Direct Shading Control for Consistent Scene Re-Lighting
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Solution Overview
Problem
Conventional image re-lighting methods struggle with uncontrollable and inconsistent results, especially when altering lighting conditions without a reference image, and models trained on synthetic scenes suffer from domain gaps and implausible-looking images.
Innovation Solution
An image generation model with a direct shading model and a lighting control network is used to extract normal and shading maps, which guide a pre-trained image generator to produce realistic images with controlled lighting, leveraging a guidance signal to maintain the ability to generate detailed and faithful depictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image re-lighting methods are used to alter lighting conditions, then lighting adjustment is achieved, but the results are uncontrollable and inconsistent
Solution Approach 1:
The patent introduces a reference image as an intermediary element that mediates between the original image and the re-lighting result. The reference image contains ground truth lighting information that guides the transformation process, ensuring consistent and controllable results. The system uses the reference image to extract lighting direction and shading information, which then serves as a mediator to transform the original image's lighting conditions reliably.
2Adaptability or versatility
If models are trained on synthetic scenes to enable re-lighting, then re-lighting functionality is achieved, but domain gaps cause implausible-looking images
Solution Approach 1:
The patent performs preliminary action by training the model on synthetic scenes with ground truth lighting information before deployment. During training, the system pre-computes accurate shading and normal maps from 3D models, establishing a strong foundation of lighting physics. This preliminary training on controlled synthetic data enables the model to generalize well to real images, bridging the domain gap and producing plausible results without requiring extensive real-world training data.
Solution Approach 2:
The patent uses 3D model copies to generate training data. Synthetic 3D models are rendered to create training image pairs with known lighting conditions. These copied 3D representations serve as a bridge between synthetic training and real application, allowing the model to learn lighting transformations from idealized copies that can be systematically varied to cover diverse lighting scenarios.
3Adaptability or versatility
If traditional illumination adjustment methods are used, then existing lighting conditions can be altered, but new lighting conditions cannot be introduced
Solution Approach 1:
The patent uses a reference image as an intermediary that encodes the desired new lighting conditions. Instead of directly transforming lighting, the system first extracts lighting information from the reference image, then uses this extracted information as a mediator to guide the transformation of the original image. This intermediary approach enables introduction of arbitrary new lighting conditions while maintaining system manageability through the structured reference image framework.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, apparatus, and system for scene re-lighting using direct shading control include obtaining an input image and a lighting direction indicator that describes a lighting direction. A direct shading map is generated based on the input image and the lighting direction indicator and a shaded image is generated depicting an object from the input image with shading consistent with the lighting direction based on the shading map.