain a 3D pose parameter used for representing a 3D human body pose:

determining a target human body in the feature map based on the 2D key point parameters; and

cropping the feature map according to the target human body, to obtain the target human body feature map.

16. The computer device according to claim 11 , wherein the updating a model parameter of the pose recognition model based on the target loss function comprises:

determining a value of the target loss function based on the 2D key point parameters and the 3D pose parameters;

determining an error signal of the pose recognition model based on the target loss function in a case that the value of the target loss function exceeds a preset threshold; and

back-propagating the error signal in the pose recognition model, and updating a model parameter of each layer during the propagation.

17. The computer device according to claim 11 , wherein the plurality of operations further comprise:

processing, by using the feature map model comprised in the pose recognition model, a to-be-recognized image comprising a human body, to obtain a feature map of the to-be-recognized image;

processing the feature map of the to-be-recognized image by using the 2D model comprised in the pose recognition model, to obtain 2D key point parameters used for representing a 2D human body pose in the to-be-recognized image; and

processing, by using the 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 in the to-be-recognized image.

18. 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.

19. The non-transitory computer-readable storage medium according to claim 18 , wherein the plurality of operations further comprise:

before 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:

determining the human body key points in a key point set according to a setting; and

labeling the sample image based on the human body key points and with reference to the key point set.

20. The non-transitory computer-readable storage medium according to claim 19 , wherein the key point set comprises:

reference key points used for localization of human body parts and extension key points, which, together with the reference key points, represent a plurality of 3D poses of the parts to which the reference key points and the extension key points belong.