3D Morphable Model Editing From Natural Language Commands
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Solution Overview
Problem
Current 3D object generation tools, particularly for avatars, do not effectively utilize natural language descriptions, leading to a lack of intuitive and personalized customization options for users.
Innovation Solution
A method utilizing artificial neural networks to edit initial textured 3D morphable models based on user commands, such as text or speech inputs, to generate customizable and personalized 3D avatars, including editing texture maps and 3D mesh models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional 3D modeling tools are used, then interface compatibility and editing frameworks are maintained, but the ability to interpret natural language descriptions and produce user-vision-accurate results deteriorates
Solution Approach 1:
The patent introduces text encoders and image encoders as intermediary components that bridge natural language descriptions and 3D model attributes. These encoders translate user commands into latent representations that guide the generation process, enabling accurate interpretation of natural language while maintaining control over 3D model precision through the diffusion model's attribute conditioning mechanisms
Solution Approach 2:
The system dynamically adjusts 3D model parameters (such as shape, texture, and pose attributes) based on encoded user commands. The diffusion model modifies these parameters iteratively during the generation process, allowing natural language descriptions to directly influence specific model attributes while maintaining overall structural integrity and visual fidelity
2Manufacturing precision
If complex attribute descriptions are required for 3D object generation, then control precision over model attributes is improved, but the ease of operation and intuitiveness deteriorates
Solution Approach 1:
The patent replaces complex manual attribute adjustment mechanisms with an AI-driven diffusion model system. Instead of requiring users to manually control multiple 3D parameters through complex interfaces, the system uses trained neural networks to automatically interpret natural language and generate corresponding attribute adjustments, substituting mechanical control with intelligent automation while maintaining precision
Solution Approach 2:
The system performs self-service by automatically encoding user commands and translating them into appropriate 3D model attributes without requiring user expertise. The text encoder and diffusion model work together to autonomously determine the correct parameter adjustments based on the user's natural language description, eliminating the need for users to understand complex attribute specifications
3Manufacturing precision
If skilled artists are involved in content creation processes, then manufacturing precision and quality are improved, but productivity and time efficiency deteriorates
Solution Approach 1:
The system enables self-service content creation by allowing users to generate 3D objects directly through natural language descriptions without requiring skilled artists. The diffusion model, trained on diverse 3D data, automatically produces high-quality results that were previously dependent on expert human intervention, thereby democratizing access to professional-grade 3D generation while dramatically improving productivity
Solution Approach 2:
The system automatically adjusts and optimizes multiple 3D model parameters simultaneously based on user commands, a task that would traditionally require skilled artists to manually adjust each parameter. The diffusion model's ability to coordinate changes across shape, texture, pose, and other attributes in a single generation process maintains quality while eliminating time-consuming manual iteration
Data Source
AI summary
A method for user command-guided editing of an initial textured 3D morphable model of an object comprising: obtaining the initial textured 3D morphable model of the object comprising an initial texture map and an initial 3D mesh model of the object; and determining an edited texture map of the object corresponding to the user command by editing the initial texture map of the object based on a first artificial neural network; and/or determining an edited 3D mesh model of the object corresponding to the user command by editing the initial 3D mesh model of the object based on a second artificial neural network; and generating an edited textured 3D morphable model of the object corresponding to the user command based on the edited texture map of the object and/or the edited 3D mesh model of the object.