Prompt-Based Image Relighting With Fidelity-Preserving Backgrounds
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Solution Overview
Problem
Conventional image editing techniques face issues with image fidelity, user and computational efficiency, and realism when applying lighting and background changes to digital objects, often resulting in mismatched lighting conditions and distorted details.
Innovation Solution
A processing device uses a content processing system to extract digital objects, apply lighting conditions through a diffusion model, restore content details with histogram matching, and generate backgrounds based on specified conditions, while considering the lighting effects of the relit object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual image editing techniques are used to apply visual effects and alter backgrounds, then image editing functionality is achieved, but the process becomes time-consuming and requires expert knowledge
Solution Approach 1:
The system enables automatic image editing through prompt-based instructions, allowing the editing system to perform complex operations autonomously without requiring expert manual intervention. The AI model automatically understands and executes editing tasks based on simple text prompts.
Solution Approach 2:
Manual editing operations are replaced with an automated AI-based image processing system that uses machine learning models to perform editing tasks. This substitution transforms the mechanical manual editing process into an automated computational process.
2Extent of automation
If machine learning approaches are applied to automate image editing tasks, then automation is improved, but issues arise with image fidelity, user efficiency, and image realism
Solution Approach 1:
The automated editing process is divided into distinct stages: background removal, relighting with diffusion models, histogram matching for detail restoration, and background generation. This segmentation allows each stage to be optimized independently while maintaining overall image fidelity.
Solution Approach 2:
A lighting example image is generated as an intermediary between the original image and the final edited result. This intermediary contains the desired lighting conditions and is used to guide the relighting process through histogram matching, ensuring both automation and fidelity.
3Extent of automation
If conventional machine learning approaches are used for image editing, then automation is achieved, but computational resources are excessively consumed
Solution Approach 1:
The system applies relighting and editing operations selectively to specific regions of the image, particularly focusing on the foreground object and its immediate surroundings. This partial action approach reduces unnecessary computational processing compared to applying operations to the entire image.
Solution Approach 2:
The background is removed and the foreground object is isolated before applying relighting operations. This preliminary action simplifies subsequent processing by focusing computational resources only on the relevant object rather than the entire scene.
4Illumination intensity
If automated relighting is applied to digital objects, then lighting effects are improved, but content details and fine structures may be distorted or lost
Solution Approach 1:
A lighting example image serves as an intermediary that captures the desired lighting conditions without containing the original content details. This intermediary is then used to guide the relighting process while preserving the original content structure through histogram matching.
Solution Approach 2:
The system applies different processing qualities to different parts of the image: the lighting conditions are transformed to match the example image, while the content details and fine structures are preserved through histogram matching that maintains the original image's local characteristics.
5Reliability
If backgrounds are generated based on lighting conditions, then realism is improved, but the complexity of coordinating lighting and background increases
Solution Approach 1:
The background generation process uses the lighting conditions as input parameters to generate a coherent background that matches the relit foreground object. By treating lighting conditions as controllable parameters, the system coordinates lighting and background generation in a systematic manner.
Solution Approach 2:
The complex task of generating a realistic background is separated into distinct steps: first generating the background based on lighting conditions, then compositing it with the relit foreground object. This segmentation makes the overall process more manageable and controllable.
Data Source
AI summary
Techniques for prompt-based image relighting and editing are described that support automatic generation of an edited digital image with high-fidelity and realistic lighting effects and background features. A processing device, for instance, receives as input a digital image that depicts a digital object, a lighting prompt, and a background prompt. The processing device generates a relit digital object that has a lighting condition specified by the lighting prompt applied to the digital object. The processing device further generates a background that includes a feature specified by the background prompt and the lighting condition. The processing device generates an edited digital object for output that includes the relit digital object and the background. The processing device further leverages a shadow synthesis model to edit shadows in the edited digital image. In this way, the techniques described herein preserve content details of the digital object when applying background and lighting effects.