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.
arXiv preprint arXiv:2501.18590 (2025)
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 6verdicts
UNVERDICTED 6roles
background 1polarities
background 1representative citing papers
A kinematic-to-visual lifting paradigm combined with hierarchically routed control generates action-conditioned surgical videos with better faithfulness, fidelity, and efficiency.
Materialist performs single-image inverse rendering via neural-initialized progressive differentiable rendering to enable physically consistent material editing, object insertion, relighting, and transparency edits without full scene geometry.
Ouroboros uses two single-step diffusion models with cycle consistency for forward and inverse rendering, extending intrinsic decomposition to indoor/outdoor scenes with faster inference than multi-step methods.
A diffusion framework decomposes images into intrinsic maps via an inverse renderer and renders controllable weather changes via a forward renderer with CLIP prompt interpolation and map-aware attention, outperforming pixel-space baselines on new 38k synthetic and 18k real datasets.
LGAA is a modular adapter framework that lifts multi-view diffusion models to produce 2D Gaussian Splats with PBR channels for high-quality relightable 3D mesh extraction using data-efficient finetuning on 69k instances.
citing papers explorer
-
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.
-
From Articulated Kinematics to Routed Visual Control for Action-Conditioned Surgical Video Generation
A kinematic-to-visual lifting paradigm combined with hierarchically routed control generates action-conditioned surgical videos with better faithfulness, fidelity, and efficiency.
-
Materialist: Physically Based Editing Using Single-Image Inverse Rendering
Materialist performs single-image inverse rendering via neural-initialized progressive differentiable rendering to enable physically consistent material editing, object insertion, relighting, and transparency edits without full scene geometry.
-
Ouroboros: Single-step Diffusion Models for Cycle-consistent Forward and Inverse Rendering
Ouroboros uses two single-step diffusion models with cycle consistency for forward and inverse rendering, extending intrinsic decomposition to indoor/outdoor scenes with faster inference than multi-step methods.
-
IntrinsicWeather: Controllable Weather Editing in Intrinsic Space
A diffusion framework decomposes images into intrinsic maps via an inverse renderer and renders controllable weather changes via a forward renderer with CLIP prompt interpolation and map-aware attention, outperforming pixel-space baselines on new 38k synthetic and 18k real datasets.
-
DreamLifting: A Plug-in Module Lifting MV Diffusion Models for 3D Asset Generation
LGAA is a modular adapter framework that lifts multi-view diffusion models to produce 2D Gaussian Splats with PBR channels for high-quality relightable 3D mesh extraction using data-efficient finetuning on 69k instances.