Abstract: An image processing method and apparatus, a computer-readable medium, and an electronic device are provided. The image processing method includes: respectively projecting, according to a plurality of view angle parameters corresponding to a plurality of view angles, a face model of a target object onto a plurality of face images of the target object acquired from the plurality of view angles, to determine correspondences between regions on the face model and regions on the face image; respectively extracting, based on the correspondences and a target region in the face model that need to generate a texture image, images corresponding to the target region from the plurality of face images; and fusing the images that correspond to the target region and that are respectively extracted from the plurality of face images, to generate the texture image.

Abstract: Embodiments of this application disclose an artificial intelligence-based (AI-based) animation character control method. When one animation character has a corresponding face customization base, and one animation character has no corresponding face customization base, the animation character having the face customization base may be used as a driving character, and the animation character having no face customization base may be used as a driven character.

Abstract: A three-dimensional face model generation method is provided. The method includes: obtaining an inputted three-dimensional face mesh of a target object; aligning the three-dimensional face mesh with a first three-dimensional face model of a standard object according to face keypoints; performing fitting on the three-dimensional face mesh and a local area of the first three-dimensional face model, to obtain a second three-dimensional face model after local fitting; and performing fitting on the three-dimensional face mesh and a global area of the second three-dimensional face model, to obtain a three-dimensional face model of the target object after global fitting.

Abstract: Embodiments of this application disclose a method for displaying a virtual character in a plurality of real-world images captured by a camera is performed at an electronic device. The method includes: capturing an initial real-world image using the camera; simulating a display of the virtual character in the initial real-world image; capturing a subsequent real-world image using the camera after a movement of the camera; determining position and pose updates of the camera associated with the movement of the camera from tracking one or more feature points in the initial real-world image and the subsequent real-world image; and adjusting the display of the virtual character in the subsequent real-world image in accordance with the position and pose updates of the camera associated with the movement of the camera.

Abstract: This application provides a three-dimensional (3D) expression base generation method performed by a computer device. The method includes: obtaining image pairs of a target object in n types of head postures, each image pair including a color feature image and a depth image in a head posture; constructing a 3D human face model of the target object according to then image pairs; and generating a set of expression bases of the target object according to the 3D human face model of the target object. According to this application, based on a reconstructed 3D human face model, a set of expression bases of a target object is further generated, so that more diversified product functions may be expanded based on the set of expression bases.

Abstract: This application discloses an artificial intelligence (AI) based animation character drive method. A first expression base of a first animation character corresponding to a speaker is determined by acquiring media data including a facial expression change when the speaker says a speech, and the first expression base may reflect different expressions of the first animation character. After target text information is obtained, an acoustic feature and a target expression parameter corresponding to the target text information are determined according to the target text information, the foregoing acquired media data, and the first expression base. A second animation character having a second expression base may be driven according to the acoustic feature and the target expression parameter, so that the second animation character may simulate the speaker's sound and facial expression when saying the target text information, thereby improving experience of interaction between the user and the animation character.

Abstract: This application disclose an artificial intelligence (AI) based animation character drive method. A first expression base of a first animation character corresponding to a speaker is determined by acquiring media data including a facial expression change when the speaker says a speech, and the first expression base may reflect different expressions of the first animation character. After target text information is obtained, an acoustic feature and a target expression parameter corresponding to the target text information are determined according to the target text information, the foregoing acquired media data, and the first expression base. A second animation character having a second expression base may be driven according to the acoustic feature and the target expression parameter, so that the second animation character may simulate the speaker's sound and facial expression when saying the target text information, thereby improving experience of interaction between the user and the animation character.

Abstract: Embodiments of this application disclose a method for determining camera pose information of a camera of a mobile terminal. The method includes: obtaining a first image, a second image, and a template image, the first image being a previous frame of image of the second image, the first image and the second image being images including a respective instance of the template image captured by the mobile terminal using the camera at a corresponding spatial position; determining a first homography between the template image and the second image; determining a second homography between the first image and the second image; and performing complementary filtering processing on the first homography and the second homography, to obtain camera pose information of the camera, wherein the camera pose information of the camera represents a spatial position of the mobile terminal when the mobile terminal captures the second image using the camera.

Abstract: This application discloses a repositioning method and apparatus in a camera pose tracking process, a device, and a storage medium, belonging to the field of augmented reality (AR).

Abstract: A method, an apparatus and a storage medium for constructing a three-dimensional (3D) facial mesh using artificial intelligence is disclosed. The method includes: obtaining a facial point cloud of a target object; determining, through an expansion calculation, pixel coordinates on a facial texture image of the target object that correspond to 3D data points in the facial point cloud, as index information of the 3D data points; performing triangulation on pixels on the facial texture image to obtain triangulation information; constructing an initial 3D facial mesh according to the triangulation information, the index information, and the facial point cloud; determining a non-core region in the initial 3D facial mesh; smoothing the non-core region in the initial 3D facial mesh; and replacing the non-core region in the initial 3D facial mesh with the smoothed non-core region to obtain a 3D facial mesh of the target object.

Abstract: Embodiments of this application disclose a method and an apparatus for generating a three-dimensional face model. The method includes obtaining a plurality of target face images and a plurality of depth images corresponding to the plurality of target face images, the plurality of target face images comprising a same face; obtaining, according to an image type of each target face image, a regional face image that is in each target face image and that corresponds to the image type of each target face image, the image type comprising a front face type, a left face type, a right face type, or a head-up type; obtaining a regional depth image in a corresponding depth image according to the regional face image; and performing image fusion based on the plurality of obtained regional face images and the plurality of obtained regional depth images, to generate a three-dimensional face model.

Abstract: A method for constructing a 3D model of a target object provided is performed by a computer device, the method including: obtaining at least two initial images of a target object from a plurality of shooting angles, the at least two initial images respectively including depth information of the target object, and the depth information indicating distances between a plurality of points of the target object and a reference position; obtaining first point cloud information corresponding to the at least two initial images respectively according to the depth information in the at least two initial images; fusing the first point cloud information respectively corresponding to the at least two initial images into second point cloud information; and constructing a 3D model of the target object according to the second point cloud information.

Abstract: A method for acquiring a texture of a three-dimensional (3D) model includes: acquiring at least two 3D networks generated by a target object based on a plurality of angles, the at least two 3D networks including a first correspondence between point cloud information and color information of the target object, and first camera poses of the target object; acquiring an offset between 3D points used for recording the same position of the target object in the at least two 3D networks according to the first camera poses respectively included in the at least two 3D networks; updating the first correspondence according to the offset, to acquire a second correspondence between the point cloud information and the color information of the target object; and acquiring a surface color texture of a 3D model of the target object according to the second correspondence.

Abstract: Embodiments of this application disclose a camera attitude tracking method and apparatus, a device, and a system in the field of augmented reality (AR). The method includes receiving, by a second device with a camera, an initial image and an initial attitude parameter that are transmitted by a first device; obtaining, by the second device, a second image acquired by the camera; obtaining, by the second device, a camera attitude variation of the second image relative to the initial image; and obtaining, by the second device, according to the initial attitude parameter and the camera attitude variation, a second camera attitude parameter, the second camera attitude parameter corresponding to the second image.

Abstract: Embodiments of this application disclose a method for displaying a virtual character in a plurality of real-world images captured by a camera is performed at an electronic device. The method includes: capturing an initial real-world image using the camera; simulating a display of the virtual character in the initial real-world image; capturing a subsequent real-world image using the camera after a movement of the camera; determining position and pose updates of the camera associated with the movement of the camera from tracking one or more feature points in the initial real-world image and the subsequent real-world image; and adjusting the display of the virtual character in the subsequent real-world image in accordance with the position and pose updates of the camera associated with the movement of the camera.

Abstract: A relocalization method includes: obtaining, by a front-end program run on a device, a target image acquired after an ith marker image in the plurality of marker images; determining, by the front-end program, the target image as an (i+1)th marker image when the target image satisfies a relocalization condition, and transmitting the target image to a back-end program; performing, by the front-end program, feature point tracking on a current image acquired after the target image relative to the target image to obtain a first pose parameter. The back-end program performs relocalization on the target image to obtain a second pose parameter, and transmits the second pose parameter to the front-end program. The front-end program calculates a current pose parameter of the current image according to the first pose parameter and the second pose parameter.

Abstract: A method for determining a pose of an image capturing device is performed at an electronic device. The electronic device acquires a plurality of image frames captured by the image capturing device, extracts a plurality of matching feature points from the plurality of image frames and determines first position information of each of the matching feature points in each of the plurality of image frames. After estimating second position information of each of the matching feature points in a current image frame in the plurality of image frames by using the first position information of each of the matching feature points extracted from a previous image frame in the plurality of image frames, the electronic device determines a pose of the image capturing device based on the first position information and the second position information of each of the matching feature points in the current image frame.

Abstract: A three-dimensional face model generation method is provided. The method includes: obtaining an inputted three-dimensional face mesh of a target object; aligning the three-dimensional face mesh with a first three-dimensional face model of a standard object according to face keypoints; performing fitting on the three-dimensional face mesh and a local area of the first three-dimensional face model, to obtain a second three-dimensional face model after local fitting; and performing fitting on the three-dimensional face mesh and a global area of the second three-dimensional face model, to obtain a three-dimensional face model of the target object after global fitting.

Abstract: This application provides a three-dimensional (3D) expression base generation method performed by a computer device. The method includes: obtaining image pairs of a target object in n types of head postures, each image pair including a color feature image and a depth image in a head posture; constructing a 3D human face model of the target object according to then image pairs; and generating a set of expression bases of the target object according to the 3D human face model of the target object. According to this application, based on a reconstructed 3D human face model, a set of expression bases of a target object is further generated, so that more diversified product functions may be expanded based on the set of expression bases.

Abstract: Embodiments of this application disclose an artificial intelligence-based (AI-based) animation character control method. When one animation character has a corresponding face customization base, and one animation character has no corresponding face customization base, the animation character having the face customization base may be used as a driving character, and the animation character having no face customization base may be used as a driven character.