WO2022257766A1

WO2022257766A1 - Image processing method and apparatus, device, and medium

Info

Publication number: WO2022257766A1
Application number: PCT/CN2022/094925
Authority: WO
Inventors: 张小伟; 刘更代
Original assignee: 百果园技术(新加坡)有限公司; 张小伟
Priority date: 2021-06-10
Filing date: 2022-05-25
Publication date: 2022-12-15
Also published as: CN113361419A

Abstract

Disclosed in the present application are an image processing method and apparatus, a device, and a medium. The image processing method comprises: obtaining target emotion information and a target image of a target user; if it is determined that the target image contains a human face, determining first characteristic information of a preset characteristic part of a human face in the target image and second characteristic information of the human face; and rendering an emoji of the target user according to a target facial 3D model and a stored stock image corresponding to the first characteristic information, wherein the target facial 3D model is determined according to the target emotion information, the second characteristic information, and a pre-configured base facial 3D model.

Description

Image processing method, device, equipment and medium

This application claims the priority of the Chinese patent application with application number 202110646132.1 submitted to the China Patent Office on June 10, 2021, the entire content of which is incorporated herein by reference.

technical field

The present application relates to the field of image processing, for example, to an image processing method, device, equipment and medium.

Background technique

With the rise of social networks and social software, emoticon images (such as emoji, etc.) have become increasingly popular. Among them, emoticon images can be understood as a group of graphical abstract human facial expressions, which are usually used to highlight specific emotions based on instant text communication. Therefore, when people use instant messaging applications (such as WeChat, WhatsApp, Internet Instant Messaging Office (Instant Messaging Office, IMO), etc.), they often use emoticons for text communication.

Common emoticon images are generally pre-made and published by developers, and all users can download and use these pre-made emoticon images. Therefore, the content and style of these emoticon images are generally fixed, and basically appear in the form of static color icons. All users use the same emoticon images, which are not personalized enough to reflect the user's personal attributes and characteristics.

Contents of the invention

The present application provides an image processing method, device, equipment, and medium to solve the problem that it is impossible to generate personalized facial expression images of target users.

The application provides an image processing method, including:

Obtain the target facial expression information and the target image of the target user;

If it is determined that the target image contains a human face, then determining the first characteristic information of the preset characteristic parts on the human face in the target image and the second characteristic information of the human face;

According to the 3D model of the target face and the stored material image corresponding to the first feature information, the expression image of the target user is rendered; wherein, the 3D model of the target face is based on the target expression information, the The second feature information and the preset basic three-dimensional face model are determined.

The application provides an image processing device, including:

An acquisition unit configured to acquire target facial expression information and a target image of a target user;

The processing unit is configured to, if it is determined that the target image contains a human face, then determine the first characteristic information of the preset characteristic parts on the human face in the target image and the second characteristic information of the human face;

The rendering unit is configured to render an expression image of the target user according to the 3D model of the target face and the stored material image corresponding to the first feature information; wherein, the 3D model of the target face is based on the determined by the target expression information, the second feature information, and a preset basic three-dimensional face model.

The present application provides an electronic device, which includes at least a processor and a memory, and the processor is configured to implement the above image processing method when executing a computer program stored in the memory.

The present application provides a computer-readable storage medium, which stores a computer program, and implements the above-mentioned image processing method when the computer program is executed by a processor.

Description of drawings

FIG. 1 is a schematic flow diagram of an image processing method provided in an embodiment of the present application;

FIG. 2 is a schematic diagram of a process for determining feature information of preset feature parts on a human face in a target image provided by an embodiment of the present application;

FIG. 3 is a schematic diagram of a three-dimensional vertex located in a skin area of a human face in a target image provided by an embodiment of the present application;

FIG. 4 is a schematic flowchart of a training method for a geometric feature extraction model provided in an embodiment of the present application;

FIG. 5 is a schematic flow diagram of an image processing method provided by an embodiment of the present application;

FIG. 6 is a schematic diagram of an image processing scene provided by an embodiment of the present application;

FIG. 7 is a schematic diagram of a process for determining the second feature information of a face in a target image provided by an embodiment of the present application;

FIG. 8 is a schematic structural diagram of an image processing device provided by an embodiment of the present application;

FIG. 9 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

The present application will be described below in conjunction with the accompanying drawings, and the described embodiments are only a part of the embodiments of the present application.

In order to quickly generate personalized emoticon images of target users, embodiments of the present application provide an image processing method, device, equipment, and medium.

Example 1:

Fig. 1 is a schematic flow chart of an image processing method provided by an embodiment of the present application, the method comprising:

S101: Obtain target expression information and a target image of a target user.

The image processing method provided in the embodiment of the present application is applied to an electronic device, and the electronic device may be a smart device such as a mobile terminal or a server.

When the target user needs to generate an expression image, the target image of the target user and target expression information (such as smile, anger, surprise, etc.) can be input to the smart device. After receiving the target image and target expression information, the smart device generates an image generation request according to the target image and target expression information and sends it to the electronic device for image processing. After receiving the image generation request for generating the target user's facial expression image, the electronic device performing image processing may acquire the target user's target image and target facial expression information carried in the image generation request. Then, based on the target image and the target expression information, the image processing method provided by the embodiment of the present application is used to perform corresponding processing to generate the target user's expression image. Wherein, the target image is an image including the face of the target user, and the target expression information is used to indicate the required expression in the generated expression image.

In an example, the target image may be any image selected by the target user on the display interface of the smart device, or may be an image collected by a camera of the smart device.

In an example, the target expression information may be determined according to an expression selected by the target user from a plurality of preset expressions displayed on the display interface of the smart device. For example, the target user selects a smiling expression from a plurality of preset expressions displayed on the display interface of the smart device, and then determines the target expression information corresponding to the smiling expression according to the correspondence between the preset expressions and expression information . The target facial expression information may also be extracted from at least one image selected by the target user on the display interface of the smart device, or may be extracted from at least one image collected by the camera of the smart device.

The at least one image used to extract target expression information may be the same as or different from the target image.

As a possible implementation, there are many ways for the target user to input the target image and target expression information of the target user to the smart device, which may be through trigger operation input, such as click, double-click, slide and other trigger operations, or Through voice input, for example, input voice information "select X picture and W expression". It can also be input by manipulating a control device (eg, remote controller, mouse). During the specific implementation process, it can be flexibly set according to actual needs, which is not limited here.

S102: If it is determined that the target image contains a human face, determine first feature information of preset feature parts on the human face in the target image and second feature information of the human face.

Due to the possibility of misoperation by the target user, the target image acquired by the electronic device for image processing may not contain a human face, making it impossible to personalize the expression image of the target user based on the face in the target image. Therefore, in the embodiment of the present application, after the target image is acquired, it may be determined whether the target image contains a human face. When it is determined that the target image contains a human face, it means that the expression image of the target user can be generated, and then the subsequent steps can be performed, that is, determining the first feature information of the preset feature parts on the human face in the target image and the second feature of the human face information.

It is possible to determine whether the target image contains a human face through a feature extraction algorithm, such as the Local Binary Patterns (LBP) algorithm, etc., or to determine whether the target image contains a human face through a pre-trained face detection model .

Optionally, in order to facilitate the subsequent generation of the target user's expression image and reduce the amount of calculation required to generate the expression image, after it is determined that the target image contains a human face, the target image can be determined according to the key points on the human face in the target image The pixels corresponding to the faces in the image. The target image is updated according to the sub-image containing all the pixels corresponding to the face. The first feature information of preset feature parts on the face and the second feature information of the face are determined in the sub-image. Wherein, the process of obtaining the key point information on the face of the target image belongs to the related technology, which is not limited here.

In an example, the characteristic parts on the human face include at least one of the following: hair, eyebrows, eyes, mouth, facial decorations, pupils, and the like. Based on this, the first characteristic information of the preset characteristic parts on the human face may include hair information on the human face, such as hairstyle, hair color, eyebrow shape, eyebrow color, beard shape, beard color, etc. Facial features information, such as the shape information of the facial features, the ratio information of the facial features and the face, pupil color information, etc., the shape information of the face, such as face shape, etc., the decoration information on the face, such as the shape information of glasses, the color of glasses Information, card issuance shape information, ear jewelry shape information, etc., skin information of the face, such as skin color, distribution information of moles, skin texture information, etc., identity information of the face, such as age, gender, etc.

The above are several examples of the first characteristic information of the preset characteristic parts on the human face, and the first characteristic information also includes other types of information, which are not limited in this embodiment of the present application.

In an example, the first feature information includes one or more of the following:

Gender information, hairstyle information, facial decoration information, hair color information, eye shape information, eyebrow shape information, pupil color information, beard type information.

For example, gender information includes male and female.

The face decoration information takes glasses information as an example, and the glasses information may include no glasses, rimless glasses, half rim glasses, round rim glasses, square rim glasses, and sunglasses.

Eyebrow shape information may include arched eyebrows, S-shaped eyebrows, crescent eyebrows, straight eyebrows, character-splayed eyebrows and saber eyebrows; pupil color information may include brown, black, blue, gray, green, amber and other colors, wherein , the colors that do not belong to the first 6 colors in the pupil color information will be determined as other colors.

The hairstyle information may generally include female hairstyle information as well as male hairstyle information. The female hairstyle information may include ultra-short hair without bangs, ultra-short hair with bangs, medium-short hair without bangs, medium-short hair with bangs, long hair with shoulders without bangs, long hair with shoulders with bangs, single-shoulder long hair, double braids, Single braid, ball head, Nezha head, other types of ultra-short hair, other types of short and medium hair, other types of long hair, and other types of braids. The information on these female hairstyles is based on the length of the hair, and subdivided into whether there are bangs and Whether to braid. For hairstyles that cannot be classified into the top 11 female hairstyles in the information on women's hairstyles, the hairstyles can be classified into other types of ultra-short hair, other types of short and medium hair, other types of long hair, and other types of braids according to the length of the hair in the hairstyle . The male hairstyle information may include bald head, short hair/very short hair, short hair without bangs, short hair with bangs, medium hair without bangs, medium hair with bangs, long hair and other types. Hairstyles that cannot be classified into the top 7 male hairstyles in the male hairstyle information will be classified into other types.

Beard type information may include no beard, stubble, upper lip beard, goatee, ring beard, full cheek beard, and extra long beard.

The first feature information of the preset feature parts on the face of the target image can be extracted through a feature extraction algorithm, or the first feature information of the preset feature parts on the face of the target image can be obtained through a pre-trained model. It can be flexibly set according to requirements, and is not limited here.

In an example, the second characteristic information of the human face may include at least one of the following: geometric shape information of the human face, texture color information, and expression information.

Optionally, since the target expression information is used to indicate the required expression in the generated expression image, it is not necessary to obtain the expression information of the face in the target image, and the second feature information of the face includes Geometry information and texture color information.

In a possible implementation manner, the method also includes:

If it is determined that the target image does not contain a human face, output prompt information to re-upload the target image.

When based on the method in this embodiment, when it is determined that the target image does not contain a human face, it means that the target user's expression image cannot be generated according to the target image, then in order to improve user experience, a prompt message for re-uploading the target image can be generated and controlled. The smart device of the target user outputs the prompt information.

The prompt information output by the smart device can be in audio format, such as a voice broadcast prompt message "The current image cannot generate an emoticon image", or the corresponding prompt information in text form can be displayed on the display interface, such as displaying prompt information on the display interface "The current image cannot generate an emoticon image", pop-up prompts, etc. It is also possible to combine the two ways of outputting prompt information, such as simultaneously broadcasting prompt information in audio format and displaying prompt information in text format on a display interface. It can be flexibly set according to actual requirements, and is not limited here.

S103: Render an expression image of the target user according to the 3D model of the target face and the stored material image corresponding to the first feature information; wherein, the 3D model of the target face is based on the target expression information , the second feature information and a preset basic three-dimensional face model.

In order to facilitate the generation of three-dimensional expression images of target users, the basic three-dimensional face model is set in advance. After obtaining the second feature information of the face in the target image, the preconfigured basic 3D face model can be adjusted according to the second feature information and target expression information to obtain the target 3D face model of the target user . Wherein, the process of adjusting the pre-configured basic three-dimensional face model according to the second feature information and the target expression information belongs to related technologies, and will not be introduced here.

In a possible implementation, in order to facilitate and quickly generate the expression image of the target user, and to make the characters in the expression image close to the image of the target user, there is also pre-saved information corresponding to each type of feature information of the preset feature parts. Footage image. For example, taking the characteristic part of a human face as an eye and the characteristic information of the eye as an eye shape as an example, material images corresponding to each eye shape are stored in advance. When generating the expression image of the target user later, the material image corresponding to the first characteristic information can be quickly determined according to the material image corresponding to each characteristic information of the preset characteristic part saved in advance. The material image corresponding to the first characteristic information is rendered to the corresponding area in the target face three-dimensional model, thereby acquiring the expression image of the target user. Paste the material image corresponding to the first feature information to the corresponding area in the target face three-dimensional model. For example, paste the material image corresponding to the ball head to the corresponding area of the hair in the 3D model of the target face.

The process of rendering the material image corresponding to the first feature information to the corresponding area in the target face 3D model is not described in this application.

The target expression information may include expression information corresponding to faces in multiple consecutive expression images, or may only include expression information of a face in one expression image.

In a possible embodiment, if the target expression information includes expression information corresponding to faces in multiple expression images, the material image corresponding to the first feature information is rendered to the corresponding area in the target face three-dimensional model , multiple emoticon images of the target user can be acquired, thereby generating an emoticon animation of the target user. Generate dynamic emoticon images of target users. For example, an emoticon image in Graphics Interchange Format (GIF) of the target image is generated.

In another possible embodiment, if the target expression information only includes the expression information of a human face in an expression image, the material image corresponding to the first feature information is rendered to the corresponding area in the target human face three-dimensional model , an emoticon image of the target user can be obtained. Generate static emoticon images of target users.

In an example, the rendered emoticon image of the target user may be three-dimensional or two-dimensional. During the specific implementation process, it can be flexibly set according to actual needs, which is not limited here.

Using the above method, after obtaining the target user's target image containing a human face and target expression information, the first feature information of the preset feature parts on the face of the target image and the second feature information of the human face can be automatically determined , Reduce manual control and workload. And because the material image corresponding to the first feature information is stored in advance, the material image that fits the image of the target user can be accurately determined later, and the material image is rendered to the image according to the target expression information, the second feature information and the target face 3D model determined by the pre-set basic 3D face model, so as to render the target user's expression image, realize personalized customization of the target user's expression image, and do not need to manually draw the target user's expression according to the target user's target image images, reducing labor costs.

Example 2:

In order to quickly and accurately determine the feature information of the face in the target image, on the basis of the above embodiments, in this embodiment, the determining the first feature information of the preset feature parts on the face of the target image, The method includes: determining the first feature information based on the target image through a pre-trained feature extraction model.

In order to quickly and accurately determine the feature information of the face in the target image, in the embodiment of the present application, a feature extraction model is pre-trained, so that the preset features of the face in the target image can be extracted through the pre-trained feature extraction model The first feature information of the part. Wherein, the quantity of the feature extraction model is at least one, and any feature extraction model is used to extract at least one kind of feature information, and the feature information extracted by each feature extraction model is different. For example, feature extraction model A is only used to extract the eye shape information of the eyes on the human face and the pupil color information of the pupils on the human face in the target image, and feature extraction model B is only used to extract the eyebrow shape information of the eyebrows on the human face in the target image , the feature extraction model C is used to extract the hairstyle information and hair color information of the hair on the face of the target image. After the target image is acquired based on the above embodiment, the first feature information of the preset feature parts on the face of the target image can be determined based on the target image through each pre-trained feature extraction model.

In one example, the pre-trained feature extraction model can be a classification network, for example, a convolutional neural network (Convolutional Neural Networks, CNN) classification model with MobileNet as a feature extraction layer, or a traditional feature extraction plus classifier Networks (such as Support Vector Machine (SVM), Random Forest), etc.

In one example, the target image can be simultaneously input to each pre-trained feature extraction model. Through each pre-trained feature extraction model, the input target image is processed to determine the first feature information of the preset feature parts on the face of the target image.

For example, feature extraction models corresponding to gender information, hair color information, eye shape information, eyebrow shape information, and pupil color information are pre-trained. Simultaneously input the target image into the feature extraction models corresponding to gender information, hair color information, eye shape information, eyebrow shape information, and pupil color information. Through the feature extraction models corresponding to gender information, hair color information, eye shape information, eyebrow shape information, and pupil color information respectively, based on the input target image, the gender information, hair color information, and eye shape of the face in the target image are obtained. information, eyebrow shape information, and pupil color information.

Optionally, in order to accurately determine the hair color information, a feature extraction model corresponding to the hair color information may be used to determine the hair region of the face in the target image. For example, the mask of the area where the hair is located. Then the color of each pixel in the area is counted, so as to accurately determine the hair color information. For example, hair color information is determined according to the color corresponding to the most pixels in the region.

Optionally, in order to accurately determine the color information of the through hole, the key points on the pupil of the face in the target image may be determined through a feature extraction model corresponding to the pupil color information. Then the colors of the pixels corresponding to the multiple key points are counted, so as to accurately determine the pupil color information.

In another possible way, due to the correlation between some types of feature information, for example, women's faces generally do not have beard type information, men's faces have different hairstyles from women's faces, etc. . Therefore, the feature extraction models corresponding to the various types of feature information can be determined according to the correlation between the various types of feature information. For example, the feature extraction model corresponding to the feature information that will not be affected by other feature information is used as the feature extraction model with the earlier execution order, and according to the processing results of the feature extraction model with the earlier execution order, it is determined to be executed after the feature extraction model feature extraction model. After the target image is subsequently acquired, the target image can be simultaneously input into each feature extraction model that is executed earlier in order for processing. According to the processing result of the feature extraction model whose execution order is earlier, determine the preset feature extraction model executed after the feature extraction model in the case of the processing result. And the target image is input into the feature extraction model in the execution sequence for processing.

For example, FIG. 2 is a schematic diagram of a process of determining feature information of preset feature parts on a human face in a target image provided by an embodiment of the present application. The feature extraction models corresponding to the gender information, hair color information, facial decoration information, eyebrow shape information, and pupil color information are preset as the feature extraction models that are executed first. Input the target image into the feature extraction model corresponding to gender information, hair color information, facial decoration information, eyebrow shape information and pupil color information respectively. Through the feature extraction models corresponding to gender information, hair color information, facial decoration information, eyebrow shape information and pupil color information, the gender information, hair color information, facial decoration information, eyebrow type information and pupil color information. Among them, if the gender information is male, the target image is input into the feature extraction model corresponding to the pre-trained beard type information and male hairstyle information for processing, and the beard type and the beard type of the beard on the face in the target image are determined. Hairstyles information for men. If the gender information is female, the target image is input into the feature extraction model corresponding to the pre-trained female hairstyle information for processing, and the female hairstyle information of the hair on the face of the target image is determined.

In order to obtain the feature extraction model, in the embodiment of the present application, it is necessary to pre-collect a sample set (denoted as the first sample set) for training the feature extraction model, and the first sample set contains sample images (denoted as the first sample set). A sample image), the first sample image contains a human face. Each first sample image is marked, and the feature information (denoted as the third feature information) of the preset feature parts on the face of each first sample image is determined. The third characteristic information can be represented by numbers, letters, character strings, etc., or in other forms, as long as the characteristic information that uniquely identifies the characteristic parts of the face in the first sample image is guaranteed. Subsequently, the original feature extraction model is trained based on the acquired first sample image and its corresponding third feature information.

The electronic device for training the feature extraction model may be the same as or different from the electronic device for image processing.

During specific implementation, any first sample image is acquired. The fourth feature information of the preset feature parts on the human face in the first sample image is determined through the original feature extraction model. A loss value of the first sample image is determined according to the fourth feature information and the third feature information of the first sample image. Based on the loss value of the first sample image, the original feature extraction model is trained to adjust the parameter values of the parameters in the original feature extraction model.

Since there are multiple first sample images in the first sample set, the above-mentioned steps are performed for each first sample image until a convergence condition is reached, then it is determined that the feature extraction model training is completed.

Satisfying the preset convergence condition can be that the sum of the loss values of each first sample image determined for the current iteration is less than the preset convergence threshold, or the number of iterations for training the original feature extraction model reaches the set maximum number of iterations, etc. . It can be set flexibly in specific implementation, and is not limited here.

In a possible implementation manner, when performing feature extraction model training, the first sample image can be divided into training samples and test samples, and the original feature extraction model is trained based on the training samples first, and then the above-mentioned The reliability of the trained feature extraction model is verified.

Example 3:

In order to quickly and accurately determine the feature information of the face in the target image, on the basis of the above-mentioned embodiments, in the embodiment of the present application, if the second feature information includes the texture color information, determine the feature information of the face The second feature information includes: obtaining the 3D feature information of the human face based on the target image through a pre-trained 3D feature extraction model; based on the face geometric information and texture information contained in the 3D feature information, for The basic three-dimensional face model is adjusted to determine the three-dimensional vertices corresponding to the human face and the first texture color information of the three-dimensional vertices; according to the three-dimensional vertices and the first texture color information, determine the human face Texture color information for the face.

In a practical application process, the face of the target user in the target image generally contains different levels of illumination. That is to say, the texture color of the face in the target image is mainly composed of two parts, one is the color of the face texture, and the other is the ambient light. In order to keep the skin color of the person in the generated expression image consistent with the skin color of the face in the target image, the texture color information of the face in the target image can be obtained to facilitate the subsequent generation of a three-dimensional expression of the target user based on the texture color information The skin tone of the people in the image.

In an example, the basic three-dimensional face model can be a Blendshape three-dimensional model, and the Blendshape three-dimensional model is formed based on three-dimensional feature information of a human face, and the three-dimensional feature information is mainly composed of three principal component analysis (Principal Components Analysis, PCA )constitute. The three principal component analyzes respectively include: 1. The principal component analysis corresponding to the geometric shape of the expressionless face, which can be composed of a plurality of (for example, 97) identity identifier (Identifier, ID) Blendshape coefficients; 2. The principal component analysis of the geometric deformation caused by the corresponding expression, which can be composed of multiple (for example, 64) expression Blendshape coefficients; 3. The texture principal component analysis corresponding to the face, the principal component analysis There may be multiple (eg, 79) texture coefficients. Therefore, by adjusting the preset Blendshape 3D model through the 3D feature information of the target face, a 3D face model similar to the face in the target image can be completely reconstructed. For example, the three-dimensional feature information of the human face in the target image is obtained. The three-dimensional feature information includes 97-dimensional ID Blendshape coefficients, 64-dimensional expression Blendshape coefficients, and 79-dimensional texture coefficients. Through the three-dimensional feature information, the preset Blendshape three-dimensional model is processed. adjustment, so as to completely reconstruct a 3D face model similar to the face in the target image.

In order to obtain the 3D feature information of the human face quickly and accurately, in the embodiment of the present application, a 3D feature extraction model is pre-trained. Input the acquired target image into the pre-trained 3D feature extraction model. Through the pre-trained 3D feature extraction model, the input target image is processed, and the 3D feature information of the face in the target image can be obtained.

The pre-trained three-dimensional feature extraction model may be a convolutional neural network, such as a convolutional neural network using MobileNet as a feature extraction layer.

Since the expression of the person in the expression image to be generated is determined according to the target expression information, it is not necessary to perform a principal component analysis on the pre-set basic 3D face model based on the principal component analysis of the geometric deformation caused by the corresponding expression in the 3D feature information. Adjustment. Therefore, after obtaining the 3D feature information of the face, the preset basic 3D face model can be adjusted based on the face geometry information and texture information contained in the 3D feature information, so that the target image can be determined The 3D vertices corresponding to the face and the first texture color information of the 3D vertices.

Based on the geometric information and texture information of the face included in the 3D feature information, the process of adjusting the pre-set basic 3D model of the face belongs to the related technology and will not be repeated here.

In a possible implementation manner, after determining the first texture color information of the 3D vertex corresponding to the face in the target image, the first texture color information may be directly determined as the texture color information of the face, or the statistics of each For the number of 3D vertices corresponding to the first texture color information, the first texture color information corresponding to more 3D vertices is determined as the texture color information of the human face. It is also possible to calculate the mean value of the first texture color information of each three-dimensional vertex, and determine the mean value as the texture color information of the human face, and the like. During the implementation process, flexible settings can be made according to actual needs, and details will not be described here.

Using the above method, it can be ensured that the skin color of the person in the generated emoticon image is the same as the skin color of the face in the target image, so that the person in the emoticon image fits the image of the target user more closely, and the emoticon image is more suitable for the personalization of the target user , improving the user experience.

In a possible implementation manner, in order to improve the accuracy of the obtained texture color information and avoid being affected by the texture color of the non-skin region on the human face, in the embodiment of the present application, according to the 3D vertices and The first texture color information, determining the texture color information of the human face, includes: determining the target three-dimensional vertex according to the pixel points corresponding to the human face in the target image and the three-dimensional vertex; wherein, the target The three-dimensional vertex is the three-dimensional vertex corresponding to the pixel point corresponding to the skin of the human face in the target image; according to the first texture color information of the target three-dimensional vertex, determine the second texture color information of the target three-dimensional vertex ; Determine the texture color information of the human face according to the second texture color information of the target 3D vertex.

Since the obtained 3D vertices corresponding to the face in the target image may include the 3D vertices corresponding to the non-skin areas on the face, the acquired first texture color information will also include the 3D vertices corresponding to the non-skin areas on the face. The texture color information of the corresponding three-dimensional vertices affects the accuracy of the determined texture color information of the human face. Therefore, after the three-dimensional vertex corresponding to the face in the target image is obtained, the pixel point corresponding to the skin of the face in the target image is determined according to the pixel point corresponding to the face in the target image (denoted as the target pixel point). Then, according to the corresponding relationship between the pixel point and the 3D vertex in the target image, the 3D vertex corresponding to the target pixel point (denoted as the target 3D vertex) is determined. The target 3D vertex can be understood as a 3D vertex located in the skin area of the human face in the target image. FIG. 3 is a schematic diagram of a three-dimensional vertex located in a skin area of a human face in a target image provided by an embodiment of the present application. As shown in FIG. 3 , the white points located in the skin area of the human face in FIG. 3 are three-dimensional vertices located in the skin area of the human face in the target image, that is, the target three-dimensional vertices.

Then, the second texture color information is determined according to the first texture color information of the target three-dimensional vertex. Then, according to the second texture color information of the target three-dimensional vertices, the texture color information of the human face in the target image is determined.

In a possible implementation manner, the second texture color information may be determined according to the first texture color information of the target 3D vertex in the following manner:

Method 1: Determine the first texture color information of any target 3D vertex as the second texture color information.

Method 2: Determine the first texture color information of the specified target three-dimensional vertex as the second texture color information.

Mode 3: Process the first texture color information of each target 3D vertex through a preset mathematical function, and determine the processed texture color information as the second texture color information.

For example, through a preset mathematical function, determine the mean value of the first texture color information of each target three-dimensional vertex, and determine the mean value as the second texture color information.

In a possible implementation manner, after determining the second texture color information of the target 3D vertex corresponding to the face in the target image, the second texture color information may be directly determined as the texture color information of the face, or the statistics of each The second texture color information corresponds to the number of target three-dimensional vertices, and the second texture color information corresponding to more target three-dimensional vertices is determined as the texture color information of the face. It is also possible to calculate the mean value of the second texture color information of each target 3D vertex, and determine the mean value as the texture color information of the human face, etc. During the implementation process, flexible settings can be made according to actual needs, and details will not be described here.

Through the above method, it is possible to effectively avoid the influence of the first texture color information of the 3D vertices corresponding to the non-skin areas on the human face in the target image, such as eyes, eyebrows, beards, etc., on the texture color of the skin areas on the human face , to ensure that the skin color of the person in the generated expression image is the same as the skin color of the face in the target image.

Example 4:

In order to quickly and accurately determine the feature information of the face in the target image, on the basis of the above embodiments, in this embodiment, if the second feature information includes face geometric information, the determination of the face The feature information includes: acquiring the three-dimensional feature information of the human face based on the target image through a pre-trained geometric feature extraction model; and determining the geometric information of the human face according to the three-dimensional feature information.

In a practical application process, the faces of the target user in the target image generally contain different degrees of expressions. That is to say, the 3D geometric shape of the face in the target image is mainly composed of two parts, one is the first 3D geometric shape of the face without any expression, and the other is the first 3D geometric shape added on the basis of the first 3D geometric shape The second three-dimensional geometric shape obtained after the geometric deformation brought by the expression is taken into account. In order to keep the face shape of the person in the generated expression image consistent with the face shape of the face in the target image, the face geometric information of the face in the target image can be obtained, so as to facilitate subsequent generation of a 3D image of the target user based on the face geometric information 3D model of the face of the person in the facial expression image.

In one example, in order to obtain face geometric information, it is necessary to pre-train a geometric feature extraction model. Through the pre-trained geometric feature extraction model, based on the target image, the three-dimensional feature information of the face in the target image can be obtained. Then, according to the three-dimensional feature information, the face geometric information of the face in the target image is determined.

The structure and functions of the geometric feature extraction model have been described in the above-mentioned embodiments, and repeated descriptions will not be repeated.

Because mainly include in the three-dimensional feature information: 1, the principal component analysis of the geometric shape of corresponding expressionless face, this principal component analysis can be made up of multiple (for example, 97) identity identification (ID) Blendshape coefficients; 2, corresponding The principal component analysis of the geometric shape deformation that expression brings, this principal component analysis can be made up of multiple (for example, 64) expression Blendshape coefficients; (eg, 79) texture coefficients. Therefore, the principal component analysis of the geometric shape of the face corresponding to the expressionless face in the three-dimensional feature information can be determined as the face geometric information of the face in the target image, that is, multiple ID Blendshape coefficients can be determined as the face of the face in the target image geometric information. For example, 97 ID Blendshape coefficients are determined as the face geometry information of the face in the target image. Subsequently, according to the obtained facial geometric information, the 3D face model of the target user's expressionless face can be accurately reconstructed to ensure that the face shape of the person in the expression image is consistent with the face shape of the target user.

In a possible implementation manner, the geometric feature extraction model is obtained in the following manner:

Obtain any sample image contained in the sample set; wherein, the sample image contains a sample face; through the original geometric feature extraction model, obtain the three-dimensional feature information of the sample face in the sample image; based on the three-dimensional Feature information, adjusting the basic three-dimensional face model to determine the sample three-dimensional vertices corresponding to the sample faces and the third texture color information of the sample three-dimensional vertices; according to the third texture of the sample three-dimensional vertices The color information and the pixel value of the pixel point corresponding to the sample three-dimensional vertex corresponding to the sample face in the sample image are used to train the original geometric feature extraction model.

In order to facilitate the training of the geometric feature extraction model, in the embodiment of the present application, it is necessary to pre-collect a sample set (denoted as the second sample set) for training the geometric feature extraction model, which contains sample images (denoted as the second sample set). Two sample images), the second sample image contains a human face (denoted as a sample human face). Wherein, the first sample image and the second sample image may be completely or partially identical, or completely different. Subsequently, based on the acquired second sample image, the original geometric feature extraction model is trained.

The electronic device for training the geometric feature extraction model may be the same as or different from the electronic device for image processing.

Since the output of the geometric feature extraction model is three-dimensional feature information, the three-dimensional feature information is mainly used to adjust the preset basic three-dimensional face model. The 3D vertices of the face in the adjusted basic 3D face model and the texture color information of the 3D vertices can reflect the accuracy of the geometric feature extraction model to a certain extent. Based on this, in the process of training the original geometric feature extraction model, the basic 3D face model can be adjusted based on the 3D feature information output by the original geometric feature extraction model to determine the sample face in the second sample image The corresponding 3D vertex (denoted as sample 3D vertex) and the texture color information of the sample 3D vertex (denoted as third texture color information). The sample 3D vertex corresponding to the sample face in each sample image in the current iteration and the third texture color information of the sample 3D vertex are used to train the original geometric feature extraction model.

During specific implementation, any second sample image is acquired. The three-dimensional feature information of the sample face in the second sample image is determined through the original geometric feature extraction model. Based on the 3D feature information of the sample face, the basic 3D face model is adjusted to determine the sample 3D vertices corresponding to the sample face and the third texture color information of the sample 3D vertices. Then, the loss value of the second sample image is determined according to the third texture color information of each sample 3D vertex and the pixel value of the pixel point corresponding to the sample 3D vertex on the sample face in the sample image. Based on the loss value of the second sample image, the original geometric feature extraction model is trained to adjust parameter values of parameters in the original geometric feature extraction model.

In a possible implementation manner, after determining the sample 3D vertex corresponding to the sample face and the third texture color information of the sample 3D vertex, according to the third texture color information of the sample 3D vertex Information, and the pixel value of the pixel point corresponding to the sample three-dimensional vertex corresponding to the sample face in the sample image, before training the original geometric feature extraction model, the method further includes: according to the sample The pixel points corresponding to the sample face in the image and the sample three-dimensional vertices determine the target sample three-dimensional vertices; wherein, the target sample three-dimensional vertices are pixel points corresponding to the skin of the sample face in the sample image The corresponding 3D vertex; according to the third texture color information of the 3D vertex of the target sample, determine the fourth texture color information of the 3D vertex of the target sample; according to the 3D vertex of the target sample and the third texture color information of the 3D vertex of the target sample Four texture color information, updating the sample 3D vertex and the third texture color information of the sample 3D vertex.

Since the sample 3D vertices corresponding to the sample face in the obtained sample image may include the sample 3D vertices corresponding to the non-skin area of the sample face, the obtained third texture color information will also include the sample face The texture color information of the sample three-dimensional vertices corresponding to the upper non-skin area affects the accuracy of the determined texture color information of the sample face. Therefore, after the sample three-dimensional vertex corresponding to the sample face in the sample image is obtained based on the above-mentioned embodiment, the pixel point corresponding to the skin of the sample face in the sample image is determined according to the pixel point corresponding to the sample face in the sample image (record is the sample pixel). Then, according to the corresponding relationship between the pixel points in the sample image and the sample three-dimensional vertices, the sample three-dimensional vertices corresponding to the sample pixel points (denoted as target sample three-dimensional vertices) are determined. The target sample 3D vertex may be understood as a sample 3D vertex located in the skin area of the sample face in the sample image. Then, according to the third texture color information of the three-dimensional vertices of the target sample, the fourth texture color information is determined. The determined sample 3D vertex and the third texture color information of the sample 3D vertex are updated according to the target sample 3D vertex and the fourth texture color information. Among the sample 3D vertices corresponding to the sample face in the sample image, the sample 3D vertices other than the target sample 3D vertices are deleted, only the target sample 3D vertices are kept, and the fourth texture color information of the target sample 3D vertices is determined as the sample image The texture color information of the sample face.

In a possible implementation manner, the fourth texture color information may be determined according to the third texture color information of the three-dimensional vertices of the target sample in the following manner:

Method 1: Determine the third texture color information of any target sample 3D vertex as the fourth texture color information.

Method 2: Determine the third texture color information of the specified three-dimensional vertex of the target sample as the fourth texture color information.

Mode 3: Process the third texture color information of the three-dimensional vertices of each target sample through a preset mathematical function, and determine the processed texture color information as the fourth texture color information.

For example, the average value of the third texture color information of the three-dimensional vertices of each target sample is determined through a preset mathematical function, and the average value is determined as the fourth texture color information.

Since there are multiple second sample images in the second sample set, the above-mentioned steps are performed for each second sample image until a convergence condition is reached, then it is determined that the training of the geometric feature extraction model is completed.

Satisfying the preset convergence condition can be that the sum of the loss values of each second sample image determined for the current iteration is less than the preset convergence threshold, or the number of iterations for training the original geometric feature extraction model reaches the set maximum number of iterations, etc. . It can be set flexibly in specific implementation, and is not limited here.

In a possible implementation, when training the geometric feature extraction model, the second sample image can be divided into a training sample and a test sample, and the original geometric feature extraction model is trained based on the training sample, and then the original geometric feature extraction model is trained based on the test sample. The reliability of the above-mentioned trained geometric feature extraction model is verified.

The following is an introduction to the training process of the geometric feature extraction model provided by the embodiment of the present application through specific embodiments. FIG. 4 is a schematic flow diagram of a training method for a geometric feature extraction model provided by the embodiment of the present application. The method includes:

S401: Acquire any second sample image in the second sample set.

S402: Obtain 3D feature information of the sample face in the second sample image by using the original geometric feature extraction model.

S403: Based on the 3D feature information of the sample face, adjust the Blendshape 3D model to determine the sample 3D vertex corresponding to the sample face and the third texture color information of the sample 3D vertex.

S404: According to the pixel points corresponding to the sample face in the sample image, determine the sample pixel points corresponding to the skin of the sample face in the sample image.

S405: According to the corresponding relationship between the pixel points in the sample image and the sample three-dimensional vertices, determine the target sample three-dimensional vertices corresponding to the sample pixel points.

S406: Determine an average value of the third texture color information of the three-dimensional vertices of the target sample as fourth texture color information.

S407: According to the target sample 3D vertex and the fourth texture color information, update the sample 3D vertex determined in S403 and the third texture color information of the sample 3D vertex.

S408: According to the third texture color information of each sample 3D vertex and the pixel value of the sample 3D vertex on the sample face corresponding to the pixel point in the sample image, train the original geometric feature extraction model to adjust the original geometry Parameter values for parameters in the feature extraction model.

In the process of geometric feature extraction model training, the offline method is generally adopted, and the original geometric feature extraction model is trained in advance through the training device based on the second sample image in the second sample set, so as to obtain the trained geometric feature extraction model . Subsequently, the trained geometric feature extraction model can be stored in an image processing electronic device, so as to facilitate the generation of an expression image of the target user.

Example 5:

The image processing method provided by the embodiment of the present application is described below through an embodiment. FIG. 5 is a schematic flow chart of an image processing method provided by the embodiment of the present application. The method includes:

S501: Obtain target expression information and a target image of a target user.

S502: Determine whether the target image contains a human face, if it is determined that the target image contains a human face, execute S503, and if it is determined that the target image does not contain a human face, execute S508.

The process of performing face detection on the target image includes: using a pre-trained face detection model to determine whether the target image contains a human face. If it is determined that the target image contains a human face, execute S503; if it is determined that the target image does not contain a human face, execute S508.

Optionally, by using a pre-trained face detection model, key points on the face of the target image can also be determined. After it is determined that the target image contains a human face, the pixel points corresponding to the human face in the target image can be determined according to the key points on the human face in the target image. The target image is updated according to the sub-image containing all the pixels corresponding to the face.

S503: Determine the first characteristic information of the preset characteristic parts on the human face in the target image.

One or more of the following first feature information: gender information, hairstyle information, hair color information, eye shape information, eyebrow shape information, pupil color information, beard type information.

FIG. 6 is a schematic diagram of an image processing scene provided by an embodiment of the present application. As shown in FIG. 6 , the first feature information of the preset feature parts on the face of the target image can be determined by the first recognition module. After the target image of the target user is acquired, the target image is processed by the first identification module to determine the first feature information of the preset feature parts on the face of the target image. For example, gender information, hairstyle information, hair color information, eye shape information, eyebrow shape information, pupil color information, and beard type information as shown in FIG. 6 .

A feature extraction model may be pre-stored in the first recognition module. Through the feature extraction model, the first feature information of the preset feature parts on the human face in the target image can be obtained.

Optionally, the feature extraction models used to extract different types of first feature information may be the same or different.

S504: Determine second feature information of the face in the target image.

The second feature information includes one or more of the following: face geometry information, texture color information, expression information.

As shown in FIG. 6 , the second feature information of the face in the target image may be determined by the second identification module. After the target image of the target user is acquired, the target image may also be processed by the second recognition module to determine the second feature information of the face in the target image. For example, face geometry information and texture color information as shown in FIG. 6 .

In a possible implementation, if the second feature information includes texture color information, the process of determining the texture color information of the face in the target image is as shown in FIG. The CNN network shown in 7), based on the target image, obtains the three-dimensional feature information of the face in the target image. Then, based on the facial geometric information and texture information contained in the obtained 3D feature information, the basic 3D model of the face (the Blendshape 3D model shown in Figure 7) is adjusted to determine the 3D vertices corresponding to the face in the target image and the first texture color information of the 3D vertices. According to the pixel points corresponding to the face in the target image and the obtained 3D vertices, the target 3D vertices corresponding to the pixel points corresponding to the skin of the face in the target image are determined. The second texture color information is determined according to the average value of the first texture color information of each target three-dimensional vertex. Then, according to the second texture color information of the target three-dimensional vertices, the texture color information of the human face in the target image is determined.

In a possible implementation manner, if the second feature information includes face geometric information, the process of determining the face geometric information of the face in the target image includes: using a pre-trained geometric feature extraction model based on the target image, obtaining The three-dimensional feature information of the face in the target image; according to the three-dimensional feature information, the geometric information of the face is determined.

The execution order of S503 and S504 is not limited, that is, S503 and S504 can be executed at the same time, or S503 can be executed first and then S504 can be executed, or S504 can be executed first and then S503 can be executed.

S505: Determine a material image corresponding to the first characteristic information.

As shown in FIG. 6 , a material library is preset, and the material image corresponding to each kind of characteristic information of a preset characteristic part is stored in the material library. After acquiring the first characteristic information of the preset characteristic parts on the face in the target image based on S503, the material image corresponding to the first characteristic information may be determined from the material library. The corresponding relationship between each characteristic information and the material image is stored in the material library, and the material image corresponding to the first characteristic information can be determined subsequently according to the stored corresponding relationship between each characteristic information and the material image.

S506: Determine a target three-dimensional face model according to the target expression information, the second feature information, and a preset basic three-dimensional face model.

S507: Render an expression image of the target user according to the 3D model of the target face and the material image corresponding to the first feature information.

The rendered emoticon image may be a dynamic emoticon image or a static emoticon image.

If the electronic device for image processing is a server, after the target user's facial expression image is acquired, the generated facial expression image can be sent to the target user's smart device, so that the target user can use the facial expression image. For example, the target user may use the emoticon image in an instant messaging scenario (such as IMO). At the same time, the target user can also use the emoticon image in a live video scene (such as Live).

In a possible implementation manner, considering that the image of the target user generally does not change much, the identity information of the target user, the material image corresponding to the determined first characteristic information, and the second characteristic information can be stored correspondingly. . Subsequent target users may not need to upload the target image again, but only need to upload the target expression information. The electronic device for image processing can directly determine the 3D model of the target face according to the target expression information selected by the target user, the second characteristic information and the preset basic 3D face model. And according to the 3D model of the target face and the material image corresponding to the saved first feature information, an expression image of the target user is rendered.

S508: Outputting prompt information for re-uploading the target image.

Embodiment 6:

The embodiment of the present application also provides an image processing device. FIG. 8 is a schematic structural diagram of an image processing device provided in the embodiment of the present application. The device includes:

The acquisition unit 81 is configured to acquire the target expression information and the target image of the target user; the processing unit 82 is configured to determine the first preset characteristic part of the human face in the target image if it is determined that the target image contains a human face. A characteristic information and the second characteristic information of the human face; the rendering unit 83 is configured to render the expression of the target user according to the three-dimensional model of the target human face and the material image corresponding to the stored first characteristic information image; wherein, the 3D model of the target face is determined according to the target expression information, the second feature information, and a preset basic 3D model of the face.

The image processing device provided in the embodiment of the present application can execute the image processing method provided in any embodiment of the present application, and has corresponding functional modules and effects for executing the method.

Embodiment 7:

9 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device includes: a processor 91, a communication interface 92, a memory 93, and a communication bus 94, wherein the processor 91, the communication interface 92, and the memory 93 pass through The communication bus 94 completes the mutual communication;

A computer program is stored in the memory 93, and when the program is executed by the processor 91, the processor 91 is executed to: obtain target facial expression information and a target image of the target user; if it is determined that the target image contains If there is a face, then determine the first feature information of the preset feature parts on the face in the target image and the second feature information of the face; according to the 3D model of the target face and the stored first feature information Corresponding to the material image, an expression image of the target user is rendered; wherein, the three-dimensional model of the target face is determined according to the target expression information, the second feature information, and a preset basic three-dimensional face model.

Since the problem-solving principle of the above-mentioned electronic device is similar to the image processing method, the implementation of the above-mentioned electronic device can refer to the implementation of the method, and the repetition will not be repeated.

The communication interface 92 is provided for communication between the above-mentioned electronic device and other devices.

The memory 93 may include a random access memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-Volatile Memory, NVM).

Embodiment 8:

On the basis of the above-mentioned embodiments, the embodiments of the present application also provide a computer-readable storage medium, the computer-readable storage medium stores a computer program executable by a processor, when the program runs on the processor When running on the computer, the processor realizes when executing: acquiring the target expression information and the target image of the target user; if it is determined that the target image contains a human face, then determine the preset feature position of the human face in the target image The first characteristic information and the second characteristic information of the human face; according to the 3D model of the target human face and the material image corresponding to the saved first characteristic information, render the expression image of the target user; wherein, the The 3D model of the target face is determined according to the target expression information, the second feature information and a preset basic 3D model of the face.

Since the problem-solving principle of the computer-readable storage medium is similar to the image processing method in the foregoing embodiments, reference can be made to the implementation of the image processing method for specific implementation.

Claims

An image processing method, comprising:

Obtain the target facial expression information and the target image of the target user;

When it is determined that the target image contains a human face, determining first feature information of preset feature parts on the face of the target image and second feature information of the human face;

According to the 3D model of the target face and the stored material image corresponding to the first feature information, the expression image of the target user is rendered; wherein, the 3D model of the target face is based on the target expression information, the The second feature information and the preset basic three-dimensional face model are determined.
The method according to claim 1, further comprising:

In a case where it is determined that the target image does not contain a human face, output prompt information for re-uploading the target image.
The method according to claim 1, wherein the first feature information includes at least one of the following:

Gender information, hairstyle information, facial decoration information, hair color information, eye shape information, eyebrow shape information, pupil color information, beard type information.
The method according to claim 3, wherein said determining the first characteristic information of the preset characteristic parts on the human face in the target image comprises:

The first feature information is determined based on the target image through a pre-trained feature extraction model.
The method according to claim 1, wherein the second feature information includes at least one of the following:

Face geometry information, texture color information, expression information.
The method according to claim 5, wherein, when the second feature information includes the texture color information, determining the second feature information of the human face comprises:

Obtaining the three-dimensional feature information of the human face based on the target image through a pre-trained three-dimensional feature extraction model;

Adjusting the basic 3D model of the face based on the face geometry information and texture information included in the 3D feature information, to determine the 3D vertices corresponding to the face and the first texture color information of the 3D vertices;

Determine the texture color information of the human face according to the 3D vertices and the first texture color information.
The method according to claim 6, wherein said determining the texture color information of the human face according to the 3D vertices and the first texture color information comprises:

Determine the target three-dimensional vertex according to the pixel points corresponding to the human face in the target image and the three-dimensional vertices; wherein, the target three-dimensional vertices are determined by the pixel points corresponding to the skin of the human face in the target image Corresponding 3D vertices;

determining second texture color information of the target 3D vertex according to the first texture color information of the target 3D vertex;

Determine the texture color information of the human face according to the second texture color information of the target three-dimensional vertex.
The method according to claim 5, wherein, in the case where the second characteristic information includes face geometric information, the determining the characteristic information of the human face comprises:

Obtaining the three-dimensional feature information of the human face based on the target image through a pre-trained geometric feature extraction model;

According to the three-dimensional feature information, the face geometric information is determined.
The method according to claim 8, wherein the geometric feature extraction model is obtained by:

Obtain a sample image contained in the sample set; wherein, the sample image contains a sample face;

Obtaining the three-dimensional feature information of the sample face in the sample image through the original geometric feature extraction model;

Adjusting the basic three-dimensional face model based on the three-dimensional feature information to determine the sample three-dimensional vertices corresponding to the sample faces and the third texture color information of the sample three-dimensional vertices;

The original geometric feature extraction model is trained according to the third texture color information of the sample 3D vertex and the pixel value of the pixel point corresponding to the sample 3D vertex corresponding to the sample face in the sample image.
According to the method according to claim 9, after the determination of the sample 3D vertex corresponding to the sample face and the third texture color information of the sample 3D vertex, the third texture according to the sample 3D vertex The color information, and the pixel value of the pixel point corresponding to the sample three-dimensional vertex corresponding to the sample face in the sample image, before training the original geometric feature extraction model, also includes:

According to the pixel points corresponding to the sample face in the sample image and the sample three-dimensional vertex, determine the target sample three-dimensional vertex; wherein the target sample three-dimensional vertex is the skin of the sample face in the sample image The three-dimensional vertices corresponding to the corresponding pixel points;

determining fourth texture color information of the three-dimensional vertices of the target sample according to the third texture color information of the three-dimensional vertices of the target sample;

Update the sample 3D vertex and the third texture color information of the sample 3D vertex according to the target sample 3D vertex and the fourth texture color information of the target sample 3D vertex.
An image processing device, comprising:

An acquisition unit configured to acquire target facial expression information and a target image of a target user;

A processing unit configured to determine first feature information of preset feature parts on the face of the target image and second feature information of the face when it is determined that the target image contains a human face;

The rendering unit is configured to render an expression image of the target user according to the 3D model of the target face and the stored material image corresponding to the first feature information; wherein, the 3D model of the target face is based on the determined by the target expression information, the second feature information, and a preset basic three-dimensional face model.
An electronic device, comprising a processor and a memory, the processor is configured to implement the image processing method according to any one of claims 1-10 when executing the computer program stored in the memory.
A computer-readable storage medium storing a computer program, the computer program implementing the image processing method according to any one of claims 1-10 when executed by a processor.