Patent Publication Number: US-2022224860-A1

Title: Method for Presenting Face In Video Call, Video Call Apparatus, and Vehicle

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of International Application No. PCT/CN2020/094090, filed on Jun. 3, 2020, which claims priority to Chinese Patent Application No. 201910944612.9, filed on Sep. 30, 2019. The disclosures of the aforementioned applications are hereby incorporated by reference in their entireties. 
    
    
     TECHNICAL FIELD 
     The present application relates to the field of artificial intelligence, and in particular, to a method for presenting a face in a video call, a video call apparatus, and a vehicle. 
     BACKGROUND 
     Artificial intelligence (AI) is a theory, a method, a technology, or an application system that simulates, extends, and expands human intelligence by using a digital computer or a machine controlled by the digital computer, to perceive an environment, obtain knowledge, and achieve an optimal result based on the knowledge. In other words, artificial intelligence is a branch of computer science, and is intended to understand essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence means studying design principles and implementation methods of various intelligent machines, so that the machines have perceiving, inference, and decision-making functions. Researches in the field of artificial intelligence include researches on robots, natural language processing, computer vision, decision-making and inference, human-machine interaction, recommendation and search, AI basic theories, and the like. 
     Autonomous driving is a mainstream application in the field of artificial intelligence. The autonomous driving technology depends on computer vision, a radar, a monitoring apparatus, a global positioning system, and the like that collaborate with each other, to implement autonomous driving of a motor vehicle without human intervention. An autonomous vehicle uses various computing systems to assist in transporting passengers from one location to another location. Some autonomous vehicles may require some initial or continuous input from operators (such as navigators, drivers, or passengers). An autonomous vehicle allows an operator to switch from a manual operation mode to an autonomous driving mode or allows a mode between the manual operation mode and the autonomous driving mode. Because the autonomous driving technology does not require a human to drive a motor vehicle, can theoretically avoid human driving mistakes effectively, reduce traffic accidents, and improve road transportation efficiency, the autonomous driving technology attracts increasing attention. 
     In a survey of 2,000 people, American Telephone and Telegraph Co. (AT&amp;T) found that approximately 10% of drivers use mobile phones to make video calls during driving. Currently, for a widely used in-vehicle video call means, a video call presentation effect, especially an angle of view, is greatly affected by a driving environment. For example, an angle of view used to present an image of a driver that is displayed at the other end of a remote conference is closely related to a location of a video call apparatus (such as a mobile phone) of the driver, and with bumping of a vehicle, the video call apparatus greatly affects an image presentation effect and video conference experience. 
     SUMMARY 
     Embodiments of the present application provide a method for presenting a face in a video call, a video call apparatus, and a vehicle. According to the embodiments of the present application, during a video call, a peer user can see a 3D avatar that is of a user and that is at a preset presentation angle in real time, to improve the video call experience. 
     According to a first aspect, an embodiment of the present application provides a method for presenting a face in a video call, including: obtaining a key feature point of a facial expression of a user based on a face image of the user in a video call process; driving a 3D head image of the user by using the key feature point of the facial expression of the user, to obtain a target 3D avatar of the user, where the target 3D avatar of the user has an expression of the user; rotating the target 3D avatar based on a preset target presentation angle, to obtain a target 3D avatar at the preset presentation angle; and sending the target 3D avatar at the preset presentation angle to a peer video call device. 
     During a video call, the 3D head avatar of the user is driven based on the key feature point that is of the facial expression of the user and that is obtained based on the face image of the user in the video call process, so that a video peer user can see an expression of the user in real time; and the 3D avatar that is of the user and that is at the preset presentation angle is obtained, and the obtained 3D avatar at the preset presentation angle is sent to the peer video call device, so that a peer user can see the head image that is of the user and that is at the preset angle, to improve the video call experience. 
     In a feasible embodiment, the method in the present application further includes: constructing the 3D head image of the user based on the face image of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user, and N is an integer greater than 0; and the constructing the 3D head image of the user based on the face image of the user includes: obtaining first 3D head point cloud information of the user based on the N infrared images of the user; and constructing the 3D head image of the user based on the first 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user, and N is an integer greater than 0; and the constructing the 3D head image of the user based on the face image of the user includes: obtaining second 3D head point cloud information of the user based on the N infrared images of the user and the color face image of the user; and constructing the 3D head image of the user based on the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user further includes the color face image of the user; and the method in the present application further includes: obtaining a face texture feature of the user based on the color face image of the user; and the constructing the 3D head image of the user based on the 3D head point cloud information of the user includes: constructing the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image, and the 3D head point cloud information of the user includes the first 3D head point cloud information of the user or the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user; and the constructing the 3D head image of the user based on the face image of the user includes: inputting the color face image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user; and constructing the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     Compared with the 3D head image that is of the user and that is constructed based on the 3D head point cloud information obtained based on the infrared images, the 3D head image that is of the user and that is constructed based on the 3D head point cloud information and the face texture feature that are obtained based on the color face image of the user and the infrared images presents more and clearer face details of the user. This improves user experience in a video call or a video conference. 
     In a feasible embodiment, the feature extraction model includes a 3D head feature extraction network and a texture feature extraction network; and the inputting the color face image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user includes: inputting the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user; and inputting the color face image of the user into the texture feature extraction network for calculation, to obtain the face texture feature of the user. 
     In a feasible embodiment, the 3D head feature extraction network is a neural network that uses an encoder-decoder architecture; and the inputting the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user includes: obtaining N image pairs based on the color face image of the user and the N infrared images of the user, where each of the N image pairs includes a color image and an infrared image of the user, the color image in the N image pairs is the color face image of the user, and infrared images in the N image pairs are respectively from the N infrared images of the user; and inputting the N image pairs into the neural network with the encoder-decoder architecture for calculation, to obtain the 3D head point cloud information of the user. 
     In a feasible embodiment, the preset presentation angle is obtained based on the N infrared images of the user. 
     In a feasible embodiment, the method in the present application further includes: obtaining the 3D head image of the user based on a color depth image; and the obtaining the 3D head image of the user based on a color depth image includes: obtaining 3D head point cloud information of the user and a face texture feature of the user based on the color depth image; and constructing the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     In a feasible embodiment, the preset presentation angle is obtained based on the color depth image. 
     In a feasible embodiment, in a video call process, a plurality of infrared images of the user are obtained in real time, 3D head point cloud information of the user is obtained based on the plurality of infrared images of the user, and then the 3D head image of the user is constructed based on the 3D head point cloud information of the user and the previously obtained 3D head point cloud information of the user. According to the solution in this embodiment, the 3D head image of the user is constructed by continuously obtaining the infrared images of the user, to optimize the 3D head image of the user, so as to improve user experience in a video call. 
     According to a second aspect, an embodiment of the present application further provides a video call apparatus, including: an obtaining unit, configured to obtain a key feature point of a facial expression of a user based on a face image of the user in a video call process; a drive unit, configured to drive a 3D head image of the user by using the key feature point of the facial expression of the user, to obtain a target 3D avatar of the user, where the target 3D avatar of the user has an expression of the user; a rotation unit, configured to rotate the target 3D avatar based on a preset presentation angle, to obtain a target 3D avatar at the preset presentation angle; and a sending unit, configured to send the target 3D avatar at the preset presentation angle to a peer video call device. 
     In a feasible embodiment, the video call apparatus further includes: a construction unit, configured to construct the 3D head image of the user based on the face image of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user, and N is an integer greater than 0; and the construction unit is specifically configured to: obtain first 3D head point cloud information of the user based on the N infrared images of the user; and construct the 3D head image of the user based on the first 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user, and N is an integer greater than 0; and the construction unit is specifically configured to: obtain second 3D head point cloud information of the user based on the N infrared images of the user and the color face image of the user; and construct the 3D head image of the user based on the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user further includes the color face image of the user. 
     The obtaining unit is further configured to obtain a face texture feature of the user based on the color face image of the user. 
     The construction unit is specifically configured to: construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image, and the 3D head point cloud information of the user includes the first 3D head point cloud information of the user or the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color head image of the user; and the construction unit is specifically configured to: input the color head image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user; and construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     In a feasible embodiment, the feature extraction model includes a 3D head feature extraction network and a texture feature extraction network; and in an aspect of inputting the color head image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user, the construction unit is specifically configured to: input the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user; and input the color face image of the user into the texture feature extraction network for calculation, to obtain the face texture feature of the user. 
     In a feasible embodiment, the 3D head feature extraction network is a neural network that uses an encoder-decoder architecture; and in an aspect of inputting the color head image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user, the construction unit is specifically configured to: obtain N image pairs based on the color face image of the user and the N infrared images of the user, where each of the N image pairs includes a color image and an infrared image of the user, the color image in the N image pairs is the color face image of the user, and infrared images in the N image pairs are respectively from the N infrared images of the user; and input the N image pairs into the neural network with the encoder-decoder architecture for calculation, to obtain the 3D head point cloud information of the user. 
     In a feasible embodiment, the preset presentation angle is obtained based on the N infrared images of the user. 
     In a feasible embodiment, the face image of the user is a color depth image; and the obtaining unit is further configured to obtain the 3D head image of the user based on the color depth image. 
     In an aspect of obtaining the 3D head image of the user based on the color depth image, the construction unit is specifically configured to: obtain 3D head point cloud information of the user and a face texture feature of the user based on the color depth image; and construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     In a feasible embodiment, the preset presentation angle is obtained based on the color depth image. 
     According to a third aspect, an embodiment of the present application provides a vehicle. The vehicle includes a video call system, the video call system includes a processor and a communications apparatus, and the processor is connected to the communications apparatus. 
     The processor is configured to: obtain a key feature point of a facial expression of a user based on a face image of the user in a video call process; drive a 3D head image of the user by using the key feature point of the facial expression of the user, to obtain a target 3D avatar of the user, where the target 3D avatar of the user has an expression of the user; rotate the target 3D avatar based on a preset presentation angle, to obtain a target 3D avatar at the preset presentation angle; and transmit the target 3D avatar at the preset presentation angle to the communications apparatus. 
     The communications apparatus is configured to send the target 3D avatar at the preset presentation angle to a peer video call device. 
     In a feasible embodiment, the processor is further configured to construct the 3D head image of the user based on the face image of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user, and N is an integer greater than 0; and in an aspect of constructing the 3D head image of the user based on the face image of the user, the processor is specifically configured to: obtain first 3D head point cloud information of the user based on the N infrared images of the user; and construct the 3D head image of the user based on the first 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user, and N is an integer greater than 0; and in an aspect of constructing the 3D head image of the user based on the face image of the user, the processor is specifically configured to: obtain second 3D head point cloud information of the user based on the N infrared images of the user and the color face image of the user; and construct the 3D head image of the user based on the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user further includes the color face image of the user; and the processor is further configured to obtain a face texture feature of the user based on the color face image of the user. 
     In an aspect of constructing the 3D head image of the user based on the face image of the user, the processor is specifically configured to: construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image, and the 3D head point cloud information of the user includes the first 3D head point cloud information of the user or the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user; and in an aspect of constructing the 3D head image of the user based on the face image of the user, the processor is specifically configured to: input the color face image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user; and construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     In a feasible embodiment, the feature extraction model includes a 3D head feature extraction network and a texture feature extraction network; and in an aspect of inputting the color face image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user, the processor is specifically configured to: input the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user; and input the color face image of the user into the texture feature extraction network for calculation, to obtain the face texture feature of the user. 
     In a feasible embodiment, the 3D head feature extraction network is a neural network that uses an encoder-decoder architecture; and in an aspect of inputting the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user, the processor is specifically configured to: obtain N image pairs based on the color face image of the user and the N infrared images of the user, where each of the N image pairs includes a color image and an infrared image of the user, the color image in the N image pairs is the color face image of the user, and infrared images in the N image pairs are respectively from the N infrared images of the user; and input the N image pairs into the neural network with the encoder-decoder architecture for calculation, to obtain the 3D head point cloud information of the user. 
     In a feasible embodiment, the preset presentation angle is obtained based on the N infrared images of the user. 
     In a feasible embodiment, the video call system further includes an infrared camera, and the infrared camera is connected to the processor. 
     The infrared camera is configured to: obtain the N infrared images of the user, and transmit the N infrared images of the user to the processor. 
     In a feasible embodiment, the processor is further configured to obtain the 3D head image of the user based on a color depth image. 
     In an aspect of obtaining the 3D head image of the user based on a color depth image, the processor is specifically configured to: obtain 3D head point cloud information of the user and a face texture feature of the user based on the color depth image; and construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     In a feasible embodiment, the preset presentation angle is obtained based on the color depth image. 
     In a feasible embodiment, the video call system further includes a depth camera, and the depth camera is connected to the processor. 
     The depth camera is configured to: obtain the color depth image, and transmit the color depth image to the processor. 
     According to a fourth aspect, an embodiment of the present application provides a system. The system includes a vehicle and a server, the vehicle includes a video call system, and the video call system includes a processor and a communications apparatus. 
     The server is configured to obtain a 3D head image of a user based on a face image of the user. 
     The communications apparatus is configured to: obtain the 3D head image of the user from the server, and transmit the 3D head image of the user to the processor. 
     The processor is configured to: obtain a key feature point of a facial expression of the user based on the face image of the user in a video call process; drive the 3D head image of the user by using the key feature point of the facial expression of the user, to obtain a target 3D avatar of the user, where the target 3D avatar of the user has an expression of the user; rotate the target 3D avatar based on a preset presentation angle, to obtain a target 3D avatar at the preset presentation angle; and transmit the target 3D avatar at the preset presentation angle to the communications apparatus. 
     The communications apparatus is configured to send the target 3D avatar at the preset presentation angle to a peer video call device. 
     In a feasible embodiment, the server is specifically configured to construct the 3D head image of the user based on the face image of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user, and N is an integer greater than 0; and in an aspect of constructing the 3D head image of the user based on the face image of the user, the server is specifically configured to: obtain first 3D head point cloud information of the user based on the N infrared images of the user; and construct the 3D head image of the user based on the first 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user, and N is an integer greater than 0; and in an aspect of constructing the 3D head image of the user based on the face image of the user, the server is specifically configured to: obtain second 3D head point cloud information of the user based on the N infrared images of the user and the color face image of the user; and construct the 3D head image of the user based on the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user further includes the color face image of the user; and the processor is further configured to obtain a face texture feature of the user based on the color face image of the user. 
     In an aspect of constructing the 3D head image of the user based on the face image of the user, the server is specifically configured to: construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image, and the 3D head point cloud information of the user includes the first 3D head point cloud information of the user or the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user; and in an aspect of constructing the 3D head image of the user based on the face image of the user, the server is specifically configured to: input the color face image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user; and construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     In a feasible embodiment, the feature extraction model includes a 3D head feature extraction network and a texture feature extraction network; and in an aspect of inputting the color face image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user, the server is specifically configured to: input the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user; and input the color face image of the user into the texture feature extraction network for calculation, to obtain the face texture feature of the user. 
     In a feasible embodiment, the 3D head feature extraction network is a neural network that uses an encoder-decoder architecture; and in an aspect of inputting the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user, the server is specifically configured to: obtain N image pairs based on the color face image of the user and the N infrared images of the user, where each of the N image pairs includes a color image and an infrared image of the user, the color image in the N image pairs is the color face image of the user, and infrared images in the N image pairs are respectively from the N infrared images of the user; and input the N image pairs into the neural network with the encoder-decoder architecture for calculation, to obtain the 3D head point cloud information of the user. 
     In a feasible embodiment, the preset presentation angle is obtained based on the N infrared images of the user. 
     In a feasible embodiment, the server is further configured to obtain the 3D head image of the user based on a color depth image. 
     In an aspect of obtaining the 3D head image of the user based on a color depth image, the server is specifically configured to: obtain 3D head point cloud information of the user and a face texture feature of the user based on the color depth image; and construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     Further, the preset presentation angle is obtained based on the color depth image. 
     These aspects or other aspects of the present application are clearer and more comprehensible in descriptions of the following embodiments. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       To describe the technical solutions in the embodiments of the present application more clearly, the following briefly describes the accompanying drawings required for describing the embodiments. It is clear that the accompanying drawings in the following descriptions show merely some embodiments of the present application, and a person of ordinary skill in the art may derive other drawings from these accompanying drawings without creative efforts. 
         FIG. 1  is a schematic diagram of a structure of an autonomous driving vehicle according to an embodiment of the present application; 
         FIG. 2 a    and  FIG. 2 b    are a schematic diagram of a video call scenario according to an embodiment of the present application; 
         FIG. 3  is a schematic flowchart of a method for presenting a face in a video call according to an embodiment of the present application; 
         FIG. 4  is a schematic architectural diagram of a 3D head feature extraction network according to an embodiment of the present application; 
         FIG. 5  is a schematic architectural diagram of a dual-channel cross-modal feature extraction network according to an embodiment of the present application; 
         FIG. 6  is a schematic flowchart of another method for presenting a face in a video call according to an embodiment of the present application; 
         FIG. 7  is a schematic flowchart of another method for presenting a face in a video call according to an embodiment of the present application; 
         FIG. 8  is a schematic diagram of installation locations of cameras in a vehicle; 
         FIG. 9  is a schematic flowchart of another method for presenting a face in a video call according to an embodiment of the present application; 
         FIG. 10  is a schematic diagram of a structure of a video call apparatus according to an embodiment of the present application; 
         FIG. 11  is a schematic architectural diagram of a video call system according to an embodiment of the present application; 
         FIG. 12  is a schematic diagram of a structure of a system according to an embodiment of the present application; 
         FIG. 13  is a schematic diagram of a structure of another video call apparatus according to an embodiment of the present application; 
         FIG. 14  is a schematic diagram of a structure of a neural-network processing unit according to an embodiment of the present application; and 
         FIG. 15  is a schematic diagram of a structure of a computer program product according to an embodiment of the present application. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     The following describes the embodiments of this application with reference to accompanying drawings. 
       FIG. 1  is a functional block diagram of a vehicle  100  according to an embodiment of the present application. In an embodiment, the vehicle  100  is configured to be in a fully or partially autonomous driving mode. For example, when the vehicle  100  is in the autonomous driving mode, the vehicle  100  may control the vehicle  100 , and may determine current statuses of the vehicle and an ambient environment of the vehicle based on human operations, determine possible behavior of at least one another vehicle in the ambient environment, determine a confidence level corresponding to a possibility that the another vehicle performs the possible behavior, and control the vehicle  100  based on the determined information. When the vehicle  100  is in the autonomous driving mode, the vehicle  100  may be set to operate without interaction with a person. 
     The vehicle  100  may include various subsystems, for example, a travel system  102 , a sensor system  104 , a control system  106 , one or more peripheral devices  108 , a power supply  110 , a computer system  112 , and a user interface  116 . Optionally, the vehicle  100  may include fewer or more subsystems, and each subsystem may include a plurality of elements. In addition, all the subsystems and elements of the vehicle  100  may be wiredly or wirelessly interconnected to each other. 
     The travel system  102  may include a component that provides power for the vehicle  100  to move. In an embodiment, the travel system  102  may include an engine  118 , an energy source  119 , a transmission apparatus  120 , and a wheel/tire  121 . The engine  118  may be an internal combustion engine, an electric motor, an air compression engine, or a combination of other types of engines, for example, a hybrid engine including a gasoline engine and an electric motor, or a hybrid engine including an internal combustion engine and an air compression engine. The engine  118  converts the energy source  119  into mechanical energy. 
     Examples of the energy source  119  include gasoline, diesel, other oil-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other power sources. The energy source  119  may also provide energy for another system of the vehicle  100 . 
     The transmission apparatus  120  may transmit mechanical power from the engine  118  to the wheel  121 . The transmission apparatus  120  may include a gearbox, a differential, and a drive shaft. In an embodiment, the transmission apparatus  120  may further include another component, for example, a clutch. The drive shaft may include one or more shafts that may be coupled to one or more wheels  121 . 
     The sensor system  104  may include several sensors that sense information about the ambient environment of the vehicle  100 . For example, the sensor system  104  may include a positioning system  122  (where the positioning system may be a GPS, a BeiDou system, or another positioning system), an inertial measurement unit (IMU)  124 , a radar  126 , a laser rangefinder  128 , and a camera  130 . The sensor system  104  may further include sensors (for example, an in-vehicle air quality monitor, a fuel gauge, and an oil temperature gauge) in an internal system of the monitored vehicle  100 . Sensor data from one or more of these sensors can be used to detect an object and corresponding features (a location, a shape, a direction, a speed, and the like) of the object. Such detection and identification are key functions of a safety operation of the autonomous vehicle  100 . 
     The positioning system  122  may be configured to estimate a geographical location of the vehicle  100 . The IMU  124  is configured to sense location and orientation changes of the vehicle  100  based on inertial acceleration. In an embodiment, the IMU  124  may be a combination of an accelerometer and a gyroscope. 
     The radar  126  may sense an object in the ambient environment of the vehicle  100  by using a radio signal. In some embodiments, in addition to sensing the object, the radar  126  may be further configured to sense a speed and/or a moving direction of the object. 
     The laser rangefinder  128  may sense, by using a laser, an object in an environment in which the vehicle  100  is located. In some embodiments, the laser rangefinder  128  may include one or more laser sources, a laser scanner, one or more detectors, and another system component. 
     The camera  130  may be configured to capture a plurality of images of the ambient environment of the vehicle  100 . The camera  130  may be a static camera or a video camera. 
     The control system  106  controls operations of the vehicle  100  and the components of the vehicle  100 . The control system  106  may include various elements, including a steering system  132 , a throttle  134 , a brake unit  136 , a sensor fusion algorithm  138 , a computer vision system  140 , a route control system  142 , and an obstacle avoidance system  144 . 
     The steering system  132  may operate to adjust a moving direction of the vehicle  100 . For example, in an embodiment, the steering system  132  may be a steering wheel system. 
     The throttle  134  is configured to control an operating speed of the engine  118  and further control a speed of the vehicle  100 . 
     The brake unit  136  is configured to control the vehicle  100  to decelerate. The brake unit  136  may use friction to reduce a rotational speed of the wheel  121 . In another embodiment, the brake unit  136  may convert kinetic energy of the wheel  121  into a current. The brake unit  136  may alternatively reduce a rotational speed of the wheel  121  by using other methods, to control the speed of the vehicle  100 . 
     The computer vision system  140  may operate to process and analyze images captured by the camera  130  to identify objects and/or features in the ambient environment of the vehicle  100 . The objects and/or features may include traffic signals, road boundaries, and obstacles. The computer vision system  140  may use an object recognition algorithm, a structure from motion (SFM) algorithm, video tracking, and other computer vision technologies. In some embodiments, the computer vision system  140  may be configured to: draw a map for an environment, track an object, estimate a speed of the object, and the like. 
     The route control system  142  is configured to determine a travel route of the vehicle  100 . In some embodiments, the route control system  142  may determine the travel route for the vehicle  100  with reference to data from the sensor  138 , the GPS  122 , and one or more predetermined maps. 
     The obstacle avoidance system  144  is configured to: identify, evaluate, and avoid or otherwise bypass a potential obstacle in the environment of the vehicle  100 . 
     Certainly, for example, the control system  106  may add or alternatively include components other than those shown and described. Alternatively, the control system  106  may not include some of the foregoing components. 
     The vehicle  100  interacts with an external sensor, another vehicle, another computer system, or a user by using the peripheral device  108 . The peripheral device  108  may include a wireless communications system  146 , a vehicle-mounted computer  148 , a microphone  150 , and/or a speaker  152 . 
     In some embodiments, the peripheral device  108  provides a means for a user of the vehicle  100  to interact with the user interface  116 . For example, the vehicle-mounted computer  148  may provide information for the user of the vehicle  100 . The user interface  116  may further operate the vehicle-mounted computer  148  to receive user input. The vehicle-mounted computer  148  may perform operations through a touchscreen. In another case, the peripheral device  108  may provide a means for the vehicle  100  to communicate with another device located in the vehicle. For example, the microphone  150  may receive audio (for example, a voice command or other audio input) from the user of the vehicle  100 . Similarly, the speaker  152  may output audio to the user of the vehicle  100 . 
     The wireless communications system  146  may wirelessly communicate with one or more devices directly or through a communications network. For example, the wireless communications system  146  may perform communication through a 3G cellular network such as CDMA, EVDO, or GSM/GPRS, perform communication through a 4G cellular network such as LTE, or perform communication through a 5G cellular network. The wireless communications system  146  may communicate with a wireless local area network (WLAN) through Wi-Fi. In some embodiments, the wireless communications system  146  may directly communicate with a device through an infrared link, Bluetooth, or ZigBee. Other wireless protocols, for example, various vehicle communications systems, such as the wireless communications system  146 , may include one or more dedicated short range communications (DSRC) devices, and these devices may include public and/or private data communication between vehicles and/or roadside stations. 
     The power supply  110  may supply power to various components of the vehicle  100 . In an embodiment, the power supply  110  may be a rechargeable lithium-ion or lead-acid battery. One or more battery packs of such a battery may be configured as the power supply to supply power to the components of the vehicle  100 . In some embodiments, the power supply  110  and the energy source  119  may be implemented together, for example, in some pure electric vehicles. 
     Some or all functions of the vehicle  100  are controlled by the computer system  112 . The computer system  112  may include at least one processor  113 . The processor  113  executes an instruction  115  stored in a non-transient computer-readable medium such as a data storage apparatus  114 . The computer system  112  may alternatively be a plurality of computing devices that control an individual component or a subsystem of the vehicle  100  in a distributed manner. 
     The processor  113  may be any conventional processor, such as a commercially available CPU. Alternatively, the processor may be a dedicated device such as an ASIC or another hardware-based processor. Although  FIG. 1  functionally illustrates the processor, the memory, and other elements of the computer system  112  in a same block, a person of ordinary skill in the art should understand that the processor, the computer, or the memory may actually include a plurality of processors, computers, or memories that may or may not be stored in a same physical housing. For example, the memory may be a hard disk drive, or another storage medium located in a housing different from that of the computer  110 . Therefore, a reference to the processor or the computer is understood as including a reference to a set of processors, computers, or memories that may or may not operate in parallel. Different from using a single processor to perform the steps described herein, some components such as a steering component and a deceleration component may include respective processors. The processor performs only computation related to a component-specific function. 
     In various aspects described herein, the processor may be located far away from the vehicle and wirelessly communicate with the vehicle. In other aspects, some of the processes described herein are performed on the processor disposed inside the vehicle, while others are performed by a remote processor. The processes include necessary steps for performing a single operation. 
     In some embodiments, the data storage apparatus  114  may include the instruction  115  (for example, program logic), and the instruction  115  may be executed by the processor  113  to perform various functions of the vehicle  100 , including the functions described above. The data storage apparatus  114  may further include additional instructions, including instructions for sending data to, receiving data from, interacting with, and/or controlling one or more of the travel system  102 , the sensor system  104 , the control system  106 , and the peripheral device  108 . 
     In addition to the instruction  115 , the data storage apparatus  114  may further store data, such as a road map, route information, a location, a direction, a speed, and other vehicle data of the vehicle, and other information. Such information may be used by the vehicle  100  and the computer system  112  when the vehicle  100  operates in an autonomous mode, a semi-autonomous mode, and/or a manual mode. 
     The camera  130  obtains a face image of the user in a video call process, and the processor  113  in the computer system  112  executes the instruction  115  stored in the memory  114 , to perform the following steps: obtaining a key feature point of a facial expression of the user based on the face image of the user in the video call process; driving a 3D head image of the user by using the key feature point of the facial expression of the user, to obtain a target 3D avatar of the user, where the target 3D avatar of the user has an expression of the user; rotating the target 3D avatar based on a preset target presentation angle, to obtain a target 3D avatar at the preset presentation angle; and sending the target 3D avatar at the preset presentation angle to a peer video call device. 
     The user interface  116  is configured to: provide information for or receive information from the user of the vehicle  100 . Optionally, the user interface  116  may include one or more input/output devices within a set of peripheral devices  108 , such as the wireless communications system  146 , the vehicle-mounted computer  148 , the microphone  150 , and the speaker  152 . 
     The computer system  112  may control functions of the vehicle  100  based on input received from various subsystems (for example, the travel system  102 , the sensor system  104 , and the control system  106 ) and from the user interface  116 . For example, the computer system  112  may use input from the control system  106  to control the steering unit  132  to avoid an obstacle detected by the sensor system  104  and the obstacle avoidance system  144 . In some embodiments, the computer system  112  may operate to provide control on the vehicle  100  and the subsystems of the vehicle  100  in many aspects. 
     Optionally, one or more of the foregoing components may be installed separately from or associated with the vehicle  100 . For example, the data storage apparatus  114  may be partially or completely separated from the vehicle  100 . The foregoing components may be communicatively coupled together in a wired and/or wireless manner. 
     Optionally, the components are merely examples. In actual application, components in the foregoing modules may be added or deleted based on an actual requirement.  FIG. 1  should not be construed as a limitation on the embodiments of the present application. 
     An autonomous vehicle traveling on a road, such as the vehicle  100 , may identify objects in the ambient environment of the vehicle  100  to determine to adjust a current speed. The objects may be the other vehicles, traffic control devices, or objects of other types. In some examples, the autonomous vehicle may independently consider each identified object, and may determine a to-be-adjusted speed of the autonomous vehicle based on characteristics of each identified object, such as a current speed of the object, acceleration of the object, and a distance between the object and the vehicle. 
     Optionally, the autonomous vehicle  100  or a computing device (such as the computer system  112 , the computer vision system  140 , and the data storage apparatus  114  in  FIG. 1 ) associated with the autonomous vehicle  100  may predict behavior of the identified object based on the characteristic of the identified object and a status (for example, traffic, rain, or ice on a road) of the ambient environment. Optionally, all the identified objects depend on behavior of each other, and therefore all the identified objects may be considered together to predict behavior of a single identified object. The vehicle  100  can adjust the speed of the vehicle  100  based on the predicted behavior of the identified object. In other words, the autonomous vehicle can determine, based on the predicted behavior of the object, a specific stable state (for example, acceleration, deceleration, or stop) to which the vehicle needs to be adjusted. In this process, another factor may also be considered to determine the speed of the vehicle  100 , for example, a horizontal location of the vehicle  100  on a road on which the vehicle travels, a curvature of the road, and proximity between a static object and a dynamic object. 
     In addition to providing an instruction for adjusting the speed of the autonomous vehicle, the computing device may further provide an instruction for modifying a steering angle of the vehicle  100 , so that the autonomous vehicle can follow a given track and/or maintain safe horizontal and vertical distances from an object (for example, a car in a neighboring lane on the road) near the autonomous vehicle. 
     The vehicle  100  may be a car, a truck, a motorcycle, a bus, a boat, an airplane, a helicopter, a lawn mower, a recreational vehicle, a playground vehicle, a construction device, a trolley, a golf cart, a train, a handcart, or the like. This is not specifically limited in the embodiments of the present application. 
     It should be understood that the user in this application may be considered as a driver. 
       FIG. 2 a    and  FIG. 2 b    are a schematic diagram of a video call scenario according to an embodiment of the present application. As shown in  FIG. 2 a    and  FIG. 2 b   , during driving, a driver needs to make a video call or a video conference. An image of the driver in a video call process or a video conference process is obtained by using a camera; a key feature point of a facial expression of the driver is obtained based on the face image; a 3D head avatar of the driver is driven by using the key feature point of the facial expression of the user, to obtain a target 3D avatar of the driver, where the target 3D avatar of the driver has an expression of the driver; the target 3D avatar of the driver is rotated based on a preset target presentation angle, to obtain a target 3D avatar at the preset presentation angle, where the 3D head avatar of the driver is constructed based on the face image of the driver; and the target 3D avatar at the preset presentation angle is sent to a peer video call device through a communications network, as shown in  FIG. 2   b.    
     In an example, the face image of the driver includes N infrared face images of the driver. Optionally, the N infrared face images of the driver may be multi-angle head images of the driver. Optionally, the 3D head avatar of the driver is constructed based on the N infrared face images of the driver. 
     Optionally, the face image of the driver includes the N infrared face images of the driver and a color face image of the driver. A color 3D head avatar of the driver is constructed based on the N infrared face images of the driver and the color face image of the driver. The 3D head avatar of the driver is driven by using the key point of the facial expression of the driver, to obtain the target 3D avatar of the driver, where the target 3D avatar of the driver is a color avatar. 
     Optionally, the 3D head avatar of the driver is obtained from a third-party server. 
     Optionally, the preset presentation angle is obtained based on the N infrared face images of the driver. 
     As shown in  FIG. 2 a   , the color face image of the driver is obtained by a cockpit surveillance camera, and the N infrared face images of the driver are obtained by a driver surveillance camera. 
     It should be noted that the cockpit surveillance camera is a color camera and the driver surveillance camera is an infrared camera. 
     In an example, the face image of the driver is a color depth image, the camera is a depth camera, and the depth camera may be a TOF camera, a binocular camera, or another depth camera. The 3D head avatar of the driver is obtained based on the color depth image. Optionally, the preset presentation angle is obtained based on the depth camera. 
     It should be noted herein that, for a specific process of implementing the scenario shown in  FIG. 2 a    and  FIG. 2 b   , refer to related descriptions in the following embodiments. 
       FIG. 3  is a schematic flowchart of a method for presenting a face in a video call according to an embodiment of the present application. As shown in  FIG. 3 , the method includes the following steps. 
     S 301 : Obtain a key point of a facial expression of a user based on a face image of the user in a video call process. 
     Optionally, the face image of the user in the video call process includes an infrared image of the user, and the key feature point of the facial expression of the user is obtained based on the infrared image of the user. 
     S 302 : Drive a 3D head image of the user by using the key feature point of the facial expression of the user, to obtain a target 3D avatar of the user. 
     The target 3D avatar of the user has an expression of the user. 
     In a feasible embodiment, the method in the present application further includes: constructing the 3D head image of the user based on the face image of the user. 
     Further, the face image of the user includes N infrared images of the user, and N is an integer greater than 0; and the constructing the 3D head image of the user based on the face image of the user includes: obtaining first 3D head point cloud information of the user based on the N infrared images of the user; and constructing the 3D head image of the user based on the first 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user, and N is an integer greater than 0; and the constructing the 3D head image of the user based on the face image of the user includes: obtaining second 3D head point cloud information of the user based on the N infrared images of the user and the color face image of the user; and constructing the 3D head image of the user based on the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user further includes the color face image of the user; and the method in the present application further includes: obtaining a face texture feature of the user based on the color face image of the user; and the constructing the 3D head image of the user based on the 3D head point cloud information of the user includes: constructing the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image, and the 3D head point cloud information of the user includes the first 3D head point cloud information of the user or the second 3D head point cloud information of the user. 
     Specifically, the obtaining a face texture feature of the user based on the color face image of the user is specifically inputting the color face image of the user into a texture feature extraction network for calculation, to obtain the face texture feature of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user; and the constructing the 3D head image of the user based on the face image of the user includes: inputting the color face image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user; and constructing the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     In a feasible embodiment, the feature extraction model includes a 3D head feature extraction network and a texture feature extraction network; and the inputting the color face image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user includes: inputting the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user; and inputting the color face image of the user into the texture feature extraction network for calculation, to obtain the face texture feature of the user. 
     In a feasible embodiment, the 3D head feature extraction network is a neural network that uses an encoder-decoder architecture; and the inputting the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user includes: obtaining N image pairs based on the color face image of the user and the N infrared images of the user, where each of the N image pairs includes a color image and an infrared image of the user, the color image in the N image pairs is the color face image of the user, and infrared images in the N image pairs are respectively from the N infrared images of the user; and inputting the N image pairs into the neural network with the encoder-decoder architecture for calculation, to obtain the 3D head point cloud information of the user. 
     Specifically, as shown in  FIG. 4 , the feature extraction model includes a 3D head feature extraction network and a texture feature extraction network. It should be noted that, in the 3D head feature extraction network, the input “infrared” is represented as an infrared face image of the user, and the input “visible light” is represented as a color face image of the user. The infrared face image of the user and the color face image of the user are input into the 3D head feature extraction network, to output 3D head point cloud information of the user. The color face image of the user is input into the texture feature extraction model, to output a face texture feature of the user. 
     As shown in  FIG. 4 , a front-end feature extraction model may also be referred to as a dual-channel cross-modal feature extraction network, and the network is a CNN-based twin neural network.  FIG. 5  is a schematic diagram of a structure of a dual-channel cross-modal feature extraction network. The network is a CNN-based twin neural network. Some or all weights in the twin neural network are the same, or two CNNs in the twin neural network share a weight. As shown in  FIG. 5 , input data in the twin neural network is an image pair, the image pair includes a color face image of a user, an infrared image of the user, and a similarity identifier, and the similarity identifier is used to indicate whether the infrared image of the user and the color face image of the user are the same, or whether the infrared image and the color frontal face image are images of a same user. 
     The image pair may be represented as (Xi, Xi′, Yi), where i indicates that the image pair is an i th  image pair in input image pairs, Xi is the color frontal face image, Xi′ represents the infrared image, and Yi is the similarity identifier in the image pair. A value of the similarity identifier is used to indicate whether the corresponding color face image Xi of the user and the corresponding infrared image Xi′ of the user are the same or are images of a same user. For example, a value 0 or “false” of the similarity identifier indicates that the corresponding color face image Xi of the user and the corresponding infrared image Xi′ of the user are different or are images of different users, and a value 1 or “true” of the similarity identifier indicates that the corresponding color face image Xi of the user and the corresponding infrared image Xi′ of the user are the same or are the images of the same user. 
     The color face image of the user and the infrared image of the user that are in the image pair are input into the twin neural network for calculation, so that operations including a convolution operation, a maximum pooling operation, a full connection operation, a feature fusion operation, and the like are implemented, and therefore a feature vector of the user is obtained. Then, the feature vector of the user is input into a back-end feature extraction model for calculation, to obtain 3D head point cloud information of the user. 
     It should be noted herein that the convolution operation, the maximum pooling operation, and the full connection operation are respectively implemented at a convolutional layer, a pooling layer, and a full connection layer in the twin neural network. 
     In a feasible embodiment, before the front-end feature extraction model is used, the twin neural network needs to be trained, to obtain the front-end feature extraction model. 
     Specifically, a plurality of image pairs are obtained, and then a color face image of the user and an infrared image of the user that are in each of the plurality of image pairs are input into a twin neural network model for calculation, to obtain a feature vector. Then, a loss value is calculated based on the feature vector and a loss function. Finally, a weight in the twin neural network model is adjusted based on the loss value. 
     The twin neural network model is repeatedly trained according to the foregoing method until the twin neural network model meets a use requirement. 
     In the dual-channel cross-modal feature extraction network (that is, the front-end feature extraction model), the used loss function is Contrastive Loss. This loss function can effectively handle a relative relationship between images in the network. An expression of Contrastive Loss is as follows: 
     
       
         
           
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     Herein, d=∥a n -b n ∥ 2 , and d represents a Euclidean distance between features of two samples (that is, the color face image of the user and the infrared image of the user); y is a label indicating whether the two samples match, y of 1 indicates that the two samples are similar or match, and y of 0 indicates that the two samples do not match; and the margin is a specified threshold. In addition, this loss function can effectively alleviate relative dependence of the training network on the images. 
     A back-end decoder (that is, the back-end feature extraction model) uses a transposed convolutional layer, and the loss function is represented by 2-norms between coordinates of each pixel and corresponding real values. The back-end feature extraction model receives a feature vector extracted by the dual-channel cross-modal feature extraction network and uses the feature vector as input, and outputs 3D point cloud coordinates (65,536), that is, the 3D head point cloud information of the user. 
     The dual-channel cross-modal feature extraction network is diversified in a specific network implementation form. Some hidden layers may be shared in two parallel convolutional neural networks, to implement parameter sharing and cross-modal information fusion, or both the infrared image and the color image may be input into a single convolutional network, to implement cross-modal information extraction and fusion, or parameters of two completely parallel and independent convolutional neural networks are enabled to be as similar as possible by using a regularization method, to implement information fusion. 
     The texture feature extraction model is a common convolutional neural network, for example, ResNet or MobileNet. A middle layer of the dual-channel cross-modal feature extraction network is used as output, to extract a face texture feature. 
     In a feasible embodiment, in a video call process, a plurality of infrared images of the user are obtained in real time, 3D head point cloud information of the user is obtained based on the plurality of infrared images of the user, and then the 3D head image of the user is constructed based on the 3D head point cloud information of the user and the previously obtained 3D head point cloud information of the user. According to the solution in this embodiment, the 3D head image of the user is constructed by continuously obtaining the infrared images of the user, to optimize the 3D head image of the user, so as to improve user experience in a video call. 
     In another feasible embodiment, the method in the present application further includes: obtaining the 3D head image of the user based on a color depth image; and the obtaining the 3D head image of the user based on a color depth image includes: obtaining 3D head point cloud information of the user and a face texture feature of the user based on the color depth image; and constructing the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     In another feasible embodiment, the 3D head image of the user is obtained from a third-party server. 
     S 303 : Rotate the target 3D avatar based on a preset presentation angle, to obtain a target 3D avatar at the preset presentation angle. 
     The target 3D avatar at the preset presentation angle may be an avatar of presenting a frontal face of the user, an avatar of presenting a side face of the user, or an avatar that is of the user and that is at another angle. 
     In a feasible embodiment, the preset presentation angle is obtained based on the N infrared images of the user. 
     In a feasible embodiment, the preset presentation angle is obtained based on the color depth image. 
     Specifically, an angle V at which the target 3D avatar needs to be rotated from a current head presentation angle of the user to the preset presentation angle is determined based on the N infrared images of the user or the color depth images, and the target 3D avatar is rotated by the angle V, so that a presentation angle of the target 3D avatar is the preset presentation angle. 
     In another specific embodiment, a face image of a user in an in-vehicle video call process is obtained, where the face image includes a color face image of the user and a plurality of infrared images of the user; a 3D head image of the user is constructed based on the color face image of the user and the plurality of infrared images of the user; a key feature point of a facial expression of the user is obtained based on the plurality of infrared images of the user; the 3D head image of the user is driven based on the key feature point of the facial expression of the user, to obtain a target 3D avatar; and the target 3D avatar is rotated based on a preset presentation angle, to obtain a target 3D avatar at the preset presentation angle. 
     The preset presentation angle may be preset, or may be obtained based on the plurality of infrared images of the user. 
     Optionally, the color face image of the user is a color frontal face image of the user. 
     S 304 : Send the target 3D avatar at the preset presentation angle to a peer video call device. 
     It can be learned that, in the solution of this embodiment of the present application, the key feature point of the facial expression of the user is obtained based on the face image of the user in the video call process; the 3D head image of the user is driven by using the key feature point of the facial expression of the user, to obtain the target 3D avatar of the user, where the target 3D avatar of the user has the expression of the user; the target 3D avatar is rotated based on the preset target presentation angle, to obtain the target 3D avatar at the preset presentation angle; and the target 3D avatar at the preset presentation angle is sent to the peer video call device. During a video call, the 3D head avatar of the user is driven based on the key feature point that is of the facial expression of the user and that is obtained based on the face image of the user in the video call process, so that a video peer user can see an expression of the user in real time; and the 3D avatar that is of the user and that is at the preset presentation angle is obtained, and the obtained 3D avatar at the preset presentation angle is sent to the peer video call device, so that a peer user can see the head image that is of the user and that is at the preset angle, to improve the video call experience. 
       FIG. 6  is a schematic flowchart of another method for presenting a face in a video call according to an embodiment of the present application. As shown in  FIG. 6 , the method includes the following steps: 
     Step  1 : Obtain a color frontal face image of a user and a plurality of consecutive infrared images of the user; extract a 3D face feature based on the color frontal face image of the user and the plurality of consecutive infrared images of the user, to obtain 3D head point cloud information of the user; extract a face texture feature based on the color frontal face image of the user, to obtain the face texture feature of the user; and extract a 3D face feature based on the plurality of infrared images of the user, to obtain a key feature point of a facial expression. 
     Step  2 : Construct a 3D face based on the 3D head point cloud information of the user and the face texture feature of the user, to obtain a color 3D head image of the user. 
     Step  3 : Drive the color 3D head image of the user based on the key feature point of the facial expression, to obtain a target 3D avatar, where the target 3D avatar of the user has a real-time expression of the user. 
     Step  4 : Estimate a head posture based on the plurality of infrared images of the user to obtain the head posture, determine a rotation angle based on the head posture, and then rotate the target 3D avatar based on the rotation angle to obtain a rotated target 3D avatar, where the rotated target 3D avatar is a frontal face avatar of the user. 
     Step  5 : Display the rotated target 3D avatar. 
     Finally, the rotated target 3D avatar is sent to a peer video call device. 
     For a specific process of implementing the embodiment shown in  FIG. 6 , refer to related descriptions in an embodiment shown in  FIG. 7 . 
       FIG. 7  is a schematic flowchart of another method for presenting a face in a video call according to an embodiment of the present application. As shown in  FIG. 7 , the method includes the following steps. 
     S 701 : Obtain a color frontal face image of a user and obtain N consecutive infrared images of the user in real time, where N is an integer greater than 1. 
     The color frontal face image of the user is collected by a cockpit surveillance camera, and the N consecutive infrared images of the user are obtained by a driver surveillance camera. The cockpit surveillance camera is a high-definition color camera, and the driver surveillance camera is an infrared camera. 
     In an example, that the N consecutive infrared images of the user are obtained by a driver surveillance camera specifically includes: The N consecutive infrared images of the user are directly collected by the driver surveillance camera, or the N consecutive infrared images of the user are obtained from an infrared video of the user, where the infrared video of the user is collected by the driver surveillance camera. 
     Installation locations of the cockpit surveillance camera and the driver surveillance camera on a vehicle are shown in  FIG. 8 . Driver surveillance cameras are installed at locations {circle around (1)}, {circle around (2)}, and {circle around (4)} shown in  FIG. 8 , where the location {circle around (1)} is below a steering wheel and above a dashboard, and the locations {circle around (2)} and {circle around (4)} are left and right A-pillars of a cockpit. An installation principle of the driver surveillance camera is to capture all information about a head and a face of a driver. 
     The cockpit surveillance camera is installed at a location {circle around (3)} in  FIG. 8 , and the location {circle around (3)} is above a rearview mirror of the cockpit. An installation principle of the cockpit surveillance camera is to capture an entire cockpit environment. 
     S 702 : Construct a color 3D head image of the user based on the color frontal face image of the user and the N infrared images of the user. 
     Specifically, the color frontal face image of the user and the N infrared images of the user are input into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user; and the color 3D head avatar of the user is constructed based on the 3D head point cloud information of the user and the face texture feature of the user. 
     In a feasible embodiment, the feature extraction model includes a 3D head feature extraction network and a texture feature extraction network; and that the color frontal face image of the user and the N infrared images of the user are input into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user includes: inputting the color frontal face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user; and inputting the color frontal face image of the user into the texture feature extraction network for calculation, to obtain the face texture feature of the user. 
     In a feasible embodiment, the 3D head feature extraction network is a neural network that uses an encoder-decoder architecture; and the inputting the color frontal face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user includes: obtaining N image pairs based on the color frontal face image of the user and the N infrared images of the user, where each of the N image pairs includes a color image and an infrared image of the user, the color image in the N image pairs is the color face image of the user, and infrared images in the N image pairs are respectively from the N infrared images of the user; and inputting the N image pairs into the neural network with the encoder-decoder architecture for calculation, to obtain the 3D head point cloud information of the user. 
     It should be noted herein that, for specific descriptions of step S 702 , refer to the related descriptions of step S 302 . Details are not described herein again. 
     In a feasible embodiment, before a front-end feature extraction model is used, a twin neural network needs to be trained, to obtain the front-end feature extraction model. 
     S 703 : Obtain a key feature point of a facial expression of the user and a rotation angle of the user based on the N infrared images of the user. 
     That a rotation angle of the user is obtained based on the N infrared images of the user is specifically: estimating a head posture based on the N infrared images of the user, to obtain the head posture of the user, and further determining, based on the head posture of the user, a rotation angle at which the head posture is rotated to an angle at which a frontal face is presented in a field of view. 
     S 704 : Drive the color 3D head avatar of the user in real time by using an expression driving technology and the key feature point of the facial expression of the user, to obtain a target 3D avatar of the user. 
     The target 3D avatar of the user has a real-time expression of the user. 
     The expression driving technology enables the peer party to see a current expression of the user in real time during a video call, so as to improve video call experience. 
     It should be noted herein that, driving the color 3D head image of the user by using the expression driving technology and the key feature point of the facial expression of the user is common sense in the art. Details not described herein. 
     S 705 : Rotate the target 3D avatar based on the rotation angle, to obtain a rotated target 3D avatar. 
     After the rotated target 3D avatar is obtained, the rotated target 3D avatar is displayed, where the rotated target 3D avatar may be a frontal face avatar of the user, and the rotated target 3D avatar is transmitted to a peer video call device. 
     In a feasible embodiment, after the rotated target 3D avatar is obtained, the 3D avatar is sent to a device that makes a video call to the user, so that a frontal face of the user is presented during the video call. 
     It can be learned that, in the solution of this embodiment of this application, the expression of the driver is captured in real time based on an in-vehicle infrared camera (that is, the driver surveillance camera in the foregoing embodiment), to obtain the infrared image, and the infrared image is migrated to the color image of the user to complete expression driving, so that cross-domain migration between infrared and visible light is implemented. Because this application scenario is insensitive to light, an application scope of this solution is expanded. On the premise of complying with traffic laws, in this solution, real-time frontal face presentation and expression change can be performed in the image of the driver at any angle within a field of view of the cockpit surveillance camera, to ensure driving safety and improve user experience in a video conference. An existing resource in an in-vehicle environment is used, and a wearable device is not required. Therefore, the solution in this application is used more simply and conveniently. 
     In another specific embodiment, as shown in  FIG. 9 , a color depth image of a user is obtained by using a depth camera; a color 3D head image of the user is constructed based on the color depth image of the user; a key feature point of a facial expression of the user is obtained based on the color depth image of the user; a color 3D head image of the user is driven based on the key feature point of the facial expression of the user, to obtain a target 3D avatar of the user, where the target 3D avatar of the user has a real-time expression of the user; the target 3D avatar is rotated based on a preset presentation angle, to obtain a target 3D avatar at the preset presentation angle; and the target 3D avatar at the preset presentation angle is sent to a peer video call device. 
     In a feasible embodiment, that a color 3D head image of the user is constructed based on the color depth image of the user includes: obtaining 3D head point cloud information of the user and a face texture feature of the user from the color depth image of the user, and then constructing the color 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user. 
     It should be noted herein that, for specific related descriptions of the embodiment shown in  FIG. 9 , refer to the related descriptions of steps S 703  to S 705 . Details are not described herein again. 
     It should be noted herein that, this embodiment of the present application is not only applicable to an in-vehicle video call or an in-vehicle video conference, but also applicable to virtual social networking, AR wear (shopping), a video call, and holographic interaction. For a specific implementation process, refer to the related descriptions in the embodiments shown in  FIG. 3 ,  FIG. 7 , and  FIG. 8 . 
       FIG. 10  is a schematic diagram of a structure of a video call apparatus according to an embodiment of this application. As shown in  FIG. 10 , the video call apparatus  1000  includes: an obtaining unit  1001 , configured to obtain a key feature point of a facial expression of a user based on a face image of the user in a video call process; a drive unit  1002 , configured to drive a 3D head image of the user by using the key feature point of the facial expression of the user, to obtain a target 3D avatar of the user, where the target 3D avatar of the user has an expression of the user; a rotation unit  1003 , configured to rotate the target 3D avatar based on a preset presentation angle, to obtain a target 3D avatar at the preset presentation angle; and a sending unit  1004 , configured to send the target 3D avatar at the preset presentation angle to a peer video call device. 
     In a feasible embodiment, the video call apparatus  1000  further includes: a construction unit  1005 , configured to construct the 3D head image of the user based on the face image of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user, and N is an integer greater than 0; and the construction unit  1005  is specifically configured to: obtain first 3D head point cloud information of the user based on the N infrared images of the user; and construct the 3D head image of the user based on the first 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user, and N is an integer greater than 0; and the construction unit  1005  is specifically configured to: obtain second 3D head point cloud information of the user based on the N infrared images of the user and the color face image of the user; and construct the 3D head image of the user based on the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user further includes the color face image of the user. 
     The obtaining unit  1001  is further configured to obtain a face texture feature of the user based on the color face image of the user. 
     The construction unit  1005  is specifically configured to: construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image, and the 3D head point cloud information of the user includes the first 3D head point cloud information of the user or the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color head image of the user; and the construction unit  1005  is specifically configured to: input the color head image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user; and construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     In a feasible embodiment, the feature extraction model includes a 3D head feature extraction network and a texture feature extraction network; and in an aspect of inputting the color face image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user, the construction unit  1005  is specifically configured to: input the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user; and input the color face image of the user into the texture feature extraction network for calculation, to obtain the face texture feature of the user. 
     In a feasible embodiment, the 3D head feature extraction network is a neural network that uses an encoder-decoder architecture; and in an aspect of inputting the color head image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user, the construction unit  1005  is specifically configured to: obtain N image pairs based on the color face image of the user and the N infrared images of the user, where each of the N image pairs includes a color image and an infrared image of the user, the color image in the N image pairs is the color face image of the user, and infrared images in the N image pairs are respectively from the N infrared images of the user; and input the N image pairs into the neural network with the encoder-decoder architecture for calculation, to obtain the 3D head point cloud information of the user. 
     In a feasible embodiment, the preset presentation angle is obtained based on the N infrared images of the user. 
     In a feasible embodiment, the face image of the user is a color depth image; and the obtaining unit  1001  is further configured to obtain the 3D head image of the user based on the color depth image. 
     In an aspect of obtaining the 3D head image of the user based on the color depth image, the construction unit  1005  is specifically configured to: obtain 3D head point cloud information of the user and a face texture feature of the user based on the color depth image; and construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     In a feasible embodiment, the preset presentation angle is obtained based on the color depth image. 
     It should be noted that the foregoing units (the obtaining unit  1001 , the drive unit  1002 , the rotation unit  1003 , the sending unit  1004 , and the construction unit  1005 ) are configured to perform the related steps of the foregoing method. For example, the obtaining unit  1001  is configured to perform the related content in step S 301 , the drive unit  1002  and the construction unit  1005  are configured to perform the related content in step S 302 , the rotation unit  1003  is configured to perform the related content in step S 303 , and the sending unit  1004  is configured to perform the related content in step S 304 . 
     In this embodiment, the video call apparatus  1000  is presented in a form of a unit. The “unit” herein may be an application-specific integrated circuit (ASIC), a processor that executes one or more software or firmware programs and a memory, an integrated logic circuit, and/or another component that can provide the foregoing functions. In addition, the obtaining unit  1001 , the drive unit  1002 , the rotation unit  1003 , and the construction unit  1005  may be implemented by using a processor  1301  in a video call apparatus shown in  FIG. 13 . 
       FIG. 11  is a schematic diagram of a structure of a video call system of a vehicle according to an embodiment of the present application. As shown in  FIG. 11 , the video call system  1100  includes a processor  1101  and a communications apparatus  1102 , and the processor  1101  is connected to the communications apparatus  1102 . 
     The processor  1101  is configured to: obtain a key feature point of a facial expression of a user based on a face image of the user in a video call process; drive a 3D head image of the user by using the key feature point of the facial expression of the user, to obtain a target 3D avatar of the user, where the target 3D avatar of the user has an expression of the user; rotate the target 3D avatar based on a preset presentation angle, to obtain a target 3D avatar at the preset presentation angle; and transmit the target 3D avatar at the preset presentation angle to the communications apparatus. 
     The communications apparatus  1102  is configured to send the target 3D avatar at the preset presentation angle to a peer video call device. 
     In a feasible embodiment, the processor  1101  is further configured to construct the 3D head image of the user based on the face image of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user, and N is an integer greater than 0; and in an aspect of constructing the 3D head image of the user based on the face image of the user, the processor  1101  is specifically configured to: obtain first 3D head point cloud information of the user based on the N infrared images of the user; and construct the 3D head image of the user based on the first 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user, and N is an integer greater than 0; and in an aspect of constructing the 3D head image of the user based on the face image of the user, the processor  1101  is specifically configured to: obtain second 3D head point cloud information of the user based on the N infrared images of the user and the color face image of the user; and construct the 3D head image of the user based on the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user further includes the color face image of the user; and the processor  1101  is further configured to obtain a face texture feature of the user based on the color face image of the user. 
     In an aspect of constructing the 3D head image of the user based on the face image of the user, the processor  1101  is specifically configured to: construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image, and the 3D head point cloud information of the user includes the first 3D head point cloud information of the user or the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user; and in an aspect of constructing the 3D head image of the user based on the face image of the user, the processor  1101  is specifically configured to: input the color face image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user; and construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     In a feasible embodiment, the feature extraction model includes a 3D head feature extraction network and a texture feature extraction network; and in an aspect of inputting the color face image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user, the processor  1101  is specifically configured to: input the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user; and input the color face image of the user into the texture feature extraction network for calculation, to obtain the face texture feature of the user. 
     In a feasible embodiment, the 3D head feature extraction network is a neural network that uses an encoder-decoder architecture; and in an aspect of inputting the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user, the processor  1101  is specifically configured to: obtain N image pairs based on the color face image of the user and the N infrared images of the user, where each of the N image pairs includes a color image and an infrared image of the user, the color image in the N image pairs is the color face image of the user, and infrared images in the N image pairs are respectively from the N infrared images of the user; and input the N image pairs into the neural network with the encoder-decoder architecture for calculation, to obtain the 3D head point cloud information of the user. 
     In a feasible embodiment, the video call system further includes a color camera  1105 , and the color camera  1105  is connected to the processor  1101 . 
     The color camera  1105  is configured to: obtain the color face image of the user, and transmit the color face image of the user to the processor  1101 . 
     In a feasible embodiment, the preset presentation angle is obtained based on the N infrared images of the user. 
     In a feasible embodiment, the video call system further includes an infrared camera  1103 , and the infrared camera  1103  is connected to the processor  1101 . 
     The infrared camera  1103  is configured to: obtain the N infrared images of the user, and transmit the N infrared images of the user to the processor  1101 . 
     In a feasible embodiment, the processor  1101  is further configured to obtain the 3D head image of the user based on a color depth image. 
     In an aspect of obtaining the 3D head image of the user based on a color depth image, the processor  1101  is specifically configured to: obtain 3D head point cloud information of the user and a face texture feature of the user based on the color depth image; and construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     In a feasible embodiment, the preset presentation angle is obtained based on the color depth image. 
     In a feasible embodiment, the video call system further includes a depth camera  1104 , and the depth camera  1104  is connected to the processor  1101 . 
     The depth camera  1104  is configured to: obtain the color depth image, and transmit the color depth image to the processor. 
     It should be noted herein that a color frontal face image of the user is built in the processor  1101  or obtained from another device, and does not need to be obtained from the outside by using the color camera  1105 . Therefore, the color camera  1105  is represented by using a dashed box. 
     It should be noted herein that the processor  1101 , the communications apparatus  1102 , the infrared camera  1103 , the depth camera  1104 , and the color camera  1105  are configured to perform the related content in the embodiments shown in  FIG. 3 ,  FIG. 6 ,  FIG. 7 , and  FIG. 9 . 
       FIG. 12  is a schematic diagram of a structure of a system according to an embodiment of this application. As shown in  FIG. 12 , the system  1200  includes a vehicle  1202  and a server  1201 . The vehicle  1202  includes a video call system  1203 , and the video call system  1203  includes a processor  1204  and a communications apparatus  1205 . 
     The server  1201  is configured to obtain a 3D head image of a user based on a face image of the user. 
     The communications apparatus  1205  is configured to: obtain the 3D head image of the user from the server  1201 , and transmit the 3D head image of the user to the processor. 
     The processor  1204  is configured to: obtain a key feature point of a facial expression of the user based on the face image of the user in a video call process; drive the 3D head image of the user by using the key feature point of the facial expression of the user, to obtain a target 3D avatar of the user, where the target 3D avatar of the user has an expression of the user; rotate the target 3D avatar based on a preset presentation angle, to obtain a target 3D avatar at the preset presentation angle; and transmit the target 3D avatar at the preset presentation angle to the communications apparatus. 
     The communications apparatus  1205  is configured to send the target 3D avatar at the preset presentation angle to a peer video call device. 
     In a feasible embodiment, the server  1201  is specifically configured to construct the 3D head image of the user based on the face image of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user, and N is an integer greater than 0; and in an aspect of constructing the 3D head image of the user based on the face image of the user, the server  1201  is specifically configured to: obtain first 3D head point cloud information of the user based on the N infrared images of the user; and construct the 3D head image of the user based on the first 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user, and N is an integer greater than 0; and in an aspect of constructing the 3D head image of the user based on the face image of the user, the server  1201  is specifically configured to: obtain second 3D head point cloud information of the user based on the N infrared images of the user and the color face image of the user; and construct the 3D head image of the user based on the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user further includes the color face image of the user; and the server  1201  is further configured to obtain a face texture feature of the user based on the color face image of the user. 
     In an aspect of constructing the 3D head image of the user based on the face image of the user, the server  1201  is specifically configured to: construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image, and the 3D head point cloud information of the user includes the first 3D head point cloud information of the user or the second 3D head point cloud information of the user. 
     In a feasible embodiment, the face image of the user includes N infrared images of the user and a color face image of the user; and in an aspect of constructing the 3D head image of the user based on the face image of the user, the server  1201  is specifically configured to: input the color face image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user; and construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     In a feasible embodiment, the feature extraction model includes a 3D head feature extraction network and a texture feature extraction network; and in an aspect of inputting the color face image of the user and the N infrared images of the user into a feature extraction model for calculation, to obtain 3D head point cloud information of the user and a face texture feature of the user, the server  1201  is specifically configured to: input the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user; and input the color face image of the user into the texture feature extraction network for calculation, to obtain the face texture feature of the user. 
     In a feasible embodiment, the 3D head feature extraction network is a neural network that uses an encoder-decoder architecture; and in an aspect of inputting the color face image of the user and the N infrared images of the user into the 3D head feature extraction network for calculation, to obtain the 3D head point cloud information of the user, the server  1201  is specifically configured to: obtain N image pairs based on the color face image of the user and the N infrared images of the user, where each of the N image pairs includes a color image and an infrared image of the user, the color image in the N image pairs is the color face image of the user, and infrared images in the N image pairs are respectively from the N infrared images of the user; and input the N image pairs into the neural network with the encoder-decoder architecture for calculation, to obtain the 3D head point cloud information of the user. 
     In a feasible embodiment, the preset presentation angle is obtained based on the N infrared images of the user. 
     In a feasible embodiment, the server  1201  is further configured to obtain the 3D head image of the user based on a color depth image. 
     In an aspect of obtaining the 3D head image of the user based on a color depth image, the server  1201  is specifically configured to: obtain 3D head point cloud information of the user and a face texture feature of the user based on the color depth image; and construct the 3D head image of the user based on the 3D head point cloud information of the user and the face texture feature of the user, where the 3D head image of the user is a color image. 
     Further, the preset presentation angle is obtained based on the color depth image. 
     It should be noted herein that, for a specific process in which the server  1201  obtains the 3D head image of the user based on the face image of the user, refer to the related descriptions in step S 302 . Details are not described herein again. Certainly, a specific manner in which the server  1201  obtains the 3D head image of the user is not limited to the present application. 
     The video call apparatus  1300  shown in  FIG. 13  may be implemented in a structure in  FIG. 13 . The video call apparatus  1300  includes at least one processor  1301 , at least one memory  1302 , and at least one communications interface  1303 . The processor  1301 , the memory  1302 , and the communications interface  1303  are connected and communicate with each other through a communications bus. 
     The processor  1301  may be a general-purpose central processing unit (CPU), a microprocessor, an ASIC, or one or more integrated circuits for controlling program execution of the foregoing solution. 
     The communications interface  1303  is configured to communicate with another device or a communications network such as the Ethernet, a radio access network (RAN), or a WLAN. 
     The memory  1302  may be a read-only memory (ROM), another type of static storage device that can store static information and an instruction, a random access memory (RAM), or another type of dynamic storage device that can store information and an instruction, or may be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or another compact disc storage, an optical disc storage (including a compact disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, and the like), a magnetic disk storage medium or another magnetic storage device, or any other medium that can be configured to carry or store expected program code in a form of an instruction or a data structure and that can be accessed by a computer. However, this is not limited thereto. The memory may exist independently, and is connected to the processor through the bus. The memory may alternatively be integrated with the processor. 
     The memory  1302  is configured to store application program code for executing the foregoing solutions, and the processor  1301  controls the execution. The processor  1301  is configured to execute the application program code stored in the memory  1302 . 
     The code stored in the memory  1302  may be used to perform any one of the foregoing methods for presenting a face in a video call, for example, obtaining a key feature point of a facial expression of a user based on a face image of the user in a video call process; driving a 3D head image of the user by using the key feature point of the facial expression of the user, to obtain a target 3D avatar of the user, where the target 3D avatar of the user has an expression of the user; rotating the target 3D avatar based on a preset target presentation angle, to obtain a target 3D avatar at the preset presentation angle; and transmitting the target 3D avatar at the preset presentation angle to a peer video call device through the communications interface  1303 . 
     An embodiment of the present application further provides a computer storage medium. The computer storage medium may store a program, and when the program is executed, some or all of the steps of any one of the methods for presenting a face in a video call in the foregoing method embodiments are performed. 
       FIG. 14  is a structural diagram of hardware of a chip according to an embodiment of the present application. 
     As a coprocessor, a neural-network processing unit NPU  50  is mounted to a host CPU, and the host CPU assigns a task. A core part of the NPU is an operation circuit  503 , and a controller  504  controls the operation circuit  503  to extract data in a memory (a weight memory or an input memory) and perform an operation. 
     In some implementations, the operation circuit  503  includes a plurality of process engines (PEs). In some implementations, the operation circuit  503  is a two-dimensional systolic array. The operation circuit  503  may alternatively be a one-dimensional systolic array or another electronic circuit that can perform mathematical operations such as multiplication and addition. In some implementations, the operation circuit  503  is a general-purpose matrix processor. 
     For example, it is assumed that there are an input matrix A, a weight matrix B, and an output matrix C. The operation circuit fetches data corresponding to the matrix B from a weight memory  502 , and caches the data on each PE in the operation circuit. The operation circuit fetches data corresponding to the matrix A from an input memory  501 , performs a matrix operation on the matrix B, and stores an obtained partial result or an obtained final result of the matrix in an accumulator  508 . 
     A vector calculation unit  507  may perform further processing such as vector multiplication, vector addition, an exponent operation, a logarithm operation, or value comparison on an output of the operation circuit. For example, the vector calculation unit  507  may be configured to perform network calculation, such as pooling, batch normalization, or local response normalization, at a non-convolutional/non-FC layer in a neural network. 
     In some implementations, the vector calculation unit  507  can store a processed output vector in a unified buffer  506 . For example, the vector calculation unit  507  can apply a non-linear function to an output of the operation circuit  503 , for example, a vector of an accumulated value, so as to generate an activated value. In some implementations, the vector calculation unit  507  generates a normalized value, a combined value, or both a normalized value and a combined value. In some implementations, the processed output vector can be used as an activated input to the operation circuit  503 , for example, can be used at a subsequent layer in the neural network. 
     A feature extraction process in this embodiment of this application, for example, extracting 3D head point cloud information of a user from a color face image and an infrared image and extracting a face texture feature of the user from the color face image, may be performed by the vector calculation unit  507  or the operation circuit  503 . 
     The unified memory  506  is configured to store input data and output data. 
     A direct memory access controller (DMAC)  505  transfers input data in an external memory to the input memory  501  and/or the unified memory  506 , stores weight data in the external memory in the weight memory  502 , and stores data in the unified memory  506  in the external memory. 
     A bus interface unit (BIU)  510  is configured to implement interaction between the host CPU, the DMAC, and an instruction fetch buffer  509  through a bus. 
     The instruction fetch buffer  509  connected to the controller  504  is configured to store an instruction used by the controller  504 . 
     The controller  504  is configured to invoke the instruction buffered in the instruction fetch buffer  509 , to control a working process of an operation accelerator. 
     The controller  504  invokes the instruction buffered in the instruction fetch buffer  509 , to implement the feature extraction process in this embodiment of this application, for example, extracting the 3D head point cloud information of the user from the color face image and the infrared image and extracting the face texture feature of the user from the color face image. 
     The vector calculation unit  507  includes a plurality of operation processing units, and if necessary, performs further processing such as vector multiplication, vector addition, an exponent operation, a logarithm operation, or value comparison on an output of the operation circuit. The vector calculation unit  507  is mainly configured to perform network calculation at a non-convolutional/FC layer in a neural network, for example, pooling, batch normalization, or local response normalization. 
     In some implementations, the vector calculation unit  507  can store a processed output vector in a unified buffer  506 . For example, the vector calculation unit  507  can apply a non-linear function to an output of the operation circuit  503 , for example, a vector of an accumulated value, so as to generate an activated value. In some implementations, the vector calculation unit  507  generates a normalized value, a combined value, or both a normalized value and a combined value. In some implementations, the processed output vector can be used as an activated input to the operation circuit  503 , for example, can be used at a subsequent layer in the neural network. 
     The instruction fetch buffer  509  connected to the controller  504  is configured to store an instruction used by the controller  504 . 
     Usually, the unified memory  506 , the input memory  501 , the weight memory  502 , and the instruction fetch buffer  509  each are an on-chip memory. The external memory is a memory outside the NPU. The external memory may be a double data rate synchronous dynamic random access memory (DDR SDRAM), a high bandwidth memory HBM), or another readable and writable memory. 
     The host CPU performs steps such as obtaining a key feature point of a facial expression of a user based on a face image of the user in a video call process, driving a 3D head image of the user by using the key feature point of the facial expression of the user, to obtain a target 3D avatar of the user, where the target 3D avatar of the user has an expression of the user, rotating the target 3D avatar based on a preset target presentation angle, to obtain a target 3D avatar at the preset presentation angle, and sending the target 3D avatar at the preset presentation angle to a peer video call device. 
     In some embodiments, the disclosed method may be implemented as computer program instructions encoded in a machine-readable format on a computer-readable storage medium or encoded on another non-transitory medium or product.  FIG. 15  schematically shows a conceptual partial view of an example computer program product arranged according to at least some embodiments shown herein. The example computer program product includes a computer program for executing a computer process on a computing device. In an embodiment, the example computer program product  1500  is provided by using a signal bearer medium  1501 . The signal bearer medium  1501  may include one or more program instructions  1502 . When the one or more program instructions are run by one or more processors, the functions or some of the functions described above with respect to  FIG. 3 ,  FIG. 6 ,  FIG. 7 , and  FIG. 9  may be provided. In addition, the program instructions  1502  in  FIG. 15  are also described as example instructions. 
     In some examples, the signal bearer medium  1501  may include a computer-readable medium  1503 , for example but not limited to, a hard disk drive, a compact disk (CD), a digital video disc (DVD), a digital tape, a memory, a ROM, or a RAM. In some implementations, the signal bearer medium  1501  may include a computer-recordable medium  1504 , for example but not limited to, a memory, a read/write (R/W) CD, or an R/W DVD. In some implementations, the signal bearer medium  1501  may include a communications medium  1505 , for example but not limited to, a digital and/or analog communications medium (for example, an optical fiber, a waveguide, a wired communications link, or a wireless communications link). Therefore, for example, the signal bearer medium  1501  may be conveyed by the communications medium  1505  in a wireless form (for example, a wireless communications medium that complies with the IEEE 802.11 standard or another transmission protocol). The one or more program instructions  1502  may be, for example, one or more computer-executable instructions or logic implementation instructions. In some examples, a computing device described with respect to  FIG. 3 ,  FIG. 6 ,  FIG. 7 , and  FIG. 9  may be configured to provide various operations, functions, or actions in response to the program instructions  1502  transferred to the computing device by using one or more of the computer-readable medium  1503 , the computer-recordable medium  1504 , and/or the communications medium  1505 . It should be understood that the arrangement described herein is merely used as an example. Therefore, a person skilled in the art understands that another arrangement and another element (for example, a machine, an interface, a function, a sequence, and a functional group) can be used to replace the arrangement, and some elements may be omitted together depending on an expected result. In addition, many of the described elements are functional entities that can be implemented as discrete or distributed components, or implemented in any suitable combination at any suitable location in combination with another component. 
     It should be noted that, for ease of description, the foregoing method embodiments are expressed as a series of actions. However, a person skilled in the art should appreciate that the present application is not limited to the described action sequence, because according to the present application, some steps may be performed in other sequences or performed simultaneously. In addition, a person skilled in the art should also know that all the embodiments described in the specification are preferred embodiments, and the related actions and modules are not necessarily mandatory to the present application. 
     In the foregoing embodiments, descriptions of the embodiments have respective focuses. For a part that is not described in detail in an embodiment, refer to related descriptions in other embodiments. 
     In the several embodiments provided in this application, it should be understood that the disclosed apparatuses may be implemented in other manners. For example, the described apparatus embodiments are merely examples. For example, division into the units is merely logical function division and may be other division in an actual implementation. For example, a plurality of units or components may be combined or integrated into another system, or some features may be ignored or not performed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections may be implemented through some interfaces. The indirect couplings or communication connections between the apparatuses or units may be implemented in an electrical form or another form. 
     The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, and may be located in one position, or may be distributed on a plurality of network units. Some or all of the units may be selected based on actual requirements to achieve the objectives of the solutions of the embodiments. 
     In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each of the units may exist alone physically, or two or more units may be integrated into one unit. The integrated unit may be implemented in a form of hardware, or may be implemented in a form of a software functional unit. 
     When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, the integrated unit may be stored in a computer-readable memory. Based on such an understanding, the technical solutions of the present application essentially, or the part contributing to the conventional technology, or all or some of the technical solutions may be implemented in the form of a software product. The computer software product is stored in a memory and includes several instructions for instructing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or some of the steps of the methods described in the embodiments of the present application. The foregoing memory includes any medium that can store program code, for example, a USB flash drive, a ROM, a RAM, a removable hard disk, a magnetic disk, or an optical disc. 
     A person of ordinary skill in the art may understand that all or some of the steps of the methods in the embodiments may be implemented by a program instructing related hardware. The program may be stored in a computer-readable memory. The memory may include a flash memory, a ROM, a RAM, a magnetic disk, an optical disc, or the like. 
     The embodiments of the present application are described in detail above. The principle and implementation of the present application are described in this specification by using specific examples. The description about the embodiments is merely provided to help understand the method and core ideas of the present application. In addition, a person of ordinary skill in the art makes variations to the present application in terms of the specific implementations and application scopes based on the ideas of the present application. Therefore, the content of this specification shall not be construed as a limitation on the present application.