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About this paper
Sam Sartor, Pieter Peers
MatFusion introduces the first generative diffusion model tailored for Spatially Varying Bidirectional Reflectance Distribution Function (SVBRDF) capture, producing multiple plausible material estimates from a single input image and adapting to diverse lighting conditions. The model achieves high-fidelity material reconstructions and reduces common rendering artifacts by modeling the underlying SVBRDF data distribution.
Key insights
- Generative diffusion models can effectively learn and synthesize complex, high-dimensional SVBRDF data distributions, offering a powerful approach to inverse rendering that addresses ambiguity.
- ConvNeXt blocks within the diffusion U-Net architecture provide a slight performance improvement over traditional residual blocks for SVBRDF data, validating their suitability for higher channel counts.
- A simple "direct conditioning" strategy significantly outperforms ControlNet for fine-tuning pre-trained diffusion models for conditional SVBRDF capture, demonstrating more effective integration with lower memory overhead.
Summary by alphaXiv.
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