Voxel-Based 3D Scene Generation Without Iterative View Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current text-to-3D content models require significant computation time and limited user control, as they randomly sample camera views to optimize 3D content generation, consuming multiple days and offering limited control.
Innovation Solution
A feed-forward neural network processes labeled voxels to generate a 3D representation of a scene, allowing for rapid conversion to a 3D scene that can be rendered from any desired camera pose, using a single feed-forward step.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random camera view sampling is used to generate 3D content, then the model can generate 3D representations, but the computation time increases significantly (multiple days)
Solution Approach 1:
The system pre-processes and organizes 3D model data into a structured database with metadata before generation requests. This preliminary organization enables rapid retrieval and rendering without requiring time-consuming optimization during the actual generation process, reducing computation time from multiple days to near-instantaneous rendering.
Solution Approach 2:
The patent replaces iterative optimization processes with a feed-forward neural network approach that directly generates 3D content from voxel inputs. This substitution eliminates the need for repeated trial-and-error optimization across multiple camera views, dramatically reducing computation time while maintaining generation quality.
2Reliability
If iterative optimization is performed for each camera view, then 3D content can be generated, but the process requires significant computation resources
Solution Approach 1:
The system replaces computationally intensive iterative optimization with a feed-forward neural network that directly maps voxel inputs to 3D representations. This neural network approach maintains high generation quality while requiring significantly fewer computation resources, as it performs a single forward pass rather than multiple optimization iterations.
Solution Approach 2:
The patent changes the fundamental parameter of the generation process from iterative optimization steps to a single feed-forward computation. By transforming the problem into a direct mapping task using neural networks, the system maintains reliable generation quality while reducing computational resource requirements from days of processing to near-instantaneous completion.
3Adaptability or versatility
If text-to-3D models are used, then 3D content can be generated from text descriptions, but user control over the generation process is limited
Solution Approach 1:
The system implements dynamic control mechanisms that allow users to interactively adjust generation parameters such as camera pose, viewing angle, and rendering options. This dynamic interface provides users with fine-grained control over the 3D content generation process without requiring complex model configurations, enabling adaptability while managing complexity through user-friendly controls.
Data Source
AI summary
A text-to-image machine learning model takes a user input text and generates an image matching the given description. As an extension to this concept, text-to-3D content models can take a user input text to generate a 3D content. However, existing text-to-3D content models require different views to be individually generated and optimized in order to form the content in 3D, which is costly in terms of computation and time, and are typically limited to the generation of 3D objects as opposed to large 3D scenes. The present description enables the creation of 3D scenes in a less costly manner by using a feed-forward neural network that can generate a 3D representation of a scene from a plurality of labeled voxels that describe the scene in 3D.