Multiple Customized Avatars Through Label-Grouped Model Retrieval
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Solution Overview
Problem
Current methods for generating user avatars are limited, lacking customization and personal identification, and require significant user interaction time, failing to meet user needs for quick and personalized avatar creation.
Innovation Solution
A method utilizing a deep-learning text-to-image diffusion model (Stable Diffusion) to generate multiple customized user avatars based on user preferences, allowing selection and real-time regeneration of avatars through a classification label system, including person, action, object, and ornament labels, and incorporating user behavior data for improved personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a deep-learning text-to-image diffusion model is used to generate avatars, then customization and personal identification are improved, but generation time and computational resources increase
Solution Approach 1:
The system pre-generates multiple avatar images in advance using the diffusion model, stores them in a database, and allows users to select from these pre-generated options. This eliminates the need for real-time generation during user interaction, resolving the contradiction between high customization quality and generation time by performing the computationally intensive work beforehand.
2Adaptability or versatility
If multiple avatar options are generated and stored in a database, then user selection flexibility is improved, but system complexity and storage requirements increase
Solution Approach 1:
The system divides the avatar generation process into separate modules: a pre-generation module that creates avatar images, a database module that stores them with metadata, and a user interface module that displays and handles selections. This segmentation allows each module to be optimized independently, reducing overall system complexity while maintaining flexibility.
3Speed
If real-time avatar generation is implemented, then user interaction speed is improved, but processing power and time consumption increase
Solution Approach 1:
Instead of generating unique avatars in real-time for each user interaction, the system creates multiple copies of avatar images in advance and stores them in the database. Users can quickly select from these pre-existing copies, achieving fast interaction speed without requiring high processing power during user sessions.
Data Source
AI summary
Method for rapidly generating multiple customized user avatars including a user selects a plurality of classifying labels according to his own preferences, including person/action/background/object/ornament label. The computation processor combines those classifying labels into a label parameter groups, and filters out the same or similar ones from the model parameter list according to the label parameter group a plurality of model parameters corresponding to those label parameter groups. Further, the computation processor extracts a corresponding plurality of avatar models from the model database according to those model parameters, and then packages those avatar models and sends them to the application program. The application program receives those avatar models and unpacks them and displays them for the user to select, if the user selects one of those avatar models, the application program binds the avatar models to the user.