Abstract: Provided is an image rendering method performed by a computer device, including: selecting, from a plurality of pre-compiled shaders associated with a rendering engine, a target shader corresponding to a graphic interface of an application program; acquiring scene data corresponding to a render target according to a rendering instruction triggered by the application program, and writing the scene data into a cache block; determining a plurality of render passes corresponding to the render target; merging, in the cache block, the plurality of render passes into a merged render pass based on the target shader; and performing image rendering on the scene data in the cache block in the merged render pass to obtain an image rendering result in the cache block.

Abstract: This application discloses an artificial intelligence-based image processing method, apparatus, device, and storage medium, and relates to the field of computer technology. The method includes: obtaining a slice image; dividing the slice image to obtain a plurality of image blocks; feeding the plurality of image blocks into a labeling model, extracting, by the labeling model, a pixel feature of the slice image based on the plurality of image blocks, determining a plurality of vertex positions of a polygonal region in the slice image based on the pixel feature, concatenating the plurality of vertex positions, and outputting label information of the slice image, the polygonal region being a region in which a target pathological tissue of interest is located.

Abstract: A computing device receives network message data. The computing device determines a message processing type corresponding to the network message data. In accordance with a determination that the message processing type is a consensus processing type, the computing device obtains a consensus parameter corresponding to the network message data. In accordance with a determination that the consensus parameter does not meet a consensus validity condition, the computing device classifies the network processing message data as an invalid consensus message; and filters out the network message data.

Abstract: A method for displaying a voting result includes: receiving a selection operation signal for an action plan from a virtual object in a target camp; displaying a vote counting area corresponding to the action plan in an interface of the terminal device, the vote counting area being used for displaying voting on the action plan, and the vote counting area comprising sub-areas in a to-be-selected state; receiving a voting operation signal for the action plan, the voting operation signal indicating a support status for the action plan; adjusting one of the sub-areas from the to-be-selected state to a selected state with a display characteristic corresponding to the support status according to the voting operation signal, wherein different support status corresponds to different display characteristics for the sub-areas; and displaying a voting result corresponding to the action plan based on the voting operation signal.

Abstract: A method and apparatus for configuring a virtual scene are provided. The method includes transmit a plurality of pieces of first-type configuration information for a candidate scene element in the virtual scene to a first-type first client; transmit a plurality of pieces of second-type configuration information for the candidate scene element in the virtual scene to a second-type first client, the first-type configuration information and the second-type configuration information being configured respectively for different attributes of the candidate scene element in the virtual scene; obtain a target configuration information for a target scene element in the virtual scene based on instructions by the first-type first client and the second-type first client indicating the different attributes of the candidate scene element; and instruct a second client to display the target scene element in the virtual scene based on the target configuration information.

Abstract: Embodiments of this disclosure include an image processing method and apparatus. The method may include obtaining feature points corresponding to training images and feature center points of images in a second quantity of categories and obtaining a feature condition probability distribution that the feature points collide with corresponding feature center points. The method may further include performing network training to obtain target feature center points of the images in the second quantity of categories. The method may further include mapping the first quantity of feature points and the second quantity of target feature center points to a Hamming space, to obtain hash codes of the first quantity of training images and hash center points of the images in the second quantity of categories. The method may further include performing network training using the hash condition probability distribution and the ideal condition probability distribution, to obtain an image hash model.

Abstract: A video decoding method, a video encoding method, and a related apparatus are provided. In the video decoding method, for a current target video frame in a target video, reference decoding parameters in a reference frame may be obtained from video frames that have been decoded before the target video frame, and a target resolution may be obtained based on a decoding indication parameter obtained based on (e.g., fusing) the reference decoding parameters. The current target video frame may be decoded by using the target resolution.

Abstract: This application discloses a method for recognizing a video clip performed by a computer device. The method includes: determining a plurality of video frame pairs between a first video and at least one second video, each video frame pair comprising a first video frame from the first video and a second video frame from the at least second video with a similarity satisfying similarity conditions; fusing the first video frames in the plurality of video frame pairs on the basis of occurrence time differences of each video frame pair to obtain at least one candidate video clip in the first video; and determining at least one target video clip in the first video on the basis of the at least one candidate video clip, the at least one target video clip being within a target time range of the first video.

Abstract: A video decoding method includes: performing entropy decoding on a coding block of a video image frame to obtain a quantization coefficient block of residual data corresponding to the coding block; inversely quantizing the quantization coefficient block to obtain an inverse quantization coefficient matrix; inversely rearranging the inverse quantization coefficient matrix to obtain a processed coefficient matrix, non-zero coefficients in the processed coefficient matrix being concentrated in left, upper and upper left regions of the coefficient matrix; and generating the residual data according to the processed coefficient matrix.

Abstract: A video encoding method is provided. The method includes decoding intra-frame prediction mode information corresponding to a current decoding unit, decoding first identification information corresponding to the current decoding unit when the intra-frame prediction mode information indicates that a block differential pulse code modulation (BDPCM) mode is applied to the current decoding unit, determining, when the first identification information indicates that a transform block (TB) corresponding to the current decoding unit does not include any non-zero transform coefficients, that a residual block corresponding to the current decoding unit is an all-zero residual block, and reconstructing the current decoding unit according to the residual block and a prediction block corresponding to the current decoding unit. The technical solutions provided in embodiments of this application improve the encoding and decoding efficiency of the decoding unit using the BDPCM mode.

Abstract: A data archiving method includes: finding, in local space when an archiving task for a target block header exists, current state trees of block headers, the current state trees including a current state tree that the target block header points to and a current state tree that a first block header before the target block header points to; reconstructing new state trees according to the found current state trees, node reuse information between the current state trees, and target state data corresponding to a second block header after the target block header; and archiving the current state trees stored in the local space to a target archiving server, and deleting the current state trees in the local space after successful archiving.

Abstract: This application provides a semantic segmentation network structure generation method performed by an electronic device, and a non-transitory computer-readable storage medium.

Abstract: A legged robot motion control method, apparatus, and device, and a storage medium. The method includes: acquiring center of mass state data corresponding to a spatial path starting point and spatial path ending point of a motion path; determining a candidate foothold of each foot in the motion path based on the spatial path starting point and the spatial path ending point; determining a variation relationship between a center of mass position variation coefficient and a foot contact force based on the center of mass state data; screening out, under restrictions of a constraint set, a target center of mass position variation coefficient and target foothold that satisfy the variation relationship; determining a target motion control parameter according to the target center of mass position variation coefficient and the target foothold; and controlling a legged robot based on the target motion control parameter to move according to the motion path.

Abstract: A video decoding and encoding method and apparatus, a computer-readable medium, and an electronic device. The video decoding method includes classifying decoded blocks adjacent to a target block according to information about the decoded blocks to obtain at least one category, obtaining a category identifier corresponding to the target block based on coding information corresponding to the target block, determining a target category corresponding to the target block from the at least one category according to the category identifier corresponding to the target block, determining a predictive vector of the target block based on vectors of decoded blocks included in the target category, and decoding the target block based on the predictive vector.

Abstract: A collision data processing method includes: determining, based on a physical engine detecting that a first rigid body having a first physical attribute collides with a second rigid body having a second physical attribute, an initial cutting model from a first model corresponding to the first rigid body and a second model corresponding to the second rigid body based on the first physical attribute and the second physical attribute, capturing, by the physical engine, a collision point between the first rigid body and the second rigid body, and determining normal vector information corresponding to the collision point based on collision data, and determining a first cutting surface in the initial cutting model based on the collision point, the normal vector information, and model information of the initial cutting model, and cutting the initial cutting model based on the first cutting surface to obtain cutting data.

Abstract: The present disclosure provides a target image generation method. The method includes obtaining a first parsed image and a first pose image based on an original image, the first parsed image being an image labeled with parts of an object in the original image, the first pose image representing a pose of the object in the original image; inputting the first parsed image, the first pose image, and a second pose image representing a target pose into a first image generation model, and determining, a first transformation parameter and adjusting the first parsed image based on the first transformation parameter to obtain a target parsed image, a pose of the object in the target parsed image being the target pose; and inputting a first combined image and a second combined image into a second image generation model, and adjusting the first combined image to obtain a target image.

Abstract: Disclosed is a virtual character interaction method, including: displaying a viewing-only picture of a virtual character interaction scene; switching from the viewing-only picture to a virtual character simulation interaction interface in response to an interaction simulation operation triggered during displaying of the viewing-only picture; simulating, in the virtual character simulation interaction interface, virtual characters in the virtual character interaction scene, and displaying a scene status of interaction between the virtual characters, wherein the virtual characters include a simulated virtual character controlled by a user of the computer device; and controlling the simulated virtual character to perform a corresponding action in response to a control operation triggered on the virtual character simulation interaction interface by the user of the computer device.

Abstract: A data processing method, an apparatus, a device, and a medium in a blockchain fund settlement system are provided. A data processing method in a blockchain fund settlement system, performed by at least one processor of a settlement institution terminal, includes: querying, on a blockchain, a settlement request that is to be processed by the settlement institution terminal between a fund settlement initiator and a fund settlement recipient, the settlement request being generated by an initiator terminal of the fund settlement initiator and recorded on the blockchain; receiving the settlement request based on the querying; and generating a settlement result of a settlement performed according to the settlement request, and recording the settlement result on the blockchain.

Abstract: An information display method includes: displaying, on the electronic device of a user account, a first display region and a second display region of a target battle; receiving information associated with a selection of a target virtual object; and in accordance with a determination that the target virtual object matches at least one second-type virtual object, displaying first prompt information associated with the target virtual object in the first display region, and displaying second prompt information associated with the target virtual object in the second display region.

Abstract: This application provides a method for decoding an unmatched pixel performed by an electronic device. The method includes: obtaining a bit stream of a target video sequence and decoding the bit stream of the target video sequence by at least two entropy decoding methods, to obtain a binary symbol string of an unmatched pixel in a target picture block, the target picture block being obtained by dividing a target picture frame in the target video sequence; inversely binarizing the binary symbol string, to obtain a component value of the unmatched pixel; and obtaining the target picture block based on the component value of the unmatched pixel. This method can improve the flexibility of coding and facilitate equalization between the coding performance and the coding overhead of the unmatched pixel.