REVIEW 18 cited by
UniRelight: Learning Joint Decomposition and Synthesis for Video Relighting
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
UniRelight: Learning Joint Decomposition and Synthesis for Video Relighting
read the original abstract
We address the challenge of relighting a single image or video, a task that demands precise scene intrinsic understanding and high-quality light transport synthesis. Existing end-to-end relighting models are often limited by the scarcity of paired multi-illumination data, restricting their ability to generalize across diverse scenes. Conversely, two-stage pipelines that combine inverse and forward rendering can mitigate data requirements but are susceptible to error accumulation and often fail to produce realistic outputs under complex lighting conditions or with sophisticated materials. In this work, we introduce a general-purpose approach that jointly estimates albedo and synthesizes relit outputs in a single pass, harnessing the generative capabilities of video diffusion models. This joint formulation enhances implicit scene comprehension and facilitates the creation of realistic lighting effects and intricate material interactions, such as shadows, reflections, and transparency. Trained on synthetic multi-illumination data and extensive automatically labeled real-world videos, our model demonstrates strong generalization across diverse domains and surpasses previous methods in both visual fidelity and temporal consistency.
Forward citations
Cited by 18 Pith papers
-
Do Image Editing Models Understand Lighting?
A 1,000-pair real-world HDR benchmark with two new affine-invariant error scores shows the best image-editing models reproduce the relative structure of real light transport but degrade in dim regions, and that VLMs f...
-
Do Image Editing Models Understand Lighting?
New 3DLP benchmark with real-world 1K HDR pairs shows state-of-the-art image editing models vary in physical lighting consistency, with best models close to reality but error-prone in low-light regions.
-
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.
-
Relightable Gaussian Splatting for Virtual Production Using Image-Based Illumination
A relightable Gaussian Splatting method for virtual production decomposes scenes into fixed appearance and variable lighting by parameterizing primitives to directly sample high-resolution background textures, enablin...
-
ID-V2V: Identity-Preserving Video Restylization
ID-V2V restyles video by conditioning a diffusion model on edited keyframes, depth, relit faces, and face normals, so scene edits propagate while facial identity and performance are preserved.
-
Decoupled Illumination Priors for Spatially Controllable Multi-View Indoor Scene Relighting
Lume-Palette decouples multi-view indoor relighting into diffusion-based distillation of canonical illumination palettes and casting under receiver-centric 3D lighting maps with asymmetric multi-view conditioning.
-
HorizonRelight: Relighting Long-horizon Videos Consistently via Diffusion Transformers
A framework for consistent long-horizon video relighting that propagates target latents across chunks and trains continuation via masked target-domain self-conditioning plus warm-start prompting.
-
AlbedoEdit: Unified Instance-Level Video Editing with Albedo Guidance
AlbedoEdit fine-tunes video foundation models to translate RGB videos into edited versions conditioned on user-edited first-frame albedo maps, trained on a new synthetic paired dataset for insertion, removal, and text...
-
RoHIL: Robust Human-in-the-Loop Robotic Reinforcement Learning Against Illumination Variations
RoHIL adapts human-in-the-loop RL policies to new illumination conditions offline by combining world-model image relighting, illumination-retention replay, and anchored Bellman regularisation, improving shifted-light ...
-
PIXLRelight: Controllable Relighting via Intrinsic Conditioning
A transformer-based neural renderer that transfers arbitrary PBR lighting to single images via shared intrinsic conditioning extracted from both multi-illumination photos and path-traced coarse 3D renders.
-
UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors
UniVidX unifies diverse video generation tasks into one conditional diffusion model using stochastic condition masking, decoupled gated LoRAs, and cross-modal self-attention.
-
Toward Real-World Adoption of Portrait Relighting via Hybrid Domain Knowledge Fusion
Hybrid Domain Knowledge Fusion distills expertise from specialized models across synthetic, OLAT, and real datasets into a lightweight student model for state-of-the-art portrait relighting with 6x-240x faster inference.
-
Relighting as a Probe of Visual Priors via Augmented Latent Intrinsics
Semantic encoders can harm relighting, and ALI—fusing dense visual features with latent intrinsics—improves relighting on glossy and specular materials.
-
RenderFlow: Single-Step Neural Rendering via Flow Matching
RenderFlow replaces iterative diffusion with flow matching for deterministic single-step neural rendering that achieves near real-time photorealistic quality and extends to inverse rendering via an adapter module.
-
RelightAnyone: A Generalized Relightable 3D Gaussian Head Model
A two-stage model turns flat-lit photos of a new head into a relightable 3D Gaussian avatar, predicting reflectance parameters without needing one-light-at-a-time captures of that person.
-
TRON: Tracing Rays to Orchestrate a Neural Renderer for 3D Gaussian Reconstructions
Hybrid system that uses ray-traced 3D Gaussians to supply radiometric guidance and material regularization to a neural renderer for editable, realistic output from captured scenes.
-
SRUG: Shadow-Guided Relightable Urban Scene with Generation Model
SRUG uses shadow-guided 3D completion and iterative LMM-based material decomposition to create relightable urban scenes from sparse views.
-
World Simulation with Video Foundation Models for Physical AI
Cosmos-Predict2.5 unifies text-to-world, image-to-world, and video-to-world generation in one model trained on 200M clips with RL post-training, delivering improved quality and control for physical AI.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.