Abstract: This application discloses a method for generating an image processing model performed by a computer device. The method includes: performing training by using a first source image sample, a first template image sample, and a first standard synthesized image, to obtain a first parameter adjustment model, and combining the first parameter adjustment model and a first resolution update layer into a first update model; adjusting the first update model into a second parameter adjustment model using a second source image sample and a second template image sample and a second standard synthesized image; combining the second parameter adjustment model and a second resolution update layer into a second update model; and adjusting the second update model into a target image fusion model using a third source image sample, a third template image sample and a third standard synthesized image.

Abstract: This application provides an image processing method and apparatus. The method includes acquiring an identity feature of a source image and an initial attribute feature of at least one measure of a target image in response to receiving a face change request; inputting the identity feature and the initial attribute feature of the at least one measure into a face change model; iteratively performing feature fusion on the identity feature and the initial attribute feature of the at least one measure by using the face change model to obtain a fusion feature; and generating a target face change image based on the fusion feature by using the face change model, and outputting the target face change image, a face in the target face change image being fused with an identity feature of the source face and a target attribute feature of the target face.

Abstract: This application provides an image processing method performed by a computer device. The image processing method includes: receiving a face swapping request; acquiring an attribute parameter of the image, an attribute parameter of the target face, and a face feature of the target face, the attribute parameter of the image indicating a three-dimensional attribute of the face in the image; determining a target attribute parameter based on the attribute parameter of the image and the attribute parameter of the target face; determining a target comprehensive feature based on the target attribute parameter and the face feature of the target face; encoding the image to obtain an image encoding feature of the image; migrating the target comprehensive feature to the image encoding feature of the image by normalization to obtain a fusion encoding feature; and decoding the fusion encoding feature to obtain a target face-swapped image including a fusion face.

Abstract: An de-identification method includes: performing sampling on a target space of a projector in a target network model to obtain N virtual identity vectors, N being a positive integer; extracting, by using an attribute extractor in the target network model, M attribute vectors from a target image, M being a positive integer; and generating, by using a generator in the target network model, a de-identified image of the target image based on the N virtual identity vectors and the M attribute vectors.

Abstract: The present disclosure describes human body attribute recognition methods and apparatus, electronic devices, and a storage medium. The method includes acquiring a sample image containing a plurality of to-be-detected areas being labeled with true values of human body attributes; generating, through a recognition model, a heat map of the sample image and heat maps of the to-be-detected areas to obtain a global heat map and local heat maps; fusing the global and the local heat maps to obtain a fused image, and performing human body attribute recognition on the fused image to obtain predicted values; determining a focus area of each type of human body attribute according to the global and the local heat maps; correcting the recognition model by using the focus area, the true values, and the predicted values; and performing, based on the corrected recognition model, human body attribute recognition on a to-be-recognized image.

Abstract: This application discloses a face image processing method performed by an electronic device. The method includes: acquiring a face image of a source face and a face template image of a template face; performing three-dimensional face modeling on the face image and the face template image to obtain a three-dimensional face image feature of the face image and a three-dimensional face template image feature of the face template image; fusing the three-dimensional face image feature and the three-dimensional face template image feature to obtain a three-dimensional fusion feature; performing face replacement feature extraction on the face image based on the face template image to obtain an initial face replacement feature; transforming the initial face replacement feature based on the three-dimensional fusion feature to obtain a target face replacement feature; and replacing the template face with the source face based on the target face replacement feature to obtain a target face image.

Abstract: An image processing method includes performing additional image feature extraction on a training source face image to obtain a source additional image feature, performing identity feature extraction on the training source face image to obtain a source identity feature, inputting a training template face image into an encoder in a to-be-trained face swapping model to obtain a face attribute feature, inputting the source additional image feature, the source identity feature, and the face attribute feature into a decoder in the face swapping model for decoding to obtain a decoded face image, obtaining a target model loss value based on an additional image difference between the decoded face image and a comparative face image, and adjusting the model parameters of the encoder and the decoder based on the target model loss value to obtain the trained face swapping model.

Abstract: In the field of artificial intelligence technologies, a gaze correction method and apparatus for a face image, a device, a computer-readable storage medium, and a computer program product are provided. The method includes: acquiring an eye image from a face image; determining an eye movement flow field based on the eye image and a target gaze direction, the target gaze direction being a gaze direction to which an eye gaze in the eye image is to be corrected; adjusting a pixel position in the eye image based on the eye movement flow field, to obtain a corrected eye image; and generating a face image with a corrected gaze based on the corrected eye image.

Abstract: An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product. The image gaze correction method includes: acquiring a to-be-corrected eye image from a to-be-corrected image, generating, based on the to-be-corrected eye image, an eye motion flow field and an eye contour mask, the eye motion flow field being used for adjusting a pixel position in the to-be-corrected eye image, and the eye contour mask being used for indicating a probability that the pixel position in the to-be-corrected eye image belongs to an eye region, performing, based on the eye motion flow field and the eye contour mask, gaze correction processing on the to-be-corrected eye image to obtain a corrected eye image, and generating a gaze corrected image based on the corrected eye image.

Abstract: An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product related to the field of artificial intelligence technologies are provided. The image gaze correction method includes: acquiring an eye image from an image; performing feature extraction processing on the eye image to obtain feature information of the eye image; performing, based on the feature information and a target gaze direction, gaze correction processing on the eye image to obtain an initially corrected eye image and an eye contour mask; performing, by using the eye contour mask, adjustment processing on the initially corrected eye image to obtain a corrected eye image; and generating a gaze corrected image based on the corrected eye image.

Abstract: This application discloses a human attribute recognition method performed at a computing device. The method includes: determining a human body region image in a surveillance image; inputting the human body region image into a multi-attribute convolutional neural network model, to obtain, for each of a plurality of human attributes in the human body region image, a probability that the human attribute corresponds to a respective predefined attribute value, the multi-attribute convolutional neural network model being obtained by performing multi-attribute recognition and training on a set of pre-obtained training images by using a multi-attribute convolutional neural network; determining, for each of the plurality of human attributes in the human body region image, the attribute value of the human attribute based on the corresponding probability; and displaying the attribute values of the plurality of human attributes next to the human body region image.

Abstract: The present disclosure describes human body attribute recognition methods and apparatus, electronic devices, and a storage medium. The method includes acquiring a sample image containing a plurality of to-be-detected areas being labeled with true values of human body attributes; generating, through a recognition model, a heat map of the sample image and heat maps of the to-be-detected areas to obtain a global heat map and local heat maps; fusing the global and the local heat maps to obtain a fused image, and performing human body attribute recognition on the fused image to obtain predicted values; determining a focus area of each type of human body attribute according to the global and the local heat maps; correcting the recognition model by using the focus area, the true values, and the predicted values; and performing, based on the corrected recognition model, human body attribute recognition on a to-be-recognized image.

Abstract: This application discloses a human attribute recognition method performed at a computing device. The method includes: determining a human body region image in a surveillance image; inputting the human body region image into a multi-attribute convolutional neural network model, to obtain, for each of a plurality of human attributes in the human body region image, a probability that the human attribute corresponds to a respective predefined attribute value, the multi-attribute convolutional neural network model being obtained by performing multi-attribute recognition and training on a set of pre-obtained training images by using a multi-attribute convolutional neural network; determining, for each of the plurality of human attributes in the human body region image, the attribute value of the human attribute based on the corresponding probability; and displaying the attribute values of the plurality of human attributes next to the human body region image.