Relighting Images Using Learned Lighting and Geometry
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Solution Overview
Problem
Current image decomposition models face challenges in generalizing from synthetic to real datasets due to the domain gap, and lack of ground truth information in real datasets makes training difficult, especially in ill-posed inverse problems like decomposing single view images into albedo, normals, and lighting components.
Innovation Solution
A novel machine learning model is trained on a mix of real and synthetic datasets, using self-supervision terms like reconstruction, cross-relighting, and adversarial training to estimate lighting and geometry components, ensuring consistency across multiple views and distributions, allowing for single-image inference and relighting capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If models are trained on synthetic datasets with known ground truth, then direct supervision can be applied over estimation of albedo, normals, and lighting information, but models have a hard time generalizing to real data due to the huge domain gap between real and synthetic datasets
Solution Approach 1:
The patent combines synthetic and real datasets in a unified training framework. Synthetic data provides ground truth for direct supervision, while real data ensures domain relevance. The model is trained to process both synthetic and real images through the same pipeline, merging the advantages of supervised learning accuracy with real-world generalization capability.
Solution Approach 2:
The patent introduces an intermediary real dataset between synthetic training data and real inference data. This real dataset acts as a bridge, allowing the model to learn from ground truth available in synthetic data while adapting to the visual characteristics and variations present in real-world images, thereby reducing the domain gap.
2Adaptability or versatility
If real datasets are used for training, then models can better generalize to real data, but obtaining real datasets with sufficient size and variety with known ground truth albedo, normal, and lighting information is very challenging and/or prohibitively expensive
Solution Approach 1:
The patent uses synthetic data as a copy or approximation of real data scenarios. By creating synthetic images that mimic real-world lighting conditions, camera perspectives, and scene compositions, the model can be trained on abundant synthetic data with known ground truth, avoiding the need to physically capture expensive real-world datasets with controlled lighting and annotated ground truth.
Solution Approach 2:
The patent varies parameters in synthetic data generation (lighting conditions, camera angles, object positions, material properties) to create diverse training scenarios that cover the range of real-world variations. This allows comprehensive training without needing to physically capture every possible real-world scenario.
3Reliability
If multiple-view images of the same scene are used during training, then reconstruction consistency can be enforced, but the training process becomes more complex and requires additional data collection
Solution Approach 1:
The patent segments the training process into distinct components: single-view decomposition, multi-view reconstruction, and consistency enforcement. Each component is handled separately with specific loss functions and validation metrics, making the complex training process more manageable and systematic rather than attempting to optimize all aspects simultaneously.
Data Source
AI summary
Novel machine learning (ML) models are introduced for image reconstruction training and inference workflows, which are able to estimate intrinsic components of single view images, including albedo, normal, and lighting components. According to some embodiments, such models may be trained on a mix of real and synthetic image datasets. For training on real datasets, both reconstruction and cross-relighting consistency terms may be imposed. The use of a cross-relighting consistency term allows for the use of multiple images of the same scene—although lit under different lighting conditions—to be used during training. At inference time, the model is able to operate on single or multiple images. According to other embodiments, adversarial training (e.g., in the form of a generative adversarial network (GAN)) may optionally be incorporated into the training workflow, e.g., in order to better refine the re-rendered images from the individual lighting and geometric components estimated by the model.