3D Scene Generation via Feature Pair Correlation
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Solution Overview
Problem
Conventional three-dimensional scene generation models using deep learning require extensive labeled data for training and lack interactive editing capabilities, making them costly and inefficient for user-customized scene modifications.
Innovation Solution
A method that extracts source image features from two-dimensional images and editing features from user instructions, forming feature pairs to update and generate three-dimensional scenes without the need for extensive model training, allowing for arbitrary editing and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image generation models are used to synthesize three-dimensional views, then the basic three-dimensional scene generation can be achieved, but interactive three-dimensional scene editing capability is lost
Solution Approach 1:
The patent segments the three-dimensional scene into multiple two-dimensional source images viewed from different angles. Each source image is processed independently to extract features, and the editing operations are applied to individual source images rather than the entire three-dimensional scene. This segmentation enables interactive editing while keeping the processing complexity manageable by dividing the problem into smaller, independent sub-problems.
Solution Approach 2:
The patent introduces source image features as an intermediary representation between the input two-dimensional images and the final three-dimensional scene. These features serve as a bridge that can be selectively modified through editing instructions while maintaining the overall scene structure. The feature pairs (source image features and editing features) act as intermediaries that enable controlled editing without requiring complete scene reconstruction.
2Adaptability or versatility
If complex image generation models are trained to achieve three-dimensional scene editing, then editing functionality is improved, but training costs and data annotation requirements increase significantly
Solution Approach 1:
The patent uses existing three-dimensional scene generation models as a basis and creates edited versions by modifying source image features rather than training entirely new models. The editing process copies the fundamental scene generation capabilities and adapts them through feature manipulation. This approach avoids the time-consuming process of training complex models from scratch while still achieving sophisticated editing functionality.
Solution Approach 2:
The patent achieves editing by changing parameters in the source image features rather than retraining the entire model. The correlation coefficient maximization updates the source image features by adjusting their parameters to align with editing instructions. This parameter-based approach allows for rapid editing operations without the computational burden of model retraining, significantly reducing time loss.
3Measurement precision
If expensive labeled data annotations are prepared for model training, then model accuracy is improved, but cost and preparation time increase
Solution Approach 1:
The patent enables the system to perform self-service editing by directly manipulating source image features based on user instructions without requiring external labeled data. The correlation coefficient maximization process automatically adjusts features to match editing intentions, making the system self-sufficient. This eliminates the need for expensive manual data annotation while maintaining accurate scene generation and editing capabilities.
Solution Approach 2:
The patent creates a universal editing framework that can handle various editing operations (adding objects, removing objects, modifying properties) using the same source image feature manipulation mechanism. This multi-functional approach allows a single system to perform diverse editing tasks without requiring specialized training data for each operation, reducing overall data annotation requirements while maintaining high accuracy across different editing scenarios.
Data Source
AI summary
A method, an electronic device, and a computer program product for generating a three-dimensional scene are provided in embodiments of the present disclosure. The method may include obtaining source image features from a plurality of two-dimensional source images associated with the three-dimensional scene to be generated. The method may further include obtaining editing features from an editing instruction input by a user for the three-dimensional scene, each of the editing features respectively forming a feature pair with each of the source image features. Furthermore, the method may include updating the source image features by maximizing a correlation coefficient of each of the feature pairs, and generating the three-dimensional scene based at least on the updated source image features. Embodiments of the present disclosure can realize arbitrary editing of a three-dimensional scene, thus enhancing the experience of human-computer interaction.