Scene Graph Distribution Transformation for Realistic Synthetic Data
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Solution Overview
Problem
Conventional methods for generating synthetic data fail to accurately replicate real-world environments, leading to inefficiencies in training machine learning models due to a domain gap and content gap between synthetic and real-world data.
Innovation Solution
A generative model, such as a graph convolutional network (GCN), transforms initial scene graphs to update attributes, bridging the domain and content gaps by generating synthetic datasets that more closely resemble real-world scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If domain randomization is used to generate synthetic data, then the amount of diverse scenes is increased, but the realism and accuracy of the synthetic environments deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming the attribute distributions of objects in synthetic scenes through a generative model. The model learns from real-world data and adjusts parameters such as location, pose, color, texture, shape, and size to match real-world distributions, thereby improving realism while maintaining diverse scene generation capabilities
Solution Approach 2:
The patent uses copying by creating synthetic scenes that replicate the structural and attribute characteristics of real-world scenes. The generative model copies the distribution patterns of real-world objects and environments, producing synthetic data that preserves the statistical properties and spatial relationships found in real-world datasets
2Manufacturing precision
If GANs are used to transform appearance of synthetic data, then the photo-realistic appearance is improved, but the content accuracy and realism of object attributes deteriorates
Solution Approach 1:
The patent applies segmentation by separating the transformation process into two independent components: (1) a scene graph generation component that ensures content accuracy and proper object attributes, and (2) a generative model component that transforms appearance to be photo-realistic. This segmentation allows each component to optimize for its specific function without compromising the other
Solution Approach 2:
The patent uses an intermediary approach by introducing scene graphs as a structured representation that mediates between the synthetic scene generation and the appearance transformation. The scene graphs serve as an intermediate structure that preserves semantic information and spatial relationships while the generative model transforms the visual appearance, ensuring both content accuracy and photo-realism
3Manufacturing precision
If manual creation of virtual worlds is used, then the accuracy of synthetic environments is improved, but the labor and time required increases
Solution Approach 1:
The patent applies self-service by enabling the system to automatically generate and transform synthetic scenes without requiring manual intervention from experts. The generative model autonomously learns from real-world data and produces accurate synthetic environments, replacing the manual labor of creating virtual worlds while maintaining high accuracy
Solution Approach 2:
The patent uses preliminary action by pre-training the generative model on real-world data to learn the distributions and characteristics of real environments. This preliminary learning enables the model to automatically generate accurate synthetic scenes without requiring manual creation, significantly reducing the time and labor needed while maintaining environmental accuracy
4Productivity
If synthetic data is generated without proper distribution transformation, then the data generation speed is improved, but the training effectiveness of downstream task networks deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming the attribute distributions of synthetic scenes to match real-world distributions. The generative model adjusts parameters such as object locations, poses, colors, textures, shapes, and sizes to create synthetic data that is statistically similar to real-world data, thereby ensuring training effectiveness while maintaining fast generation speeds
Data Source
AI summary
In various examples, a generative model is used to synthesize datasets for use in training a downstream machine learning model to perform an associated task. The synthesized datasets may be generated by sampling a scene graph from a scene grammar—such as a probabilistic grammar—and applying the scene graph to the generative model to compute updated scene graphs more representative of object attribute distributions of real-world datasets. The downstream machine learning model may be validated against a real-world validation dataset, and the performance of the model on the real-world validation dataset may be used as an additional factor in further training or fine-tuning the generative model for generating the synthesized datasets specific to the task of the downstream machine learning model.