57 results about "Target expression" patented technology

The invention discloses an advertisement material intelligent optimization system and method based on knowledge extraction and knowledge fusion, and the method comprises the following steps: S1, obtaining and preprocessing a to-be-optimized advertisement material, and collecting an external knowledge source; s2, combined semantic analysis is executed, and cross-modal semantic alignment is carried out; s3, extracting entity units and corresponding semantic attributes, performing type labeling on the entity units, and establishing a semantic relation structure; s4, performing semantic fusion with an external knowledge source, calculating similarity between semantic vectors, performing entity mapping, performing normalization, and correcting conflict boundaries and attribute value divergence; s5, extracting a target expression fragment, and performing optimization according to a preset optimization rule set; and S6, verifying and detecting the optimized advertisement material, and outputting an optimization result. According to the method, deep understanding and cross-modal fusion optimization of advertisement materials can be realized, and the accuracy, attraction and propagation effect of advertisement semantic expression are improved.

The invention discloses a bionic robot facial expression synchronization method and device based on visual perception, equipment and a medium, and relates to the technical field of man-machine interaction, and the method comprises the steps: controlling a target bionic robot to execute a target expression control instruction used for achieving a preset expected robot facial expression, and capturing a robot visual facial expression of the robot; based on the robot visual facial expression and a preset expected robot facial expression, carrying out calibration on the target bionic robot so as to construct a hardware compensation parameter vector; executing a facial expression synchronization operation based on the hardware compensation parameter vector and a pre-trained target expression mapping model by asynchronously running a preset sensing thread and a preset driving thread; the preset sensing thread is used for collecting a target face image in real time and outputting a target steering engine control vector through a target expression mapping model; and the preset driving thread is used for outputting a steering engine control instruction at a preset output frequency based on a preset frame compensation algorithm, the hardware compensation parameter vector and the target steering engine control vector.

The invention discloses an expression recognition method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring an image pair comprising a target user face; the image pair comprises a natural expression image of the target user and a to-be-recognized image; performing fusion processing on the image pair, and inputting the image pair after fusion processing into a trained expression recognition model to obtain an expression recognition result of the to-be-recognized image generated by the expression recognition model; wherein the expression recognition model comprises an expression change extraction module and a decoder; the expression change extraction module is used for extracting target expression change features of the fused image pair; and the decoder is used for outputting an expression recognition result based on the target expression change feature. According to the embodiment of the invention, inherent differences of different individuals in emotional expression are fully considered, and the problem of identification deviation caused by neglecting individual expression characteristics in a traditional expression identification method is reduced, so that the accuracy of expression identification is improved.

The application provides a high-efficiency expression migration method using base expression space transformation, comprising a preprocessing stage and a facial expression migration stage; the preprocessing stage comprises: obtaining a source model O, the source model O being a base expression model comprising expression meshes under multiple different expressions; performing facial expression reconstruction on the source model O and a target model T to obtain expression description parameters of the two; performing similarity estimation according to the expression description parameters to obtain an expression parameter conversion matrix and a similarity matrix; the facial expression migration stage comprises: inputting a character picture into the base expression model, calculating expression description parameters of a target expression model according to the expression description parameters in the base expression model and the similarity matrix, and completing expression migration.

The present disclosure provides a landmark feature determination method and device, electronic equipment and storage medium. The landmark feature determination method comprises: obtaining target expression data, wherein the target expression data is used to represent the expression amount of each candidate feature in different cells; the average expression amount and expression proportion of each candidate feature in different cell types are counted; for each candidate feature, the first inter-class comparison data is determined according to the average expression amount of the candidate feature in the target cell type and the average expression amount of the candidate feature in the non-target cell type; for each candidate feature, the second inter-class comparison data is determined according to the expression proportion of the candidate feature in the target cell type and the expression proportion of the candidate feature in the non-target cell type; and the landmark feature of the target cell type is determined according to the first inter-class comparison data and the second inter-class comparison data. The embodiment of the present application can solve the problem of landmark feature specificity.

The invention provides a manufacturing decision system-oriented modular functional architecture and a collaborative mechanism thereof, and the structural organization and reusable collaboration of a manufacturing decision process are realized by constructing a multi-layer capability system, a functional module system and a standardized behavior interface. The architecture comprises a data interface module, a decision-making target module, a disturbance response module and a fusion decision-making module, and defines a data flow, a control flow and a trigger relationship among the modules. The system completes target expression, constraint analysis, disturbance identification and collaborative decision generation through standardized access of multi-source data such as orders, productivity, materials and processes, and forms an interpretable and executable decision link. According to the invention, modularization, interpretability and intelligent cooperation of the manufacturing decision-making system are realized, and multi-scene and multi-industry manufacturing decision-making application is supported.

The application discloses a three-dimensional face generation method and device, a three-dimensional face reenactment method and device, wherein the generation method comprises the following steps: acquiring an expression vector of a first image containing a first identity object based on a target expression coding network model; the first image is an image with a face having an occlusion; inputting the expression vector and a second image containing a second identity object into a target face generation network model to determine a target image of migrating the expression vector of the first image to a face of the second identity object in the second image; and enabling the face region of the first identity object in the first image to be identified as the expression vector even in the case of having the occlusion and to be migrated to the face of the second identity object in the second image, so that the accuracy of face expression migration is improved, and the robustness of face expression migration in the case of having the occlusion is improved.

The invention discloses an information recommendation method, electronic equipment and a computer readable storage medium. The method comprises the following steps: in response to a received first data request, based on environmental parameters and position transformation information of a target object, controlling expression acquisition equipment to acquire the target object to view a target expression image of current recommendation information; performing feature extraction on the target expression image to obtain expression features of multiple dimensions; analyzing the expression of the target object for viewing the current recommendation information based on the expression features of the multiple dimensions to obtain an expression analysis result; in response to a received second data request sent by the target client, obtaining object behavior data of the target object for the current recommendation information; and adjusting the current recommendation information based on the expression analysis result and the object behavior data to obtain target recommendation information. According to the invention, the technical problem of poor information recommendation effect in related technologies is solved.

The application discloses a face expression recognition method and device, equipment and storage medium, relates to the artificial intelligence technical field, and includes: in the process of utilizing a target expression recognition model to perform face expression recognition on video frames of a target face video, for any video frame at any time, extracting each face key point, and generating a spatial heat map based on each face key point, and generating a spatial feature vector based on the spatial heat map and the image features of the video frame; determining the spatial feature vector difference between the spatial feature vector of the video frame at the current time and the spatial feature vector of the video frame at the previous time, and determining the first hidden state corresponding to the spatial feature vector of the video frame at the current time and the second hidden state corresponding to the spatial feature vector difference; determining the fusion features based on the first hidden state and the second hidden state, and determining the face expression recognition result of the video frame at the current time based on the fusion features. The application can improve the stability and accuracy of dynamic face expression recognition.

The invention discloses a robot facial expression generation control method based on a multi-particle swarm optimization algorithm, and relates to the technical field of robot control. According to the method, motor parameters corresponding to facial expressions of a robot are abstracted into particles, populations and parameters are initialized, expressions are generated through physical mapping, and an RGB camera captures and processes images; the method comprises the following steps: firstly, obtaining a Pareto solution set, then respectively calculating expression accuracy, key motion unit activation degree and motion unit coordination degree by using three objective functions to obtain particle fitness, then updating the Pareto solution set and particle information, finally judging whether an end condition is met, and carrying out loop iteration until a natural and vivid target expression is generated. Manual calibration is not needed, the automation degree is high, the universality is good, computing resources can be saved, and the generated expressions conform to the human face movement rule and are vivid and real.

This application provides a model training method, apparatus, electronic device, and storage medium based on balanced samples. The method calculates accumulated coefficients based on the same type of expression coefficients from multiple training samples, and constructs an accumulated vector from these coefficients. A target accumulated coefficient is determined based on the distribution of the accumulated coefficients in the accumulated vector. A portion of the target accumulated coefficient representing the target expression coefficient is assigned a first weight, and the remaining expression coefficients are assigned a second weight, resulting in a weight matrix composed of the first and second weights. The model is adjusted and trained further under the guidance of a loss function based on the weight matrix until preset requirements are met. In this scheme, the distribution of expression coefficients in the training samples is used to assign different weights to the expression coefficients, and training is performed under the guidance of a weighted loss function. This ensures that the training is based on information from uniform expression coefficients in the samples, optimizing the model training effect based on samples that conform to real-world scenarios.

The invention discloses a graphical interface program design method capable of dynamically simulating, which comprises the following steps: designing a set of data flow grammar normal forms, the data flow is an expression for expressing an attribute logic relationship between controls, and the expression comprises a source expression and a target expression; based on the grammar normal form, data streams are designed according to requirements in the interface design process, the updating sequence of the data streams is calculated according to the interdependence relation, and a design result is stored in a design file; and carrying out dynamic simulation based on a design result, and sequentially and circularly executing the data streams or updating controls according to an updating sequence of the data streams, so as to realize simulation operation. According to the method, the problem that the design effect cannot be visually and quickly checked when a complex interface is designed is solved.

The embodiment of the invention provides an image editing method and electronic equipment. In the scheme, the electronic equipment can edit the expression of the face object in the target image through the expression of the face object in the real-time preview image collected by the camera, or edit the expression of the face object in the target image according to the expression in the target expression template selected by the user. According to the scheme, the expression of the face object in the image can be edited through simple operation, so that the expression of the face object in the edited image is more natural, user requirements are met, the editing time is shortened, the operation threshold is reduced, the expression editing efficiency of the image is improved, and finally the user experience is improved.

The invention provides a text processing method and device, an interaction method and device, equipment, a medium and a program product. The method can be applied to the technical fields of artificial intelligence and data processing. The method comprises the following steps: determining a plurality of to-be-processed texts from a plurality of pieces of dialogue information related to the same service; based on a field identifier included in the target expression, performing field extraction on the to-be-processed text to obtain a target field matched with field identifier semantics; filling the target field into the target expression according to the field identifier to obtain a structured text of the to-be-processed text; based on respective feature vectors of the plurality of structured texts, the plurality of to-be-processed texts are clustered to obtain a clustered text set, and the feature vectors are obtained by encoding the structured texts; and rewriting the clustered text set to obtain a target text.

The application discloses a face image generation method, device and equipment and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: acquiring a face image of a first face, acquiring a face image of a second face, and acquiring face vertex data of multiple faces with different face shapes and different expressions; taking the first face as a fitting target, fitting the face vertex data of the multiple faces to determine target face shape features and target expression features of the first face; taking the second face as a fitting target, fitting the face vertex data of the multiple faces to determine target face shape features and target expression features of the second face; and generating a target face image of the second face based on the target expression features of the first face, the target face shape features of the second face, and target face texture features of the second face. The method reduces the time cost and labor cost.

This application relates to the field of data processing and provides a data processing method. The method ensures the comparability of multiple AI-generated content variants by screening candidate content containing the same technical feature words and performing correlation analysis. Through semantic alignment and extraction of target expressions, it unifies the expression forms of the same technical meaning in different AI-generated content variants. By classifying and quantifying the target expressions, it improves the accuracy of tendency type identification. By extracting statistical features and comparing them with knowledge constraints to generate candidate restoration values, the restoration results conform to statistical laws and do not violate domain common sense. Finally, by comprehensively considering the characteristics of propagation nodes and cross-difference consistency, it performs credibility assessment and sorts the restoration results for output. The processing provided by this method can reverse-engineer the original true information from multiple AI-generated content variants distributed across different propagation nodes through statistical laws and domain knowledge constraints.

The invention relates to an interaction processing method and apparatus, an electronic device, a medium and a program product. The method comprises the steps of displaying an account association page of a live broadcast account corresponding to a target live broadcast room; under the condition that the interaction information input operation is detected, displaying a live broadcast associated expression material corresponding to the currently input interaction information on an account associated page; the live broadcast associated expression material is generated based on a target interaction keyword corresponding to a target live broadcast room and a target expression material, and the target interaction keyword is a preset interaction keyword matched with the current input interaction information in at least one preset interaction keyword matched with a live broadcast interaction theme of the target live broadcast room; the target expression material is a preset expression material matched with the current input interaction information in the at least one preset expression material. According to the embodiment of the invention, the adaptability of expression material interaction and live broadcast interaction atmosphere can be improved, meanwhile, the enthusiasm of a user entering a live broadcast room in a related interaction page of an anchor can be improved, and the live broadcast interactivity is improved.

The embodiment of the invention provides a target business node determination method, electronic equipment, a storage medium and a product, and the method comprises the steps: obtaining a logic expression corresponding to a business process, the logic expression comprises a plurality of expression items, and each expression item corresponds to at least one business node; under the condition that the overall processing result of the business process does not meet the first preset value, according to the local processing result corresponding to each expression item, determining a target expression item of which the local processing result does not meet a second preset value; and determining a target service node according to the target expression item. The target service node determination method provided by the embodiment of the invention aims to accurately and efficiently identify the target service node causing the failure of the service process.

The application discloses a visual perception-based bionic robot facial expression synchronization method and device, equipment and medium, and relates to the technical field of human-computer interaction. The method comprises the following steps: controlling a target bionic robot to execute a target expression control instruction for realizing a preset expected robot facial expression, and capturing a robot visual facial expression of the robot; calibrating and setting the target bionic robot based on the robot visual facial expression and the preset expected robot facial expression, so as to construct a hardware compensation parameter vector; executing a facial expression synchronization operation based on the hardware compensation parameter vector and a pre-trained target expression mapping model by asynchronously running a preset perception thread and a preset driving thread; the preset perception thread is used for collecting a target human face image in real time, and outputting a target steering wheel control vector through the target expression mapping model; and the preset driving thread is used for outputting a steering wheel control instruction at a preset output frequency based on a preset frame interpolation algorithm, the hardware compensation parameter vector and the target steering wheel control vector.

The application relates to a vehicle state conflict comparison method and device, a vehicle and a storage medium, wherein the method comprises the following steps: obtaining a vehicle state arranged by a user; performing abstract processing on the vehicle state to obtain an arrangement expression, and obtaining a configuration expression matched with the vehicle state arranged by the user from a preset cloud platform; obtaining a target expression according to the arrangement expression and the configuration expression based on a preset relationship symbol rule table, obtaining a conflict result of the vehicle state according to the target expression, and determining that the vehicle state is not in conflict when the conflict result is true. Therefore, the problem that a vehicle state value cannot be judged for conflict in the related art is solved, and whether the state that the user wants to arrange conflicts with the configured rule can be judged when the user arranges the scene.

The application provides a method and system for zero-shot video-to-video expression transfer, which comprises the following steps: obtaining a source video and an expression video, wherein a face in the source video is a to-be-transferred object of a face in the expression video; preprocessing the source video and the expression video to obtain a target source video and a target expression video; performing video expression transfer according to a target face global feature, a target motion feature and a target expression transfer model to obtain a transferred video; the target face global feature is obtained by calculating the target source video through the target expression transfer model; the target motion feature is obtained by calculating the target expression video through the target expression transfer model; the video expression transfer comprises face feature extraction, face feature global analysis, face motion analysis, face volume deformation and rendering processing. The above-mentioned scheme solves the problem of poor expression transfer effect of the existing video transfer technology.

The invention discloses an expression package automatic generation and management method, which comprises the following steps that: after a user inputs a semantic text, a system firstly analyzes and extracts key semantic elements, and then retrieves visual common knowledge elements strongly associated with the key semantic elements in an expression package structured knowledge base; and the two are assembled by fusion logic to generate a quintuple structured description (including individual relationship, image action, expression, blank space and dialogue content) of a complete definition picture. And after the description is coded into a condition vector, a condition generation model is driven to synthesize a target expression package image. If the user adopts, the result and the quintuple description thereof are fed back to the knowledge base to continuously optimize the system. According to the method, a'quintuple 'intermediate layer is introduced, and the fuzzy intention is converted into an accurate structured script, so that the generation result is highly controllable in style, action, composition and blank space and meets the expectation, and the one-time generation success rate is greatly improved.

The application provides a facial expression model generation method and device, an electronic device and a storage medium. The method comprises the following steps: determining an expressionless character model of a target character; determining a target expressionless reference model of a target reference character and a plurality of target expression reference models of different expressions of the target reference character from a model library according to facial features of the expressionless character model; and generating a plurality of expression character models of different expressions corresponding to the expressionless character model according to morphing data between each target expression reference model and the target expressionless reference model. The method can automatically generate a plurality of expression character models of different expressions of the target character, improve the generation efficiency of the expression character model, and determine the corresponding target expressionless reference model through the facial features of the expressionless character model, thereby improving the accuracy of the expression character model in expressing the expression and improving the generation effect of the expression character model.

Embodiments of the present application disclose a data processing method and device, computer equipment and a computer readable storage medium, wherein the method comprises: performing first processing on first facial feature data corresponding to a target frame in a video, the first processing comprising: determining a target expression coefficient corresponding to the target frame according to the first facial feature data, a regression model and adjustment information; updating the adjustment information according to the target expression coefficient; wherein the first facial feature data is input data of the regression model; the target expression coefficient is output data obtained based on the regression model; the adjustment information affects the output data of the regression model; and sequentially taking multiple frame images in the video as target frames to perform the first processing.

The invention discloses a risk control index calculation method, system and device and a storage medium. The method comprises the steps of obtaining a target risk control index corresponding to a target risk control scene and a target expression and a target rule corresponding to the target risk control index; constructing a target feature value dependency tree of the target risk control index according to the target expression; acquiring risk control event data corresponding to the target risk control scene; determining a target index value of the target risk control index according to the risk control event data, the target expression and the target feature value dependency tree; and determining a target risk control identification result according to the target index value and the target rule, and displaying the target risk control identification result through a preset visual system. Therefore, the risk control index is calculated by constructing the feature value dependence tree, and accurate calculation of the risk control index is realized.

The embodiment of the invention provides a virtual object model processing method and device and a normal map generation method and device.The virtual object model processing method comprises the steps that an expression generation instruction for an object model of a virtual object is received, the object model comprises a plurality of vertexes, and the vertexes correspond to the vertexes; the expression generation instruction carries a target expression type and expression intensity corresponding to the target expression type; based on the target expression type, a normal map is obtained from a preset normal map library, and any pixel point in the normal map has a corresponding normal offset; adjusting the normal offset of the pixel point based on the expression intensity to obtain the adjusted normal offset of the pixel point; according to the method, on the basis of the normal offset of the pixel points, the normal of the vertex corresponding to the pixel points is subjected to offset processing, normal errors caused by bone transformation are avoided, and the concave-convex expressive force and the visual reality sense of the virtual object under subtle expressions are remarkably enhanced. The method can be widely applied to the field of digital culture product manufacturing software.

The invention discloses a personalized representation-based high-fidelity expression portrait video generation method. The method comprises the following steps of: performing initial three-dimensional reconstruction on an input image of a reference object and a driving video; constructing personalized head representation of the reference object; optimizing the personalized head representation according to a decoupling principle of the personalized head representation; constructing an identity self-adaptive expression migration module to realize cross-identity dynamic expression migration; migrating the expression of the driving video to the personalized head representation of the reference object through an identity self-adaptive expression migration module, and generating a three-dimensional grid sequence with a target expression and keeping the identity of the reference object; and rendering a geometric condition graph based on the generated three-dimensional grid sequence, inputting the geometric condition graph as a control signal into the video diffusion model, and generating the high-fidelity expression portrait video, the method does not need to depend on a large amount of specific training data, and can generate a movie-level portrait video only through a single optimized network parameter. And the reality and controllability of the generated result are obviously improved.

A method for generating face images is provided, including: acquiring a face image of a first face, a face image of a second face, and face vertex data of a plurality of faces with different face shapes and different expressions; fitting the face vertex data of the plurality of faces by using a first face as a fitting target to determine a target face shape feature and a target expression feature of the first face; fitting the face vertex data of the plurality of faces by using a second face as a fitting target to determine a target face shape feature and a target expression feature of the second face; and generating a target face image of the second face based on the target expression feature of the first face, the target face shape feature of the second face, and a target face texture feature of the second face.

The invention discloses an enterprise information pushing method and device, equipment and a storage medium, and relates to the technical field of information processing, and the method comprises the steps: obtaining initial enterprise information, and carrying out the slicing of the initial enterprise information, and obtaining a plurality of initial enterprise information segments; performing duplicate removal and clustering processing on each initial enterprise information segment to obtain a plurality of target enterprise information segments, and generating each initial structured data based on semantic features of each target enterprise information segment; determining a target expression template based on the role information of the user and a target enterprise task to be processed by the user, and generating initial push information based on the target expression template and each piece of initial structured data; and determining a target push mode based on the user portrait of the user, adjusting the initial push information to obtain target push information, and pushing the target push information to the user side based on the target push mode, so that the user side processes the target enterprise task based on the target push information. According to the invention, user demands can be accurately matched and enterprise information pushing can be carried out.

This application provides an intelligent response method, apparatus, device, and medium based on user expression preferences, belonging to the fields of artificial intelligence and financial technology. The method includes: acquiring input data of a target object; extracting the target object's expression preferences from the input data or a preset database to obtain target expression preference data; extracting question information from the input data to obtain preliminary question data; encapsulating the target expression preference data and preliminary question data into a question based on a preset question template to obtain target question data; generating an answer to the target question data using a pre-trained question-and-answer model to obtain a target answer; and replying to the target object based on the target answer. This application can determine the target object's preferred response expression method by combining the target object's predicted expression preference data, and generate reply content based on this response expression method, improving the personalization of intelligent customer service in consultation responses.