IntrinSync is a unified framework that jointly decomposes an RGB image into albedo, shading, surface normal, roughness, and metallic maps through a 1-to-N mapping, allowing information exchange across channels. It also establishes inverse-forward reciprocity via a dual cycle-consistent objective that aligns predictions across a closed loop, improving coherence and physical consistency. The method demonstrates competitive performance on three datasets and offers a physically grounded interface for image editing by manipulating intrinsic properties and rendering them back into RGB images.
arXiv:2512.23667v3 Announce Type: replace Abstract: Intrinsic image decomposition aims to recover material and illumination factors from RGB observations, but real-world images entangle reflectance w...
arXiv:2602.18741v3 Announce Type: replace-cross Abstract: Spectral rendering reproduces the wavelength-dependent appearance that RGB rendering cannot: metamerism, colour shifts under spectrally rich...
arXiv:2608.29925v1 Announce Type: new Abstract: Controllable image relighting is an important problem in image editing, and hand-drawn scribbles provide an intuitive interface for specifying the desi...
GLOW is a Global Illumination‑aware inverse rendering framework for indoor scenes captured with dynamic co‑located light and camera setups. It combines a neural implicit surface representation with a neural radiance cache to jointly optimize geometry and reflectance, while introducing a dynamic radiance cache and a surface‑angle‑weighted radiometric loss to handle near‑field motion, strong inter‑reflections, and specular highlights. Experiments demonstrate that GLOW significantly outperforms prior methods in estimating material reflectance under both natural and co‑located illumination.
GS-PI introduces an optimization‑decoupled framework that transforms Gaussian Splatting (GS) assets into physically based rendering (PBR) compatible Gaussian assets. By treating PBR material generation as a geometry‑conditioned diffusion process on 3D point clouds, it achieves multi‑view consistency and avoids the pixel‑correspondence problems of 2D diffusion. The method employs a multi‑scale cross‑view conditioning mechanism—combining global semantic priors, photometric cues, and spatial view‑direction signals—to prevent specular highlights from baking into intrinsic colors, and then distills the predicted attributes back into a fully relightable PBR‑GS asset without requiring proxy meshes.
Recent advances in neural scene representations enable photorealistic novel-view synthesis, yet most methods remain tightly coupled to a single rendering paradigm, limiting their versatility and integration with conventional graphics workflows. We introduce Floating Radiance Networks (FlaRe), a neural scene representation combining explicit ray-traceable geometry with continuous neural radiance functions.
arXiv:2407.00500v2 Announce Type: replace-cross Abstract: Recent point-based intrinsic decomposition and inverse rendering methods have advanced the modelling of the shading and albedo of 3D scenes....
arXiv:2610.19847v1 Announce Type: new Abstract: We address inverse rendering with 3D Gaussian Splatting (3DGS) by introducing explicit visibility modeling for relighting as well as material and light...
arXiv:2606. 31637v1 Announce Type: cross Abstract: Intrinsic decomposition which expresses image colors as the product of diffuse albedo and shading, possibly augmented with view-dependent residuals has a long history in image editing as it enables the modification of object colors and textures without altering lighting.
RelightFormer is a feed‑forward generative Transformer that performs single‑ and multi‑view image relighting without explicit intrinsic property estimation. It incorporates a latent illumination module that injects target environment maps into spatial features via cross‑attention, and uses permutation‑invariant positional encodings to process unordered multi‑view inputs symmetrically. Trained on the large Laval Objaverse Dataset, the model achieves state‑of‑the‑art visual and photorealistic relighting quality, and demonstrates strong zero‑shot generalization across various relighting tasks.
arXiv:2608.23549v1 Announce Type: new Abstract: Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces...
arXiv:2607.08016v3 Announce Type: replace Abstract: Video relighting requires balancing long-form temporal consistency with a physically grounded understanding of light transport, which depends on ac...