NLP Voxel Object Generation via Variational Autoencoder
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack the ability to efficiently generate three-dimensional objects in virtual spaces using natural language processing, particularly in online gaming platforms, where user-generated content requires conversion between different three-dimensional formats and lacks seamless integration with voxel-based systems.
Innovation Solution
A system utilizing natural language processing (NLP) that converts textual descriptions into voxelized three-dimensional objects through a variational autoencoder, fine-tuning a pretrained text-based system to generate multi-dimensional vectors, and decoding these vectors into voxelized objects, enabling the creation of user-provided three-dimensional objects in virtual spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user-generated three-dimensional content is converted between different three-dimensional formats, then compatibility with various systems is improved, but conversion complexity and processing time increase
Solution Approach 1:
The patent introduces a format conversion module as an intermediary component that handles the transformation between different three-dimensional formats (voxel, mesh, point cloud). This intermediary module abstracts the conversion complexity, allowing the system to maintain compatibility with various formats without requiring each component to implement multiple conversion algorithms, thus resolving the contradiction between versatility and complexity.
Solution Approach 2:
The system employs a universal three-dimensional object representation framework that can handle multiple formats (voxel, mesh, point cloud) through a common interface. The format conversion module serves multiple functions by supporting bidirectional conversion between different formats, reducing the need for separate conversion routines and simplifying the overall system architecture while maintaining broad compatibility.
2Ease of operation
If natural language processing is integrated into the system, then user experience and content creation capabilities are enhanced, but system complexity and computational requirements increase
Solution Approach 1:
The patent introduces an NLP processing module as an intermediary layer between the user interface and the three-dimensional object generation system. This module handles natural language input, extracts relevant semantic information, and translates it into parameters for object generation. By isolating the NLP complexity in a dedicated module, the system enhances user experience without requiring the core three-dimensional processing components to become more complex.
Solution Approach 2:
The system segments the overall functionality into distinct modules: NLP processing module for text analysis, object generation module for creating three-dimensional content, and format conversion module for compatibility handling. This segmentation allows each module to specialize in its specific function, improving ease of operation through natural language interfaces while containing system complexity within manageable, independent components.
3Productivity
If voxelized three-dimensional objects are generated from textual descriptions, then productivity and automation are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing by pre-defining a library of three-dimensional object templates and pre-computing their voxelized representations. When generating objects from textual descriptions, the system retrieves and modifies these pre-prepared templates rather than creating objects from scratch, significantly reducing processing time while maintaining high automation levels. The NLP module quickly matches user descriptions to appropriate templates and applies parameter adjustments.
Solution Approach 2:
The system uses template copying as a strategy to accelerate object generation. Instead of performing full voxelization computations for each new object, the system creates copies of pre-voxelized object templates and modifies their parameters based on the textual description. This copying approach maintains automation and productivity while dramatically reducing the computational time required compared to generating objects de novo.
Data Source
AI summary
Systems and methods for using natural language processing (NLP) to automatically generate three-dimensional objects in a virtual space are disclosed. Exemplary implementations may: obtain three-dimensional objects using a three-dimensional voxelized format; encode those objects, using a variational autoencoder, into pairs of vectors that are subsequently sampled; decode the sampled vectors; determine loss information for the decoded voxelized three-dimensional objects; use the loss information to train the variational autoencoder; fine-tune a pretrained text-based system; receive user input describing a three-dimensional object; generate a vector from the user input; decode the vector into a voxelized three-dimensional object; present the voxelized three-dimensional object to the user.