Abstract: A method for training an image processing model includes: acquiring a to-be-corrected eye image set matching a usage environment of the image processing model; performing style transfer on to-be-corrected eye images in the to-be-corrected eye image set through a style transfer network in the image processing model, to obtain a target eye image; acquiring a training sample matching the usage environment of the image processing model based on the to-be-corrected eye images and the target eye image, the training sample including object eye images matching different gaze positions; and training a correction network in the image processing model through the training sample matching the usage environment of the image processing model, to obtain a model update parameter matching the correction network, and generating a trained image processing model based on the model update parameter.

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: An image processing method includes: determining a brightness value of an image; and enhancing a brightness of the image when the brightness value of the image is less than an image brightness threshold. Enhancing the brightness of the image includes: determining each pixel of the image as a target pixel; determining a brightness enhancement value of the target pixel based on the brightness value of the image, an initial brightness value of the target pixel, and initial brightness values of neighboring pixels of the target pixel; and using the brightness enhancement value as an enhanced brightness value of the target pixel.

Abstract: An image processing method includes: obtaining a target image; performing blurring processing on an original image of a specified channel of channels of the target image to obtain a first blurred image; performing high pass processing on the original image of the specified channel and the first blurred image to obtain a high pass image; obtaining, for a channel, a second blurred image corresponding to the channel based on an original image of the channel and the high pass image; and obtaining a processed image corresponding to the target image based on the second blurred image of the channel and the high pass image.