A regularization technique that treats diffusion model outputs as a similarity kernel during material optimization in inverse rendering, enabling joint reconstruction of geometry, materials, and illumination that satisfies the rendering equation and generalizes to new lighting.
In: ACM SIG- GRAPH 2024 Conference Papers
5 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
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2026 5representative citing papers
BodyReLux achieves photorealistic, temporally consistent full-body video relighting via a diffusion model with token-based lighting conditioning trained on a hybrid static-dynamic capture dataset.
A Debye CZT-based wave-optics pipeline generates lens-diverse synthetic defocus blur datasets that improve cross-device deblurring generalization over existing real and synthetic data.
DJM constructs compact base meshes for displacement mapping by guiding QEM simplification with a Jacobian lower-bound constraint to ensure bijective low-distortion mappings, outperforming priors on accuracy-size trade-off.
Monte Carlo estimation of volumetric Steklov operators enables robust spectral geometry processing at the scale of hundreds of thousands of in-the-wild meshes and supports contrastive 3D representation learning.
citing papers explorer
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Diffusion-Based Material Regularization for Physics-Based Inverse Rendering
A regularization technique that treats diffusion model outputs as a similarity kernel during material optimization in inverse rendering, enabling joint reconstruction of geometry, materials, and illumination that satisfies the rendering equation and generalizes to new lighting.
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BodyReLux: Temporally Consistent Full-Body Video Relighting
BodyReLux achieves photorealistic, temporally consistent full-body video relighting via a diffusion model with token-based lighting conditioning trained on a hybrid static-dynamic capture dataset.
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Realistic Compound-Lens Defocus Blur Synthesis
A Debye CZT-based wave-optics pipeline generates lens-diverse synthetic defocus blur datasets that improve cross-device deblurring generalization over existing real and synthetic data.
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DJM: Compact Base Meshes for Displacement Mapping using Triangle Jacobians
DJM constructs compact base meshes for displacement mapping by guiding QEM simplification with a Jacobian lower-bound constraint to ensure bijective low-distortion mappings, outperforming priors on accuracy-size trade-off.
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Monte Carlo Steklov Operators for Large-Scale Geometry Processing in the Wild
Monte Carlo estimation of volumetric Steklov operators enables robust spectral geometry processing at the scale of hundreds of thousands of in-the-wild meshes and supports contrastive 3D representation learning.