Abstract: A hand key-point recognition model training method is provided for a model training device. The method includes: converting a sample virtual image into an emulation image through a Cycle-GAN model, the sample virtual image being an image generated through three-dimensional modeling, and the sample virtual image including key-point coordinates corresponding to hand key-points, and the emulation image being used for emulating an image acquired in a real scenario; extracting a hand image in the emulation image; and training a hand key-point recognition model according to the hand image in the emulation image and the key-point coordinates, the hand key-point recognition model being used for outputting hand key-point coordinates of a hand in a real image according to the inputted real image.

Abstract: This disclosure provides a method and a system for detecting and recognizing a target object in a real-time video. The method includes: determining whether a target object recognition result RX-1 of a previous frame of image of a current frame of image is the same as a target object recognition result RX-2 of a previous frame of image of the previous frame of image; performing target object position detection in the current frame of image by using a first-stage neural network to obtain a position range CX of a target object in the current frame of image when the two recognition results RX-1 and RX-2 are different; or determining a position range CX of a target object in the current frame of image according to a position range CX-1 of the target object in the previous frame of image when the two recognition results RX-1 and RX-2 are the same; and performing target object recognition in the current frame of image according to the position range CX by using a second-stage neural network.

Abstract: This disclosure provides a method and a system for detecting and recognizing a target object in a real-time video. The method includes: determining whether a target object recognition result RX-1 of a previous frame of image of a current frame of image is the same as a target object recognition result RX-2 of a previous frame of image of the previous frame of image; performing target object position detection in the current frame of image by using a first-stage neural network to obtain a position range CX of a target object in the current frame of image when the two recognition results RX-1 and RX-2 are different; or determining a position range CX of a target object in the current frame of image according to a position range CX-1 of the target object in the previous frame of image when the two recognition results RX-1 and RX-2 are the same; and performing target object recognition in the current frame of image according to the position range CX by using a second-stage neural network.

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: This application relates to an image processing method and apparatus, a storage medium, and a computer device. The method includes obtaining acquired image frames; identifying, in each obtained image frame, a target area and a reference area that are obtained through image semantic segmentation; detecting, when a location relationship between the target area and the reference area in an obtained first image frame meets an action start condition and a location relationship between the target area and the reference area in an obtained second image frame meets an action end condition, an action to trigger adding an additional element, the second image frame being acquired after the first image frame; obtaining the additional element when the triggering action is detected; and adding the additional element to image frames acquired after the second image frame.

Abstract: This application discloses a method, an apparatus, and a storage medium for gesture movement recognition.

Abstract: A method for recognizing object nodes in an image includes: performing image detection on an original image, to determine a target image region that comprises a target image object, the original image being a two-dimensional image; performing image cropping on the original image according to the target image region, to obtain a subimage comprising the target image object; and performing node recognition on the subimage based on a preset node recognition model, to obtain image position information of each node of the target image object in the original image.

Abstract: An embodiment of this application provides an image object detection method. The method may include obtaining a detection image, an n-level deep feature map framework, and an m-level non-deep feature map framework. The method may further include extracting deep feature from an (i?1)-level feature of the detection image using an i-level deep feature map framework, to obtain an i-level feature of the detection image. The method may further include extracting non-deep feature from a (j?1+n)-level feature of the detection image using a j-level non-deep feature map framework, to obtain a (j+n)-level feature of the detection image. The method may further include performing information regression operation on an a-level feature to an (m+n)-level feature of the detection image, to obtain an object type information and an object position information of an object in the detection image. The a is an integer less than n and greater than or equal to 2.

Abstract: This disclosure provides a method and a system for detecting and recognizing a target object in a real-time video. The method includes: determining whether a target object recognition result RX-1 of a previous frame of image of a current frame of image is the same as a target object recognition result RX-2 of a previous frame of image of the previous frame of image; performing target object position detection in the current frame of image by using a first-stage neural network to obtain a position range CX of a target object in the current frame of image when the two recognition results RX-1 and RX-2 are different; or determining a position range CX of a target object in the current frame of image according to a position range CX-1 of the target object in the previous frame of image when the two recognition results RX-1 and RX-2 are the same; and performing target object recognition in the current frame of image according to the position range CX by using a second-stage neural network.

Abstract: An embodiment of this application provides an image object detection method. The method may include obtaining a detection image, an n-level deep feature map framework, and an m-level non-deep feature map framework. The method may further include extracting deep feature from an (i?1)-level feature of the detection image using an i-level deep feature map framework, to obtain an i-level feature of the detection image. The method may further include extracting non-deep feature from a (j?1+n)-level feature of the detection image using a j-level non-deep feature map framework, to obtain a (j+n)-level feature of the detection image. The method may further include performing information regression operation on an a-level feature to an (m+n)-level feature of the detection image, to obtain an object type information and an object position information of an object in the detection image. The a is an integer less than n and greater than or equal to 2.

Abstract: This application discloses a method, an apparatus, and a storage medium for gesture movement recognition.

Abstract: A hand key-point recognition model training method is provided for a model training device. The method includes: converting a sample virtual image into an emulation image through a Cycle-GAN model, the sample virtual image being an image generated through three-dimensional modeling, and the sample virtual image including key-point coordinates corresponding to hand key-points, and the emulation image being used for emulating an image acquired in a real scenario; extracting a hand image in the emulation image; and training a hand key-point recognition model according to the hand image in the emulation image and the key-point coordinates, the hand key-point recognition model being used for outputting hand key-point coordinates of a hand in a real image according to the inputted real image.

Abstract: This application relates to an image processing method and apparatus, a storage medium, and a computer device. The method includes obtaining acquired image frames; identifying, in each obtained image frame, a target area and a reference area that are obtained through image semantic segmentation; detecting, when a location relationship between the target area and the reference area in an obtained first image frame meets an action start condition and a location relationship between the target area and the reference area in an obtained second image frame meets an action end condition, an action to trigger adding an additional element, the second image frame being acquired after the first image frame; obtaining the additional element when the triggering action is detected; and adding the additional element to image frames acquired after the second image frame.

Abstract: A method for recognizing object nodes in an image includes: performing image detection on an original image, to determine a target image region that comprises a target image object, the original image being a two-dimensional image; performing image cropping on the original image according to the target image region, to obtain a subimage comprising the target image object; and performing node recognition on the subimage based on a preset node recognition model, to obtain image position information of each node of the target image object in the original image.

Abstract: A method and device are described for implementing instant communication. Information indicating that a first user is logged into an instant communication interface is obtained. On-line users are displayed to the first user in response to the login of the first user. A first user selects an object to follow from the on-line users and, information about the selected object to follow is received in response to the first user selecting the object to follow from the on-line users. The first user may be set into a first instant communication room in which the selected object to follow resides, for performing instant communication. The device includes: an obtaining module, a displaying module and a processing module. In this manner communication between strangers is facilitated and the efficiency of making friends is enhanced.

Abstract: A method and device are described for implementing instant communication. Information indicating that a first user is logged into an instant communication interface is obtained. On-line users are displayed to the first user in response to the login of the first user. A first user selects an object to follow from the on-line users and, information about the selected object to follow is received in response to the first user selecting the object to follow from the on-line users. The first user may be set into a first instant communication room in which the selected object to follow resides, for performing instant communication. The device includes: an obtaining module, a displaying module and a processing module. In this manner communication between strangers is facilitated and the efficiency of making friends is enhanced.

Abstract: A method and device are described for implementing instant communication. Information indicating that a first user is logged into an instant communication interface is obtained. On-line users are displayed to the first user in response to the login of the first user. A first user selects an object to follow from the on-line users and, information about the selected object to follow is received in response to the first user selecting the object to follow from the on-line users. The first user may be set into a first instant communication room in which the selected object to follow resides, for performing instant communication. The device includes: an obtaining module, a displaying module and a processing module. In this manner communication between strangers is facilitated and the efficiency of making friends is enhanced.

Abstract: There are provided a method, apparatus, system and computer readable storage medium of adding an instant message contact. The method includes: receiving a joining-in request for a group from a client where a user locates, the joining-in request comprising a group identifier; acquiring audio and/or video information of other user in the group, based on the group identifier; and transmitting the audio and/or video information of the other user to the client where the user locates, for adding by the client where the user locates the instant message contact based on the audio and/or video information of the other user. Thereby, the mutual trust between unknown users is improved, and the success ratio of adding an unknown user as a contact and the ratio of turning into a contact from an unknown user are increased.

Abstract: The present disclosure discloses an Instant Messaging (IM) terminal and a method for displaying a session message in real time. The method includes: obtaining user inputted literal contents and a font identifier of a user selected personalized font; invoking a personalized font configuration file corresponding to the font identifier according to the font identifier; synthesizing the personalized font configuration file and the literal contents into a session message and displaying the session message in real time. By implementing the present disclosure, when plotting characters, the IM terminal in real time is able to statically synthesize a personalized font according to literal contents of a message and a personalized font configuration file corresponding to a font identifier, which greatly satisfies the user requirements of inputting and displaying the personalized font in real time.

Abstract: A method and device are described for implementing instant communication. Information indicating that a first user is logged into an instant communication interface is obtained. On-line users are displayed to the first user in response to the login of the first user. A first user selects an object to follow from the on-line users and, information about the selected object to follow is received in response to the first user selecting the object to follow from the on-line users. The first user may be set into a first instant communication room in which the selected object to follow resides, for performing instant communication. The device includes: an obtaining module, a displaying module and a processing module. In this manner communication between strangers is facilitated and the efficiency of making friends is enhanced.