Image Relighting via Conditional Diffusion and Inverse Rendering
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Solution Overview
Problem
Existing image relighting techniques struggle to efficiently generate coherent shadows and skies for outdoor scenes, leading to incoherent lighting effects that do not complement the scene's outline.
Innovation Solution
The proposed solution employs a conditional diffusion model trained with inverse rendering to extract albedo, normal, residual, and lighting channels, which are then used to generate relighted images with coherent sky and shadow portions by manipulating spherical harmonic coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing image relighting techniques are used, then the relighting process can be performed, but the shadows and skies generated are incoherent and do not match the scene's outline
Solution Approach 1:
The patent segments the image into multiple channels (albedo, normal, shadow, sky, residual) through inverse rendering. Each channel represents a specific lighting component that can be independently processed and manipulated, allowing coherent reconstruction of shadows and skies that match the scene geometry.
Solution Approach 2:
The patent manipulates spherical harmonic coefficients to change lighting conditions. By adjusting these parameters in the frequency domain and transforming back to spatial domain, the system generates coherent shadows and skies that are consistent with the scene's geometric outline while achieving desired lighting effects.
2Reliability
If conditional diffusion model with multiple latent features is used, then global coherence of lighting effects is improved, but the computational complexity and training requirements increase
Solution Approach 1:
The patent divides the complex relighting task into multiple specialized neural networks, each processing specific latent features (albedo feature, normal feature, shadow feature, sky feature). This modular architecture manages complexity by assigning specific functions to each network component while achieving global coherence through their integrated output.
Solution Approach 2:
The patent introduces latent features as intermediary representations between the input image channels and the final relighted output. These latent features serve as mediators that capture essential lighting information in a compressed form, enabling the diffusion model to generate coherent lighting effects without directly processing all raw image data.
Data Source
AI summary
Methods and apparatus for relighting images. According to an example embodiment, inverse rendering is applied to an input image to extract a plurality of channels including an original lighting channel. A first neural network is used to determine a first latent feature corresponding to the input image based on a first set of channels including a shading channel generated using a replacement lighting channel. A second neural network is used to determine a second latent feature corresponding to the input image based on a different second set of channels including the replacement lighting channel. A relighted image is generated by propagating samples of a latent image map corresponding to the input image through a conditional diffusion model to which the first and second latent features are applied as first and second conditions.