An animation character design method and system based on text content

Abstract

The application relates to an animation character design method and system based on text content, which comprises the following steps: inputting text information into a semantic analysis model to extract semantic features; dividing the semantic features into core features and modification features based on a character element classification table; constructing a graphic component database, and mapping the core features into invariable components and the modification features into variable components; assigning initial weights to the variable components and adjusting the initial weights into modified weights, and determining the dynamic parameter range of the variable components; generating a character design image scheme and feeding back to a user end; by constructing a feature processing and weight regulation system based on text semantic driving, the application realizes process optimization from semantic understanding to visual generation of the animation character design, effectively solves the problems of insufficient importance differentiation of character features and great blindness of component weight parameter adjustment in the prior art, and has the effects of improving the recognition of the animation design character and reducing the operation threshold of the user.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to a method and system for designing anime characters based on text content. Background Technology

[0002] Character design, as a core area of ​​digital content creation, plays a crucial role in game development, film and television production, and the virtual idol industry. With the rapid development of computer graphics and artificial intelligence technologies, this field has gradually evolved from traditional manual modeling to intelligent generation.

[0003] Currently, except for some special scenarios that still require manual modeling by professional designers, the mainstream technical solutions mainly adopt two types of automated design methods: modular design systems based on pre-set component libraries and parametric adjustment and generation systems.

[0004] However, existing intelligent design systems for anime characters still have significant limitations. While parametric adjustment systems offer flexibility in feature modification through slider controls, their lack of deep understanding of design semantics makes it difficult to effectively identify and differentiate the importance levels of different design elements. This deficiency leads to the system easily equating core features that determine a character's identity (such as professionally distinctive equipment) with secondary decorative details (such as accessories), resulting in two main problems: first, insufficient expression of key features, affecting character recognizability; and second, the largely arbitrary adjustment of weight parameters for secondary features, easily leading to overemphasis on secondary features and disrupting overall harmony. These limitations mean that ordinary users still need professional knowledge or must perform multiple manual screenings and adjustments to achieve the desired design effect, severely hindering the improvement of design efficiency and quality. Summary of the Invention

[0005] To address the aforementioned shortcomings, this application provides a method and system for designing anime characters based on text content.

[0006] The above-mentioned objective of this application is achieved through the following technical solution:

[0007] A text-based method for designing anime characters, comprising the following steps:

[0008] When text information based on anime character design is received from the user, the text information is input into a pre-trained semantic parsing model for semantic feature extraction.

[0009] Based on the pre-stored role element classification table, semantic features are divided into core features and modifier features;

[0010] Build a database of graphical components, and map core features to immutable components and decorative features to variable components;

[0011] Assign initial weights to variable components, adjust the initial weights of variable components to correct weights using preset permission adjustment rules, and determine the dynamic parameter range of variable components based on the corrected weights;

[0012] Character design schemes are generated based on immutable and variable components and then fed back to the user.

[0013] By adopting the above technical solution, a pre-trained semantic parsing model is used to deeply understand user text. Semantic features are intelligently divided into core features and decorative features using a character element classification table, establishing an importance hierarchy for design elements. Furthermore, by mapping core features to immutable components and decorative features to variable components, the problem of existing parametric systems' inability to distinguish feature importance is fundamentally solved. Simultaneously, a two-layer control mechanism of dynamic weight correction and parameter range mapping is constructed to achieve intelligent adjustment of variable components: initial weights are assigned based on semantic parsing results, multi-dimensional corrections are performed using preset permission adjustment rules, and the dynamic parameter range of variable components is determined based on the corrected weights. This application, by constructing a hierarchical feature processing and intelligent weight control system driven by text content semantics, achieves full-process optimization of animation character design from semantic understanding to visual generation. It effectively solves the problems of insufficient distinction of character feature importance and poor design coordination caused by blind adjustment of weight parameters in existing technologies. This ensures the coordination of animation character design, improves the recognizability of animation characters, and reduces the user's operational threshold.

[0014] In a preferred embodiment, this application can be further configured such that: the semantic parsing model includes a text processing layer and a feature extraction layer; the step of inputting the text information based on the anime character design from the user terminal into the pre-trained semantic parsing model for semantic feature extraction includes the following steps:

[0015] The text processing layer performs word segmentation on the input text information and identifies keywords related to the design intent;

[0016] The feature extraction layer extracts explicit and implicit features based on the design intent keywords and outputs them as semantic features.

[0017] By adopting the above technical solution, a semantic parsing model with a text processing layer and a feature extraction layer as its architecture is used to achieve deep understanding and accurate feature extraction of animation design text: the text processing layer performs structured word segmentation on the input text information and identifies design intent keywords; the feature extraction layer extracts explicit features and implicit features based on the design intent keywords and outputs them as semantic features; this application realizes the transformation from raw input text information to design elements through a layered and progressive text parsing process, which has the effect of improving the accuracy of semantic understanding and enhancing the comprehensiveness of feature extraction.

[0018] In a preferred embodiment, this application can be further configured as follows: the step of dividing semantic features into core features and modifier features based on a pre-stored role element classification table includes the following steps:

[0019] Calculate a multidimensional score vector of semantic features based on a pre-stored role element classification table;

[0020] The semantic features are divided into core features and modifier features based on multidimensional scoring vectors using a pre-trained classification decision tree.

[0021] Identify whether there are conflicting features, and if so, process them based on a pre-set conflict handling strategy.

[0022] By adopting the above technical solution, multi-dimensional scoring vectors are calculated for semantic features based on a pre-stored role element classification table. Subsequently, a pre-trained classification decision tree model is used to intelligently analyze the multi-dimensional scoring vectors and divide the semantic features into core features and modifying features. For possible feature conflicts, conflicting feature items are identified and pre-set conflict handling strategies are triggered for processing. This application achieves standardization and automation of feature classification by combining quantitative scoring with intelligent decision-making, which improves the accuracy of feature segmentation and the consistency of design logic.

[0023] In a preferred embodiment, this application can be further configured as follows: the step of constructing a graphical component database and mapping core features to immutable components and modifying features to variable components includes the following steps:

[0024] Retrieve standardized 3D mesh models that match the core features from the base model library and perform immutable processing to map them to immutable parts;

[0025] Create a parameterized template data structure for modifying features and configure an adjustable range of base parameters to map them to variable parts;

[0026] Establish assembly constraint relationships between immutable and variable parts and store them in the graphical part database.

[0027] By adopting the above technical solutions, intelligent component mapping and assembly constraint design are used to realize the transformation and collaboration of anime character design elements. Three-dimensional mesh models matching the core features are retrieved from the basic model library and converted into immutable components through immutable processing, ensuring the stability of the character's basic framework. Simultaneously, a parametric template data structure is created for decorative features, and flexible control of variable components is achieved by configuring the basic parameter range of adjustable parameters such as color, size, and texture. Assembly constraint relationships between immutable and variable components based on physical rules and aesthetic principles are established and stored in the graphic component database. This application achieves a balance between stability and flexibility in character design through differentiated processing of immutable and variable components and intelligent assembly constraints, enhancing the controllability of anime character design and reducing the probability of component conflicts.

[0028] In a preferred embodiment, this application can be further configured as follows: the step of establishing the assembly constraint relationship between immutable and variable parts and storing it in the graphic part database includes the following steps:

[0029] Determine the standard assembly anchor points and connection types on immutable components;

[0030] The assembly rule set is matched according to the physical characteristics of the variable component. The assembly rule set includes connection method, rotational degree of freedom and displacement restriction.

[0031] The physical rationality of the combination of variable and invariable parts based on the matched assembly rule set is verified by using a collision detection algorithm.

[0032] Establish assembly constraint relationships between variable and invariable components that have passed physical rationality verification.

[0033] By adopting the above technical solution, the geometric structure of immutable components is analyzed to determine the standard assembly anchor point positions and connection types, providing a benchmark reference for component docking. Then, the corresponding assembly rule set is matched according to the physical characteristics of variable components. Subsequently, the physical rationality of the combination of variable and immutable components based on the matched assembly rule set is verified by a collision detection algorithm. Finally, the assembly constraint relationship is established for the combination of variable and immutable components that has passed the physical rationality verification and stored in the database. This application realizes the establishment of connection relationships between components through standardized assembly anchor point definition, intelligent assembly rule set matching, and physical rationality verification process, which has the effect of improving assembly accuracy and ensuring the naturalness of movement, providing technical support for generating character designs that conform to physical laws.

[0034] In a preferred embodiment, this application can be further configured as follows: the step of verifying the physical rationality of the combination of variable and invariable parts based on the matched assembly rule set using a collision detection algorithm includes the following steps:

[0035] Perform multi-level conflict detection for combinations of variable and invariable components;

[0036] Detected conflicts are processed using a pre-defined strategy based on their conflict level.

[0037] By adopting the above technical solution, a hierarchical, multi-level conflict detection mechanism is used to analyze the conflict between variable and invariable component combinations. For detected conflicts, a pre-set processing strategy is executed based on the corresponding pre-defined conflict level. Through a hierarchical detection process and conflict classification processing, this application achieves accurate positioning and automatic repair of model component assembly problems, which improves detection efficiency and optimizes processing accuracy.

[0038] In a preferred embodiment, this application can be further configured as follows: the steps of assigning initial weights to variable components, adjusting the initial weights of variable components to modified weights using preset permission adjustment rules, and determining the dynamic parameter range of variable components based on the modified weights include the following steps:

[0039] Sentiment features are extracted from textual information, and initial weights are assigned to variable components based on these sentiment features.

[0040] Variable components are mapped to component numerical features, and these component numerical features are associated with initial weights.

[0041] The component numerical features are input into the pre-trained weight correction model, which then adjusts the initial weights to corrected weights based on the component numerical features.

[0042] By adopting the above technical solution, and using emotion-driven component weight adjustment, precise optimization of variable component parameters in animation character design is achieved: Emotional tendency features are extracted from user input text information based on text sentiment analysis technology, and initial weight values ​​are assigned to variable components based on these features; variable components are transformed into multi-dimensional component numerical features, and a correlation is established between these features and the initial weights; the component numerical features are input into a pre-trained weight correction model, which adjusts the initial weights to corrected weights by analyzing the relationship between component characteristics and design constraints; this application achieves adaptive optimization of design parameters through a three-stage process of emotional feature extraction, component digital representation, and weight correction, which improves the matching degree of user design intent and enhances component coordination, enabling the generated animation character model to reflect user emotional tendencies while meeting design standards.

[0043] The second objective of this invention is achieved through the following technical solution:

[0044] A text-based anime character design system, comprising:

[0045] The feature extraction module is used to input text information based on anime character design from the user terminal into a pre-trained semantic parsing model for semantic feature extraction.

[0046] The feature segmentation module is used to divide semantic features into core features and modifier features based on a pre-stored role element classification table.

[0047] The feature mapping module is used to build a database of graphical parts and map core features to immutable parts and decorative features to variable parts.

[0048] The parameter adjustment module is used to assign initial weights to variable components, adjust the initial weights of variable components to corrective weights according to preset permission adjustment rules, and determine the dynamic parameter range of variable components based on the corrective weights.

[0049] The design generation module is used to generate character design schemes based on immutable and variable components and then feed them back to the user.

[0050] By adopting the above technical solution, the feature extraction module is used to input the text information based on the anime character design from the user terminal into a pre-trained semantic parsing model for semantic feature extraction; the feature segmentation module is used to segment the semantic features into core features and decorative features based on a pre-stored character element classification table; the feature mapping module is used to construct a graphical component database and map the core features to immutable components and the decorative features to variable components; the parameter adjustment module is used to assign initial weights to variable components, adjust the initial weights of variable components to corrective weights through preset permission adjustment rules, and determine the dynamic parameter range of variable components based on the corrective weights; the design generation module is used to generate character design schemes based on immutable and variable components and feed them back to the user terminal.

[0051] In summary, this application includes at least one of the following beneficial technical effects:

[0052] 1. This application constructs a hierarchical feature processing and intelligent weight control system based on text content semantics, which realizes the optimization of the entire process of animation character design from semantic understanding to visual generation. It effectively solves the problems of insufficient differentiation of the importance of character features and poor design coordination caused by the blind adjustment of weight parameters in the existing technology. It has the effects of ensuring the coordination of animation character design, improving the recognizability of animation character design and reducing the user's operation threshold.

[0053] 2. This application achieves a balance between stability and flexibility in character design through differentiated processing of immutable and variable components and intelligent assembly constraints, which enhances the controllability of anime character design and reduces the probability of component conflicts.

[0054] 3. This application establishes the connection relationship between components through standardized assembly anchor point definition, intelligent assembly rule set matching, and physical rationality verification process, which improves assembly accuracy and ensures natural movement, providing technical support for generating character designs that conform to physical laws;

[0055] 4. This application achieves precise location and automatic repair of assembly problems of model parts through a hierarchical inspection process and conflict classification handling, which improves inspection efficiency and optimizes processing accuracy;

[0056] 5. This application achieves adaptive optimization of design parameters through a three-stage process of emotional feature extraction, component digital representation, and weight correction. This improves the matching degree of user design intent and enhances component coordination, enabling the generated animation character model to reflect user emotional tendencies and meet design standards. Attached Figure Description

[0057] Figure 1 This is a flowchart of an embodiment of an anime character design method based on text content according to this application;

[0058] Figure 2 This is a flowchart of step S20 in an embodiment of a text-based anime character design method of this application;

[0059] Figure 3 This is a flowchart of step S30 in an embodiment of a text-based anime character design method of this application;

[0060] Figure 4 This is a flowchart of step S33 in an embodiment of a text-based anime character design method of this application;

[0061] Figure 5 This is a flowchart of step S40 in an embodiment of a text-based anime character design method of this application. Detailed Implementation

[0062] The following is in conjunction with the appendix Figure 1-5 This application will be described in further detail.

[0063] In one embodiment, such as Figure 1 As shown, this application discloses a method for designing anime characters based on text content, specifically including the following steps:

[0064] S10: When receiving text information based on anime character design from the user, input the text information into a pre-trained semantic parsing model for semantic feature extraction;

[0065] In this embodiment, the text information is natural language text input by the user describing the design requirements of the anime character; the semantic parsing model is a pre-trained natural language processing model used to analyze the text information and extract the design elements and semantic features therein; the semantic features are structured design elements extracted from the text through the semantic parsing model, including attribute descriptions of various aspects of the character.

[0066] Specifically, a pre-trained semantic parsing model is used to deeply understand user text and extract structured design elements containing multi-faceted attribute descriptions of anime character design from the text information.

[0067] S20: Based on the pre-stored role element classification table, semantic features are divided into core features and modifier features;

[0068] In this embodiment, the character element classification table is a predefined classification rule table used to divide semantic features into categories of different importance; the core features are the most important design elements that determine the basic attributes and identity recognition of the character; the decorative features are secondary design elements used to enrich the details of the character, which can be adjusted within a certain range.

[0069] Specifically, semantic features are intelligently divided into core features and decorative features through a pre-stored role element classification table, establishing an importance hierarchy for design elements.

[0070] S30: Construct a database of graphical components and map core features to immutable components and decorative features to variable components;

[0071] In this embodiment, the graphic component database is a collection of data storing various graphic components and models required for character design; immutable components are fixed components corresponding to core features, whose shape and attributes remain unchanged in the design; variable components are adjustable components corresponding to decorative features, allowing for parametric modification.

[0072] Specifically, by mapping core features to immutable components and modifying features to variable components, the technical solution fundamentally solves the problem that existing parameterization systems have difficulty distinguishing the importance of features.

[0073] S40: Assign initial weights to variable components, adjust the initial weights of variable components to modified weights according to preset permission adjustment rules, and determine the dynamic parameter range of variable components based on the modified weights;

[0074] In this embodiment, the initial weight is the initial adjustment permission value assigned to the variable component based on semantic features; the permission adjustment rule is a set of rules defining how to modify the weight of the variable component according to constraints; the corrected weight is the final weight value after optimization by the permission adjustment rule; and the dynamic parameter range is the parameter range that the variable component is allowed to adjust, calculated based on the corrected weight.

[0075] Specifically, initial weights are assigned to variable components based on semantic parsing results, multi-dimensional corrections are made through preset permission adjustment rules, and the dynamic parameter range of variable components is determined based on the corrected weights.

[0076] S50: Generate character design schemes based on immutable and variable components and feed them back to the user.

[0077] In this embodiment, the character design scheme is the final generated complete character design scheme, which includes all components and their parameter settings, as well as the corresponding image, model, etc.

[0078] Specifically, character design schemes based on anime character designs are generated using both immutable and variable components, and then fed back to the user for confirmation and manual adjustments.

[0079] In one embodiment, the semantic parsing model includes a text processing layer and a feature extraction layer, and step S10 includes the following steps:

[0080] S11: The text processing layer performs word segmentation on the input text information and identifies the design intent keywords;

[0081] S12: The feature extraction layer extracts explicit and implicit features based on the design intent keywords and outputs them as semantic features.

[0082] In this embodiment, the text processing layer is the first-stage processing module layer of the semantic parsing model, used to perform basic processing on the original text information, including preprocessing operations such as word segmentation and keyword recognition; the feature extraction layer is the second-stage processing module layer of the semantic parsing model, which further extracts explicit and implicit features based on the output of the text processing layer, and outputs semantic features; word segmentation is the process of segmenting continuous user input text information according to semantic units; design intent keywords are core words in the text that directly express the user's design needs, such as specific design elements such as "mechanical wing" and "glowing"; explicit features are design features that are clearly stated in the text information and can be directly obtained, such as intuitive attributes such as color and shape; implicit features are potential design features in the text information that require reasoning and analysis to obtain, such as deep information such as style preferences and functional requirements.

[0083] Specifically, the semantic parsing model, with a text processing layer and a feature extraction layer as its architecture, achieves deep understanding and accurate feature extraction of animation design text: the text processing layer performs structured word segmentation on the input text information and identifies design intent keywords; the feature extraction layer extracts explicit and implicit features based on the design intent keywords and outputs them as semantic features.

[0084] In one embodiment, such as Figure 2 As shown, step S20 includes the following steps:

[0085] S21: Calculate the multidimensional score vector of semantic features based on the pre-stored role element classification table;

[0086] S22: Using a pre-trained classification decision tree, semantic features are divided into core features and modifier features based on multi-dimensional scoring vectors;

[0087] S23: Identify whether there are conflicting features. If so, process them based on the pre-set conflict handling strategy.

[0088] In this embodiment, the multidimensional scoring vector is a numerical vector representing semantic feature attributes, including quantitative scores for multiple dimensions such as feature importance, design relevance, and user preference intensity; the classification decision tree is a pre-trained machine learning model that uses a tree-structured multi-level judgment rule to automatically make classification decisions based on the multidimensional scoring vector of the input features; conflicting feature items are combinations of mutually contradictory or mutually exclusive design elements identified during feature classification; the conflict handling strategy is a predefined set of rules for resolving feature conflicts, including priority determination, feature fusion, and other processing methods.

[0089] Specifically, based on the pre-stored role element classification table, multi-dimensional score vectors are calculated for semantic features. Then, a pre-trained classification decision tree model is used to intelligently analyze the multi-dimensional score vectors and divide the semantic features into core features and modifying features. For possible feature conflicts, conflicting feature items are identified and pre-set conflict handling strategies are triggered for processing.

[0090] In one embodiment, such as Figure 3 As shown, step S30 includes the following steps:

[0091] S31: Retrieve a standardized 3D mesh model that matches the core features from the base model library and perform immutable processing to map it to immutable parts;

[0092] S32: Create a parameterized template data structure for the modification features and configure an adjustable range of base parameters to map them to variable parts;

[0093] S33: Establish assembly constraint relationships between immutable and variable parts and store them in the graphic part database.

[0094] In this embodiment, the basic model library is a pre-set 3D model resource library containing various standardized character component models and their metadata, organized and stored according to design element classification; the standardized 3D mesh model is a triangular mesh model conforming to unified modeling specifications, containing geometric data such as vertex coordinates and face connection relationships, as well as additional attributes such as materials and skeletons; immutable processing refers to the solidification operations performed on model components, including locking the geometric topology, fixing material parameters, and disabling deformation animations, to ensure that model attributes cannot be changed; the parameterized template data structure is a structured data format that defines the attributes of variable components, containing metadata such as parameter types, value ranges, and relationships; the basic parameter range is the legal adjustment interval set for each variable parameter, usually represented as a numerical range of [min, max] or an enumerated set of options; the assembly constraint relationship is a set that defines the connection methods and interaction rules between different variable components and immutable components, including constraints such as spatial positional relationships and motion transmission logic; the graphic component database is a structured database that stores processed components and their relationships, providing data support for subsequent design generation;

[0095] Specifically, the system retrieves 3D mesh models that match the core features from the basic model library and performs immutable processing to transform them into immutable parts, ensuring the stability of the character's basic framework. At the same time, it creates a parameterized template data structure for decorative features, and achieves flexible control of variable parts by configuring the basic parameter range of adjustable parameters such as color, size, and texture. It establishes assembly constraint relationships between immutable and variable parts based on physical rules and aesthetic principles, and stores them in the graphic part database.

[0096] In one embodiment, such as Figure 4 As shown, step S33 includes the following steps:

[0097] S331: Determine the standard assembly anchor points and connection types on immutable components;

[0098] S332: Match the corresponding assembly rule set according to the physical characteristics of the variable component. The assembly rule set includes connection method, rotational degree of freedom and displacement restriction.

[0099] S333: Verify the physical rationality of the combination of variable and invariable parts based on the matched assembly rule set using a collision detection algorithm;

[0100] S334: Establish assembly constraint relationships for combinations of variable and invariable components that have passed physical rationality verification.

[0101] In this embodiment, the standard assembly anchor point is a three-dimensional spatial coordinate point preset on the immutable component, used to determine the connection position of other components, usually located at key positions such as joints and connections; the connection type defines the classification of connection methods between components, including different types such as hinge, fixed, and sliding, each type corresponding to specific motion constraints; physical characteristics are a set of attributes describing the physical behavior of the variable component, including parameters such as mass distribution, material stiffness, and collision volume; the collision detection algorithm is a computer graphics algorithm used to detect interference between three-dimensional models, judging the rationality of the assembly by calculating the intersection of geometric bodies; physical rationality verification is a process of comprehensively evaluating whether the component combination conforms to physical laws and design specifications, including tests such as geometric interference checks and motion range verification;

[0102] Specifically, the geometric structure of immutable components is analyzed to determine the location and connection type of standard assembly anchor points, providing a benchmark reference for component docking; then, the corresponding assembly rule set is matched according to the physical characteristics of variable components; then, the physical rationality of the combination of variable and immutable components based on the matched assembly rule set is verified by a collision detection algorithm; finally, the assembly constraint relationship is established for the combination of variable and immutable components that has passed the physical rationality verification and stored in the database.

[0103] In one embodiment, step S333 includes the following steps:

[0104] S3331: Perform multi-level conflict detection for combinations of variable and invariable components;

[0105] S3332: Execute a pre-set processing strategy for detected conflicts based on their conflict level.

[0106] In this embodiment, the multi-level conflict detection is a hierarchical conflict detection mechanism; the conflict level is a problem level divided according to the severity of the conflict, including three levels: minor, moderate, and severe, each with a corresponding different handling strategy; the preset handling strategy is a set of pre-programmed conflict solutions, which provides responses such as automatic repair, warning prompts, or design suggestions for different conflict levels.

[0107] Specifically, a hierarchical, multi-level conflict detection mechanism is used to perform conflict analysis on combinations of variable and invariable components; for detected conflicts, a pre-set processing strategy is executed based on the corresponding pre-defined conflict level.

[0108] In one embodiment, such as Figure 5 As shown, step S40 includes the following steps:

[0109] S41: Extract sentiment features based on text information, and assign initial weights to variable components based on sentiment features;

[0110] S42: Map the variable components to component numerical features and associate the component numerical features with the initial weights;

[0111] S43: Input the component numerical features into the pre-trained weight correction model, so that the weight correction model adjusts the initial weights to corrected weights based on the component numerical features.

[0112] In this embodiment, the sentiment tendency feature is a quantitative indicator extracted from user text information that reflects the intensity of user preferences and is used to measure the degree of importance users attach to different design elements; the component numerical feature is a structured parameter representation of variable components, including quantifiable features such as geometric attributes (size, shape) and material attributes (color, texture); the weight correction model is a pre-trained machine learning model used to combine component characteristics, design constraints and other factors to optimize the initial weights so that they better meet the design needs of actual users.

[0113] Specifically, based on text sentiment analysis technology, the sentiment characteristics of user input text information are extracted, and initial weight values ​​are assigned to variable components based on the sentiment characteristics; the variable components are transformed into multi-dimensional component numerical features, and the component numerical features are associated with the initial weights; the component numerical features are input into a pre-trained weight correction model, and the weight correction model adjusts the initial weights to corrected weights by analyzing the relationship between component characteristics and design constraints.

[0114] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0115] In one embodiment, a text-based anime character design system is provided, and the text-based anime character design method corresponds one-to-one with the text-based anime character design system described in the above embodiment. The text-based anime character design system includes:

[0116] The feature extraction module is used to input text information based on anime character design from the user terminal into a pre-trained semantic parsing model for semantic feature extraction.

[0117] The feature segmentation module is used to divide semantic features into core features and modifier features based on a pre-stored role element classification table.

[0118] The feature mapping module is used to build a database of graphical parts and map core features to immutable parts and decorative features to variable parts.

[0119] The parameter adjustment module is used to assign initial weights to variable components, adjust the initial weights of variable components to corrective weights according to preset permission adjustment rules, and determine the dynamic parameter range of variable components based on the corrective weights.

[0120] The design generation module is used to generate character design schemes based on immutable and variable components and then feed them back to the user.

[0121] For specific limitations regarding a text-based anime character design system, please refer to the limitations of a text-based anime character design method described above, which will not be repeated here. Each module in the aforementioned text-based anime character design system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0122] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for designing anime characters based on text content, characterized in that: The method comprises the steps of: When receiving text information based on the design of an animation character from a user terminal, inputting the text information into a pre-trained semantic analysis model to extract semantic features; Based on a pre-stored role element classification table, dividing the semantic features into core features and modification features; Building a graphic component database and mapping the core features into immutable components and the modification features into variable components; Assigning initial weights to the variable components, adjusting the initial weights of the variable components to modified weights through a pre-set permission adjustment rule, and determining the dynamic parameter range of the variable components based on the modified weights; Generating a character design image scheme based on the immutable components and the variable components and feeding back to the user terminal; The role element classification table is a pre-defined classification rule table for dividing semantic features into different importance categories; The core features are the most important design elements that determine the basic attributes and identity recognition of the character; The modification features are secondary design elements for enriching the details of the character, which allow adjustment within a certain range.

2. The method of claim 1, wherein the method further comprises: The semantic analysis model comprises a text processing layer and a feature extraction layer, and the step of inputting the text information into the pre-trained semantic analysis model to extract semantic features when receiving text information based on the design of an animation character from a user terminal comprises the steps of: The text processing layer performs word segmentation processing on the input text information and identifies design intent keywords; The feature extraction layer extracts display features and implicit features based on the design intent keywords and outputs them as semantic features.

3. The method of claim 1, wherein the method further comprises: The step of dividing the semantic features into core features and modification features based on the pre-stored role element classification table comprises the steps of: Calculating a multi-dimensional score vector of the semantic features based on the pre-stored role element classification table; Dividing the semantic features into core features and modification features based on the multi-dimensional score vector through a pre-trained classification decision tree; Identifying whether there are conflicting feature items, and if so, processing them based on a pre-set conflict handling strategy.

4. The method of claim 1, wherein the method further comprises: The step of building a graphic component database and mapping the core features into immutable components and the modification features into variable components comprises the steps of: Retrieving a standardized three-dimensional grid model matching the core features from a basic model library and performing immutable processing to map it into an immutable component; Creating a parameterized template data structure for the modification features and configuring adjustable basic parameter ranges to map them into variable components; Establishing assembly constraint relationships between the immutable components and the variable components and storing them in the graphic component database.

5. The method of claim 4, wherein the method further comprises: The step of establishing assembly constraint relationships between the immutable components and the variable components and storing them in the graphic component database comprises the steps of: Determining standard assembly anchor points and connection types on the immutable components; Matching corresponding assembly rule sets according to the physical properties of the variable components, the assembly rule sets including connection methods, rotational degrees of freedom, and displacement limits; Verifying the physical rationality of the combination of the variable components and the immutable components based on the matched assembly rule sets through a collision detection algorithm; Establishing assembly constraint relationships for the combination of the variable components and the immutable components that pass the physical rationality verification.

6. The method of claim 5, wherein the method further comprises: The step of verifying the physical rationality of the combination of the variable component and the non-variable component based on the matched assembly rule set through the collision detection algorithm comprises the steps of: performing multi-level conflict detection on the variable component combination and the non-variable component combination; performing a pre-set processing strategy on the detected conflict based on the conflict level of the conflict.

7. The method of claim 1, wherein the method further comprises: The step of assigning an initial weight to the variable component, adjusting the initial weight of the variable component to a corrected weight through a pre-set authority adjustment rule, and determining the dynamic parameter range of the variable component based on the corrected weight comprises the steps of: extracting sentiment tendency features based on the text information, and assigning an initial weight to the variable component based on the sentiment tendency features; mapping the variable component to a component numerical feature, and associating the component numerical feature with the initial weight; inputting the component numerical feature into a pre-trained weight correction model, and adjusting the initial weight to a corrected weight based on the component numerical feature. 8.A system for designing an animation character based on text content, characterized in that: Comprise: a feature extraction module for inputting text information based on an animation character design received from a user end into a pre-trained semantic analysis model for semantic feature extraction; a feature division module for dividing the semantic features into core features and modification features based on a pre-stored character element classification table; a feature mapping module for constructing a graphic component database, and mapping the core features to non-variable components and the modification features to variable components; a parameter adjustment module for assigning an initial weight to the variable component, adjusting the initial weight of the variable component to a corrected weight through a pre-set authority adjustment rule, and determining the dynamic parameter range of the variable component based on the corrected weight; a design generation module for generating a character design image scheme based on the non-variable components and the variable components and feeding back to the user end; the character element classification table is a pre-defined classification rule table for dividing the semantic features into different importance categories; the core features are the most important design elements that determine the basic attributes and identity recognition of the character; the modification features are secondary design elements for enriching the details of the character, which allow adjustment within a certain range.

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