Abstract: A speckle detection method includes: obtaining a to-be-detected image; converting the to-be-detected image into Lab color space to obtain a Lab image; extracting a speckle feature from the Lab image to obtain a speckle feature image, where the speckle feature image includes a skin speckle feature and a subcutaneous speckle feature; and determining a skin speckle and a subcutaneous speckle in the speckle feature image.

Abstract: An image acquisition method includes acquiring a first image based on a first photographing parameter, where the first image includes a first to-be-detected item, acquiring a second image based on a second photographing parameter, where the second image includes a second to-be-detected item, and determining, based on the first image and the second image, a detection result corresponding to the first to-be-detected item and a detection result corresponding to the second to-be-detected item.

Abstract: A skin detection and evaluation method includes: obtaining a face image of a user; detecting a skin problem that appears in the face image; prompting in a first interface, the user that the skin problem appears on a face, wherein the first interface comprises the face image; and displaying a second interface in response to a first operation performed by the user in the first interface, wherein the second interface comprises a first facial simulated image obtained after the skin problem is aged; or displaying a third interface in response to a second operation performed by the user in the first interface, wherein the third interface comprises a second facial simulated image obtained after the skin problem is de-aged.

Abstract: The present application discloses an image recognition method, apparatus, device, and a computer storage medium, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. The method includes: performing organ recognition on a human face image and marking positions of the human facial five sense organs in the human face image, obtaining a marked human face image; inputting the marked human face image into a backbone network model and performing feature extraction, obtaining defect features of the marked human face image outputted by different convolutional neural network levels of the backbone network model; and fusing the defect features of different levels that are located in a same area of the human face image, obtaining a defect recognition result of the human face image.

Abstract: An image recognition method is provided, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. An implementation includes: performing five-sense-organ recognition on a preprocessed human face image and marking positions of the human facial five sense organs in the human face image, to obtain the marked human face image; determining human face images at multiple scales of the marked human face image, inputting the human face images of multiple scales into a backbone network model, and performing feature extraction, to obtain a wrinkle feature of the human face image at each of the multiple scales; and fusing the wrinkle feature at each scale that is located in a same area of the human face image, to obtain a wrinkle recognition result of the human face image.

Abstract: A method and an apparatus of transferring an image, a method of training an image transfer model, a device, and a medium. The method of transferring the image includes: extracting a first attribute feature of a first object and a first shape feature of a target part of the first object respectively according to a first image and a first position information of the target part of the first object in the first image; extracting a first identity feature of a second object contained in a second image; and generating a first transferred image according to the first attribute feature, the first shape feature and the first identity feature, wherein the first transferred image contains the second object having the first attribute feature and the first shape feature.

Abstract: A method, an apparatus, a device and a storage medium for three-dimensional reconstruction are provided. The method may include: acquiring a sequence of RGBD images of a target; determining a set of first three-dimensional key points of the target on each RGBD image for the each RGBD image in the sequence of the RGBD images; transferring the set of the first three-dimensional key points to a camera coordinate system to obtain a set of second three-dimensional key points of the target in the camera coordinate system; calculating an Euler angle of the each RGBD image based on the set of the second three-dimensional key points; selecting an RGBD image based on the Euler angle; and performing three-dimensional reconstruction of the target based on the selected RGBD image to obtain a three-dimensional model of the target.

Abstract: A skin detection and evaluation method includes: obtaining a face image of a user; detecting a skin problem that appears in the face image; prompting in a first interface, the user that the skin problem appears on a face, wherein the first interface comprises the face image; and displaying a second interface in response to a first operation performed by the user in the first interface, wherein the second interface comprises a first facial simulated image obtained after the skin problem is aged; or displaying a third interface in response to a second operation performed by the user in the first interface, wherein the third interface comprises a second facial simulated image obtained after the skin problem is de-aged.

Abstract: The present disclosure relates to a field of artificial intelligence technology, in particular to a field of computer vision and deep learning technology, and more particularly, a method and an apparatus for changing a hairstyle of a character, a device, and a storage medium are provided. The method includes: determining an original feature vector of an original image containing the character, wherein the character in the original image has an original hairstyle; acquiring a boundary vector associated with the original hairstyle and a target hairstyle based on a hairstyle classification model; determining a target feature vector corresponding to the target hairstyle based on the original feature vector and the boundary vector; and generating a target image containing the character based on the target feature vector, wherein the character in the target image has the target hairstyle.

Abstract: A method, an apparatus, a device and a storage medium for three-dimensional reconstruction are provided. The method may include: acquiring a sequence of RGBD images of a target; determining a set of first three-dimensional key points of the target on each RGBD image for the each RGBD image in the sequence of the RGBD images; transferring the set of the first three-dimensional key points to a camera coordinate system to obtain a set of second three-dimensional key points of the target in the camera coordinate system; calculating an Euler angle of the each RGBD image based on the set of the second three-dimensional key points; selecting an RGBD image based on the Euler angle; and performing three-dimensional reconstruction of the target based on the selected RGBD image to obtain a three-dimensional model of the target.

Abstract: An image acquisition method includes acquiring a first image based on a first photographing parameter, where the first image includes a first to-be-detected item, acquiring a second image based on a second photographing parameter, where the second image includes a second to-be-detected item, and determining, based on the first image and the second image, a detection result corresponding to the first to-be-detected item and a detection result corresponding to the second to-be-detected item.

Abstract: A speckle detection method includes: obtaining a to-be-detected image; converting the to-be-detected image into Lab color space to obtain a Lab image; extracting a speckle feature from the Lab image to obtain a speckle feature image, where the speckle feature image includes a skin speckle feature and a subcutaneous speckle feature; and determining a skin speckle and a subcutaneous speckle in the speckle feature image.

Abstract: An image recognition method is provided, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing.

Abstract: The present application discloses an image recognition method, apparatus, device, and a computer storage medium, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. The method includes: performing organ recognition on a human face image and marking positions of the human facial five sense organs in the human face image, obtaining a marked human face image; inputting the marked human face image into a backbone network model and performing feature extraction, obtaining defect features of the marked human face image outputted by different convolutional neural network levels of the backbone network model; and fusing the defect features of different levels that are located in a same area of the human face image, obtaining a defect recognition result of the human face image.

Abstract: The present disclosure discloses a method, apparatus and electronic device for determining skin smoothness, which relates to the field of computer vision technologies. The specific implementation solution is as follows: when the skin smoothness is calculated, an image to be detected including a face area is obtained first, and then the image to be detected and a smoothness analysis mask image corresponding to the image to be detected are inputted into a deep learning model to obtain a plurality of feature vectors for indicating the skin smoothness of the face. Because the smoothness analysis mask image does not include preset factors including at least one of five sense organs, reflection and hair, the influence of the preset factors on the skin smoothness is avoided, so that the accuracy for the skin smoothness of the face is ensured to a certain extent.