Semantic 3D Object Generation With Natural Connected Motions
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Solution Overview
Problem
Current three-dimensional modeling technologies are insufficient in generating dynamic objects that naturally reproduce human motion and convert moving objects into three-dimensional space, with a focus on static object production and limited development in recognizing and expressing human motion.
Innovation Solution
A method and system that analyzes a sentence in text format to generate a three-dimensional object by defining motions corresponding to the sentence's meaning, using a trained artificial neural network to create a moving three-dimensional object with connected motions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current three-dimensional modeling technology is used, then static objects can be produced, but dynamic objects with natural human motion cannot be generated
Solution Approach 1:
The patent replaces traditional mechanical 3D modeling systems with an AI-based system that uses neural networks to generate dynamic objects. The system takes text descriptions as input and automatically generates 3D models with natural human motions, eliminating the need for manual mechanical modeling while achieving more natural and versatile dynamic object generation.
Solution Approach 2:
The system transforms static 3D models into dynamic ones by changing the state parameters of the models. It generates motion parameters and temporal information from text descriptions, allowing the same 3D structure to exhibit different motions and behaviors, thereby enabling versatile dynamic object generation with natural human motion.
2Productivity
If manual definition of motions is required, then motion precision can be controlled, but generation time and operational complexity increase
Solution Approach 1:
The system performs self-service by automatically generating 3D objects with natural motions from text descriptions without requiring manual intervention. The neural network processes the text input and autonomously creates the complete 3D model with appropriate motion parameters, eliminating the need for users to manually define motions while maintaining high generation speed.
Solution Approach 2:
The system performs preliminary action by pre-processing text descriptions and extracting motion parameters before generating the final 3D model. The neural network analyzes the text input in advance to determine the appropriate motions and temporal characteristics, enabling rapid generation without requiring manual motion definition during the creation process.
3Adaptability or versatility
If focus is on static object production, then existing tools are well-optimized, but conversion of moving dynamic objects into three dimensions is insufficient
Solution Approach 1:
The patent creates a universal system that can handle both static and dynamic object generation through a single neural network architecture. The same system processes text descriptions to generate either static 3D models or dynamic 3D models with natural motions, eliminating the need for separate specialized tools while maintaining optimization for both functions.
Solution Approach 2:
The system introduces dynamics by enabling the generated 3D models to perform natural human motions. The neural network generates temporal information and motion parameters that allow the static 3D structure to animate dynamically, transforming the system from purely static object generation to dynamic object conversion while managing complexity through AI-based approaches.
Data Source
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AI summary
The present invention relates to a method and a system for generating a three-dimensional object on the basis of semantic analysis. More specifically, the present invention relates to a method and a system for analyzing a sentence in a text format to automatically generate a three-dimensional object corresponding to the meaning of the sentence.