Machine Learning Image Relighting With Unified Background Generation
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Solution Overview
Problem
Conventional image rendering processes for image relighting are inefficient and expensive due to specialized hardware and software, while conventional machine learning processes require multiple models and suffer from foreground bias, leading to inaccurate and less diverse background generation.
Innovation Solution
The use of a low-rank adaptation (LoRA) layer in an image generation model to adapt weights for relighting tasks, combined with a color transformation function, allows for efficient and accurate generation of relighted images, including both foreground and background, using a single model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image rendering processes are used for image relighting, then specialized hardware and software can achieve rendering tasks, but the process becomes inefficient and expensive
Solution Approach 1:
The patent replaces conventional mechanical image rendering systems with a machine learning-based relighting system. The machine learning model predicts relighted image features directly from input images and prompts, eliminating the need for specialized rendering hardware and software while achieving superior efficiency and accuracy in relighting tasks
2Adaptability or versatility
If multiple machine learning models are used to accomplish relighting and background generation, then task coverage is improved, but system complexity and computational cost increase
Solution Approach 1:
The patent merges relighting and background generation capabilities into a single unified machine learning model. This integrated model simultaneously processes input images and text prompts to generate both relighted foreground objects and corresponding backgrounds, reducing system complexity while maintaining comprehensive task coverage
Solution Approach 2:
The unified machine learning model is designed with multi-functionality to handle both relighting and background generation tasks. The model uses a single architecture with parameters that can be adapted to different tasks through low-rank adaptation, allowing one model to perform multiple functions that previously required separate specialized models
3Manufacturing precision
If conventional machine learning models are used for relighting, then basic relighting can be achieved, but foreground bias leads to inaccurate and less diverse background generation
Solution Approach 1:
The patent applies low-rank adaptation to modify model parameters efficiently for relighting tasks. By adapting weights through low-rank decomposition, the model can accurately capture relighting characteristics while reducing foreground bias. The parameter adaptation allows the model to focus on lighting conditions rather than foreground content, generating more diverse and accurate backgrounds
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image generation includes obtaining an input image and an input prompt, where the input image depicts an object and the input prompt describes a lighting condition for the object, generating relighted image features based on the input image and the input prompt, where the relighted image features represent the object with the lighting condition, and generating a synthetic image based on the relighted image features, where the synthetic image depicts the object with the lighting condition.