A Diffusion Approach To Radiance Field Relighting Using Multi Illumination Synthesis


A Diffusion Approach To Radiance Field Relighting Using Multi Illumination Synthesis
Overview :
This method involves creating relightable radiance fields by leveraging priors extracted from 2D image diffusion models. The approach can convert multi-view data captured under single lighting conditions into a dataset with multi-illumination effects, represented through 3D Gaussian splats for relightable radiance fields. This method does not rely on precise geometric shapes and surface normals, making it well-suited for handling cluttered scenes with complex geometries and reflective BRDFs.
Target Users :
This technology is targeted towards researchers and developers in the field of computer graphics, particularly those specializing in image processing, 3D modeling, and visual effects. It offers an innovative method to handle and enhance the lighting effects of 3D scenes, which is crucial for creating realistic visuals and animations.
Use Cases
Used in film production to create realistic 3D scene lighting effects
Enhancing visual effects in virtual reality and game development
Simulating the appearance of architectural designs under different lighting conditions in architectural visualization
Features
Enhance single illumination data with multiple illuminations using a 2D diffusion model
A 2D relighting neural network that allows direct control over light direction
Create relightable radiance fields that consider inaccuracies in synthetic relighting input images
Force multi-view consistency by optimizing auxiliary feature vectors for each image
Control low-frequency illumination by parameterizing light direction with a multilayer perceptron
Demonstrate advantages in handling complex scenes compared to techniques such as Outcast, Relightable 3D Gaussians, and TensoIR
How to Use
Step 1: Prepare a multi-view dataset under single illumination conditions
Step 2: Use a 2D diffusion model to enhance the dataset with multiple illuminations
Step 3: Train a 2D relighting neural network using the enhanced dataset
Step 4: Apply the trained network to single illumination data to generate a multi-illumination dataset
Step 5: Create a radiance field represented by 3D Gaussian splats from the multi-illumination dataset
Step 6: Ensure multi-view consistency by optimizing auxiliary feature vectors for each image
Step 7: Achieve direct control of low-frequency illumination by parameterizing light direction with a multilayer perceptron
Step 8: Apply the final radiance field to the target scene for relighting
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