Abstract: A virtual object control method includes: displaying a first virtual object located in a virtual environment (301); displaying one or more candidate object identifiers corresponding to one or more candidate virtual objects in the virtual environment in response to a moving operation on the first virtual object (302); and controlling, in response to a trigger operation on a target object identifier in the one or more candidate object identifiers, the first virtual object to automatically follow a target virtual object in the virtual environment (303).

Abstract: A speech decoding method is performed by a computer device, the speech including a current audio frame and a previous audio frame. The method includes: obtaining a target token corresponding to a smallest decoding score from a first token list including first tokens obtained by decoding the previous audio frame, each first token including a state pair and a decoding score, the state pair being used for characterizing a correspondence between a first state of the first token in a first decoding network corresponding to a low-order language model and a second state of the first token in a second decoding network corresponding to a differential language model; determining pruning parameters according to the target token and an acoustic vector of the current audio frame when the current audio frame is decoded; and decoding the current audio frame according to the first token list, the pruning parameters, and the acoustic vector.

Abstract: Embodiments of this application relate to a virtual reality display device. The virtual reality display device includes a left lens barrel and a right lens barrel; the left lens barrel includes: a left lens barrel housing, a left display screen, and a left lens; the right lens barrel includes: a right lens barrel housing, a right display screen, and a right lens; an inner side of an opening portion of the left lens barrel housing includes a left lens holder; an inner side of an opening portion of the right lens barrel housing includes a right lens holder; a left fill layer is provided between the left lens holder and the left lens; and a right fill layer is provided between the right lens holder and the right lens.

Abstract: Aspects of the disclosure provide methods and apparatuses for storing data. In some examples, an information processing apparatus that includes processing circuitry is provided. The processing circuitry is configured to receive data and determine a target blockchain based on an attribute associated with the data and blockchain correspondence information. The blockchain correspondence information indicates attributes associated with a plurality of blockchains. Further, the processing circuitry is configured to store a first block that is generated based on the received data in the target blockchain.

Abstract: The present disclosure provides a terminal vibration evaluation method performed by an electronic device. The method includes: acquiring an actual vibration curve of a target terminal when a target game scenario is displayed; acquiring a predefined vibration description file associated with the target game scenario, and determining a predefined vibration curve according to the predefined vibration description file; determining target deviation data between the actual vibration curve and the predefined vibration curve; and determining, according to the target deviation data, whether vibration of the target terminal matches the target game scenario. The present disclosure provides a measurement solution used for determining whether terminal vibration matches a game scenario (for example, a game sound and a game picture), which helps improve a matching degree between terminal vibration and the game scenario, thereby improving a sense of substitution of the game and a sense of immersion of a player.

Abstract: The present disclosure provides methods, apparatus, and storage medium for generating a training sample of a semantic segmentation model. The method includes obtaining an object identifier of a to-be-trained object and a model file corresponding to the object identifier, the model file comprising a three-dimensional model of the to-be-trained object and a skin map set, the skin map set comprising a plurality of solid-color skin maps and a plurality of colorful skin maps; determining transformation models corresponding to the three-dimensional model; generating a plurality of sample image groups corresponding to each transformation model according to the plurality of colorful skin maps and each transformation model; generating a labeled image of the each sample image group according to the object identifier, the plurality of solid-color skin maps, and each transformation model; and generating a training sample of the to-be-trained object according to the labeled image and the each sample image group.

Abstract: Embodiments of this disclosure include an encoding method and apparatus. The encoding may include obtaining a target paragraph and a context sentence of the target paragraph, and inputting the target paragraph and the context sentence into a memory encoding model. The encoding may further include obtaining an original vector set and a memory vector set in the input layer and obtaining a first target sentence matrix of the original vector set in the memory layer according to the original vector set and the memory vector set. The encoding may further include obtaining a paragraph vector of the target paragraph in the output layer according to the first target sentence matrix and performing processing based on the paragraph vector.

Abstract: This application relates to a data processing method performed by a computer device. The method includes: for each block of a plurality of blocks in a first block file of a blockchain platform, detecting a to-be-maintained transaction record in the block; selecting, from a hash tree of the block, nodes used for proving that the transaction record belongs to the block; constructing a node maintenance tree according to the selected nodes; storing the transaction record at a corresponding leaf node of the node maintenance tree; migrating an association between a block head of the block and the node maintenance tree storing the transaction record from the first block file to a second block file, the block head of the block being adjacent to a block head of a previous block in the second block file; and deleting the first block file after the update processing for each block is completed.

Abstract: Embodiments of this disclosure include a training method for an image processing model. In the method, parameters of an encoder in the image processing model are updated according to a to-be-replaced face in an original image, to configure the encoder to encode the to-be-replaced face to obtain a visual feature of the to-be-replaced face. Parameters of a decoder in the image processing model are updated according to the to-be-replaced face in the original image, to configure the decoder to perform decoding based on the visual feature of the to-be-replaced face. The parameters of the decoder are further updated according to a target face in a target image without changing the parameters of the encoder, to configure the decoder to perform decoding based on the visual feature of the to-be-replaced face and obtain a target face having the same visual feature as the to-be-replaced face.

Abstract: A method for managing network live streaming data and a related apparatus, device and storage medium, the method includes: a stream pushing request transmitted by a data access device is received; a stream pushing parameter set based on a media content feature of stream pushing data from multiple data sources in the data access device is determined; a target port is then determined according to the stream pushing parameter; the stream pushing data transmitted by the data access device is further received through the target port; and the stream pushing data is pushed to a live streaming device, so that the live streaming device plays media content data in a network live streaming process. In this way, a process of combining media content of multiple terminals and performing network live streaming is implemented.

Abstract: A spectator interaction method includes displaying an interactive control for a first spectator account watching an online battle, the interactive control configured to trigger an interaction between the first spectator account and one or more spectator accounts currently watching the online battle. The method further includes displaying first interactive content in response to receiving a trigger operation on the interactive control. The method further includes displaying a quantity of spectator accounts corresponding to the first interactive content, the quantity of spectator accounts including accounts that have interacted with the first interactive content, and accounts that have interacted with second interactive content triggered by a second spectator account. The second interactive content being a same interactive content type as the first interactive content.

Abstract: In a data processing method for a point cloud media, a media file of the point cloud media is obtained. The media file includes stream data of a plurality of point cloud frames and decoding indication information of each of the plurality of point cloud frames. The decoding indication information of each of the plurality of point cloud frames includes at least two attribute headers. Each of the at least two attribute headers includes attribute identifier information. The point cloud media is processed based on the stream data and the decoding indication information.

Abstract: A conference data processing method includes acquiring a first meeting list of meetings created by a user, meetings in the first meeting list having been created by the user through an instant messaging (IM) client. The IM client includes an interface for calling a software development kit (SDK) of a separate conference application implementing at least one of audio or video conferencing. The method further includes requesting, through a fusion server, a server of the conference application to create a second meeting list according to the first meeting list, and receiving, through the fusion server, the second meeting list transmitted by the server of the conference application. The second meeting list includes meetings that correspond respectively to the meetings in the first meeting list. The method further includes establishing an association relationship between a meeting in the first meeting list and a meeting in the second meeting list.

Abstract: The present disclosure provides an endoscopic image processing method and system, and a computer device. The method can include acquiring a current endoscopic image of a to-be-examined user, and predicting the current endoscopic image by using a deep convolutional network based on a training parameter. The training parameter can be determined according to at least one first endoscopic image and at least one second endoscopic image transformed from the at least one first endoscopic image, where the at least one endoscopic image corresponds to a human body part. The method can further include determining an organ category corresponding to the current endoscopic image. The method provided in the present disclosure can make a prediction process more intelligent and more robust, thereby improving resource utilization of a processing apparatus.

Abstract: A point cloud including a plurality of points is acquired. Each of the plurality of points includes N pieces of attribute information. N is a positive integer greater than 1. A to-be-coded value is determined for each of the N pieces of attribute information of a current point of the plurality of points based on the N pieces of attribute information of a point prior to the current point being encoded. The to-be-coded values of the N pieces of attribute information of the current point are encoded based on at least one of a selected encoder or a selected coding mode to obtain a code stream of the point cloud. The to-be-coded value for each of the N pieces of attribute information of the current point is encoded based on a respective one of the one or more encoders and a respective one of the one or more coding modes.

Abstract: A virtual interaction method including displaying a virtual scenario comprising a first virtual figure and a second virtual figure that participate in a battle game task, wherein the first virtual figure is controlled by a first device. The method further includes, in response to a trigger operation on the first device, displaying the first virtual figure and a target virtual figure performing an action directed to the second virtual figure together, wherein the target virtual figure is not controlled by the first device.

Abstract: A method for obtaining sensing data includes transmitting, by a network function element to a terminal device, sensing data request information comprising parameters for sensing and reporting data by the terminal device. The sensing data request information indicates, to the terminal device, to report sensing data to the network function element according to the parameters. The method further includes obtaining sensing data returned by the terminal device, the sensing data being transmitted by the terminal device according to the parameters indicated in the sensing data request information. The method further includes preprocessing, by the network function element, the sensing data and providing a result of the preprocessing to a server.

Abstract: A point cloud decoding method is provided. In the method, a target point cloud group of a plurality of point cloud groups is obtained. The target point cloud group includes at least one point. An attribute coding mode of the target point cloud group is determined. Prediction attribute information of each point in the target point cloud group is obtained based on attribute prediction of the respective point in the target point cloud group according to the attribute coding mode of the target point cloud group. Reconstruction residual information of each point in the target point cloud group is obtained based on attribute decoding of the respective point in the target point cloud group. Reconstruction attribute information of each point in the target point cloud group is determined according to the prediction attribute information and the reconstruction residual information of the respective point in the target point cloud group.

Abstract: A picture filtering method includes determining encoding information of a picture, the encoding information comprising classification information indicating one of an intra encoding mode or an inter encoding mode of the picture. The method further includes inputting the picture and the classification information indicating one of the intra encoding mode or the inter encoding mode into a general filtering model trained using deep learning, and obtaining a filtered picture based on the filtering model performing filtering on the picture based on the encoding information.

Abstract: A video encoding/decoding approach including generating a prediction vector candidate list according to displacement vectors of an encoded/decoded block whose reference frame is a current frame; selecting a prediction vector of a to-be-encoded/decoded current block from the prediction vector candidate list; and encoding/decoding the current block based on the prediction vector of the current block.