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: Embodiments of this application disclose an image processing method and apparatus, and a computer-readable storage medium. The method includes acquiring an image pair to be processed, and performing image feature extraction on an image to be processed in the image pair to be processed to obtain an image; extracting an association feature from the image feature, the association feature characterizing mutual information between images in the image pair to be processed; identifying a common view image of a common view region in the image to be processed according to the association feature, and calculating a scale difference between common view images; adjusting a size of the common view image based on the scale difference to obtain an adjusted common view image; and extracting at least one common view feature point from each of the adjusted common view images, and processing the image based on the common view feature point.

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 image super-resolution method includes performing frequency domain transformation on a first image to obtain a spectral feature of the first image, the spectral feature representing a distribution of a grayscale gradient in the first image. The method further includes performing blur kernel prediction based on the spectral feature to obtain a blur kernel of the first image, the blur kernel being a convolution kernel. The method also includes performing super-resolution processing on the first image based on the blur kernel to generate a super-resolved image, a definition of the super-resolved image being higher than a definition of the first image.

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: A facial image processing method includes: obtaining first optical flow information; obtaining, based on the first optical flow information, second optical flow information through an optical flow information prediction sub-model of an image processing model; generating, based on the first facial sample image and the second optical flow information, a predicted image of the first facial sample image through a generator of the image processing model; discriminating the predicted image and the second facial sample image through a discriminator of the image processing model to obtain a first discrimination result and a second discrimination result; and training the image processing model based on the first discrimination result, the second discrimination result, the predicted image, the second facial sample image, the first optical flow information, and the second optical flow information.

Abstract: Disclosed herein are an image detection method and apparatus, a computer-readable storage medium, and a computer device. The method includes iteratively training a plurality of neural network models to obtain a plurality of trained neural network model; and performing detection on an image to be detected using the trained plurality of neural network models to obtain a detection result. Each iteration of training includes: for each of a plurality of sample images, separately inputting the sample image into the neural network models to obtain a fuzzy probability value set, and calculating, based on the fuzzy probability value set and preset label information of the sample image, a loss parameter of the sample image; selecting target sample images based on a distribution of loss parameters of the plurality of sample images; and updating the plurality of neural network models based on the target sample images.

Abstract: Embodiments of this application disclose an image style conversion method performed by an electronic device. The method includes: performing quality enhancement on a first target style image to obtain a second target style image; performing feature extraction on the second target style image to obtain a target style feature; performing migration training on a preset target style conversion model by using a full style conversion model and the target style feature to obtain a target style conversion model; inputting a full style feature, the target style feature, and a to-be-converted image into the target style conversion model, and performing style conversion on the to-be-converted image using the target style conversion model to obtain a target image conforming to a target style.

Abstract: An image processing method and apparatus that includes: performing feature extraction processing on N image regions of a to-be-processed image respectively to obtain feature data corresponding to the N image regions respectively, N being an integer greater than or equal to 1, performing defect detection on the N image regions respectively according to the feature data corresponding to the N image regions respectively to obtain a prediction probability of a defect in each of the N image regions, obtaining attention for each image region, adjusting the prediction probability of the defect in each image region according to the attention for each image region, and generating a prediction result of the to-be-processed image according to the adjusted prediction probability of each image region.

Abstract: This disclosure relates to image processing method and apparatus. The method includes: processing, with a first generator in an image processing model, a first sample image in a first sample set to obtain a first predicted image; processing, with the first generator, a second sample image in a second sample set to obtain a second predicted image; and training the image processing model according to a difference between the target avatar in the first sample image and the first predicted avatar and a difference between a first type attribute of the sample avatar in the second sample image and a first type attribute of the second predicted avatar.

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: A computing device acquires an original image sequence. The device performs image preprocessing on the original image sequence to obtain a feature map sequence and a confidence map sequence that are corresponding to the original image sequence. The device performs feature fusion on the feature map sequence based on the confidence map sequence, to obtain a target fused feature map corresponding to a target original image frame in the original image sequence. The device reconstructs the target original image frame based on the target fused feature map to obtain a target reconstructed image frame. Credibility supervision at the pixel level is performed on features in the feature fusion process to guide the fusion of image features with high credibility, thereby improving the image quality of a reconstructed 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 and apparatus are provided. During image matting on an original image, a plurality of segmented images including different regions are first obtained through semantic segmentation. Further, according to the segmented images, lines of different widths are drawn on a contour line of a foreground region to obtain a target trimap. Finally, a target image is generated based on the target trimap. For the target trimap, because lines of different widths are drawn on the contour line of the foreground region, pertinent image matting may be implemented for different regions, so that image matting precision may be improved for a region requiring fine image matting, and image matting precision of another region can also be ensured. In this way, a final image obtained through image matting is fine and natural.

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: The present subject matter discloses an action recognition method, apparatus and device, a storage medium, and a computer program product, belonging to the field of image recognition. Multiple video frames in a target video are obtained. Feature extraction is performed on the multiple video frames respectively according to multiple dimensions to obtain multiple multi-channel feature patterns. Each video frame corresponds to one multi-channel feature pattern. Each channel represents one dimension. An attention weight of each multi-channel feature pattern is determined based on a similarity between every two multi-channel feature patterns. The attention weight is used for representing a degree of correlation between a corresponding multi-channel feature pattern and an action performed by an object in the target video. A type of the action is determined based on the multiple multi-channel feature patterns and the determined multiple attention weights.

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 provides a method for reconstructing a three-dimensional model, a method for training a three-dimensional reconstruction model, an apparatus, a computer device, and a storage medium. The method for reconstructing a three-dimensional model includes: obtaining an image feature coefficient of an input image; respectively obtaining, according to the image feature coefficient, a global feature map and an initial local feature map based on a texture and a shape of the input image; performing edge smoothing on the initial local feature map, to obtain a target local feature map; respectively splicing the global feature map and the target local feature map based on the texture and the shape, to obtain a target texture image and a target shape image; and performing three-dimensional model reconstruction according to the target texture image and the target shape image, to obtain a target three-dimensional model.