arxiv: 2605.06658 · v1 · submitted 2026-05-07 · 💻 cs.CV

Recognition: unknown

Relit-LiVE: Relight Video by Jointly Learning Environment Video

Weiqing Xiao , Hong Li , Xiuyu Yang , Houyuan Chen , Wenyi Li , Tianqi Liu , Shaocong Xu , Chongjie Ye

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Hao Zhao Beibei Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-08 12:21 UTC · model grok-4.3

classification 💻 cs.CV

keywords videorelightingcamerarelit-liverenderingenvironmentintrinsicnovel

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The pith

Video relighting works by feeding raw reference frames into a diffusion model that jointly predicts the output video and per-frame environment maps.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that video relighting breaks down when it relies on intrinsic decomposition of scenes, because that step often loses or corrupts material and lighting cues in real footage. Instead, the approach inserts the original raw images directly into the process and runs a single diffusion step that outputs both the relit video and a matching sequence of environment maps aligned to each viewpoint. If this holds, it removes the need for known camera poses, produces stable results under moving cameras and changing lights, and opens practical uses such as editing materials or inserting objects into existing videos without accumulated artifacts.

Core claim

Relit-LiVE produces physically consistent, temporally stable video relighting by explicitly introducing raw reference images into the rendering process to recover critical scene cues lost in intrinsic representations, and by formulating environment video prediction that simultaneously generates the relit video and per-frame environment maps aligned with each camera viewpoint in one diffusion process, enforcing geometric-illumination alignment without requiring known per-frame camera poses.

What carries the argument

Joint diffusion prediction of the relit video together with per-frame viewpoint-aligned environment maps, conditioned on raw reference images.

If this is right

Relit videos remain physically consistent under dynamic lighting and arbitrary camera motion.
No explicit per-frame camera pose is required for the relighting process.
The same model supports downstream tasks including material editing, object insertion, and streaming video relighting.
Performance exceeds prior video relighting and neural rendering methods on both synthetic and real benchmarks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The joint-prediction idea could apply to other video tasks that need geometry-lighting consistency, such as video color grading or weather simulation.
Skipping separate decomposition networks may simplify end-to-end pipelines for neural video rendering.
Faster diffusion sampling could turn the method into a practical tool for live video relighting.
pacs
msc
keywords
feed_headline
feed_subtitle

Load-bearing premise

Raw reference images contain enough uncorrupted scene information to let the diffusion model recover accurate cues and maintain alignment even when intrinsic decomposition would fail.

What would settle it

A real-world video sequence with moving camera and complex materials where the relit output shows lighting that contradicts the visible geometry or exhibits temporal flickering.

Figures

Figures reproduced from arXiv: 2605.06658 by Beibei Wang, Chongjie Ye, Hao Zhao, Hong Li, Houyuan Chen, Shaocong Xu, Tianqi Liu, Weiqing Xiao, Wenyi Li, Xiuyu Yang.

**Figure 1.** Figure 1: We present Relit-LiVE, a novel video relighting framework that produces physically consistent and temporally stable results without needing prior knowledge of camera pose. This is achieved by jointly generating relighting videos and environment videos. Additionally, by integrating real-world lighting effects with intrinsic constraints, the relighting videos demonstrate remarkable physical plausibility, sho… view at source ↗

**Figure 2.** Figure 2: Overview of our Relit-LiVE. Given an input video and environment maps of the initial viewpoint, our method jointly predicts relit videos and frame-specific environment maps (i.e., environment video). The input video is converted into intrinsic properties by a pre-trained inverse rendering model, then mapped into latent space alongside environment maps and randomly sampled reference images. Subsequently, la… view at source ↗

**Figure 3.** Figure 3: Overview of intrinsic perception enhancement. Step 1: Generate multi-illumination data. Step 2: Use these multi-illumination data as the raw reference images for training. Step 1 Illumination Consistency (Sec.3.4) Image Init Model Env Light Step 1 Step 2 Our Model Our Model Step 2 Our Model 0.3 Reference image Multi-illuminated reference image “C ycle Consistency” Our Model Source Video Reversed Video Env … view at source ↗

**Figure 4.** Figure 4: Overview of self-supervised learning based on illumination consistency. The symbol (*) denotes inference results for reverse-order video. Dotted line operations do not compute gradients. We relit a video under random environment map and then relit the video in reverse order based on the final frame of generated environment video. These two relit results form a self-supervised training pair. as: znew = zw/ … view at source ↗

**Figure 5.** Figure 5: and supplemental material present corresponding visualizations. Among them, NeuralGaffer fails on scene-level tests, struggling to remove lighting details such as shadows and highlights from the original scene. Diffusion Renderer exhibits distortion on materials, which is particularly noticeable on transparent objects. In contrast, our method outperforms others across all metrics while demonstrating exce… view at source ↗

**Figure 8.** Figure 8: As shown in the middle example of Figure 7, due to the lack view at source ↗

**Figure 6.** Figure 6: Results under dynamic lighting in a dynamic scene. Our method remains stable under simultaneous changes in scene content and illumination view at source ↗

**Figure 7.** Figure 7: Qualitative comparison of video relighting on in-the-wild data. We simultaneously evaluate advanced environment map-based methods and text prompt-based methods, aligning their lighting styles. Our approach outperforms baselines in both relighting quality and physical consistency. S o u r c e Vid e o L AV Dif . R e n d e r e r O u rs T C-Lig ht Env. B rig ht a n d wa r m n atu r al su nlig ht. Frame 1 Frame… view at source ↗

**Figure 8.** Figure 8: Qualitative comparison of video relighting. Our method achieves superior relighting quality, temporal consistency, and photorealistic generation results compared to baseline methods. Effectiveness of raw reference image. We present the quantitative ablation results of raw reference images in view at source ↗

**Figure 9.** Figure 9: Comparison of relighting results for long video sequences using different methods. Light-A-Video and TC-Light process an entire 81-frame video in a single pass. Our approach divides a long video into multiple 57-frame segments, where the lighting conditions for each segment are derived from the lighting estimates of the preceding segment. In addition to the relighting results, our method also displays the … view at source ↗

**Figure 10.** Figure 10: Qualitative comparison of video lighting estimation on the synthetic dataset. Given the environment map of the first frame, our method generates environment maps for every frame of the entire video. It produces smooth lighting deformations and accurately aligns with the camera viewpoint of each frame. We also provide the results of DiffusionLight [Phongthawee et al. 2024]. caused by imperfect intrinsic d… view at source ↗

**Figure 12.** Figure 12: Visualization results of video delighting. Our method achieves realistic and natural lighting removal by resynthesizing video illumination through specific environmental map. Training strategy validation. We conduct a comparative analysis of the design schemes for the three-stage training in view at source ↗

**Figure 13.** Figure 13: Qualitative ablation of relighting. The raw reference image significantly improves relighting quality on complex materials. Input & Tgt Env. with IPE with IPE & SIC Base (Init) Lighting Lighting Lighting view at source ↗

**Figure 14.** Figure 14: Ablation on training strategies. We mark the direction of peak illumination. IPE: Intrinsic Perception Enhancement. SIC: Self-supervised learning based on Illumination Consistency. fact, after the standard supervised training in the first stage, our initial model achieves a PSNR of 21 on the MIT multi-illumination benchmark, surpassing state-of-the-art models. However, this model occasionally struggles wi… view at source ↗

read the original abstract

Recent advances have shown that large-scale video diffusion models can be repurposed as neural renderers by first decomposing videos into intrinsic scene representations and then performing forward rendering under novel illumination. While promising, this paradigm fundamentally relies on accurate intrinsic decomposition, which remains highly unreliable for real-world videos and often leads to distorted appearances, broken materials, and accumulated temporal artifacts during relighting. In this work, we present Relit-LiVE, a novel video relighting framework that produces physically consistent, temporally stable results without requiring prior knowledge of camera pose. Our key insight is to explicitly introduce raw reference images into the rendering process, enabling the model to recover critical scene cues that are inevitably lost or corrupted in intrinsic representations. Furthermore, we propose a novel environment video prediction formulation that simultaneously generates relit videos and per-frame environment maps aligned with each camera viewpoint in a single diffusion process. This joint prediction enforces strong geometric-illumination alignment and naturally supports dynamic lighting and camera motion, significantly improving physical consistency in video relighting while easing the requirement of known per-frame camera pose. Extensive experiments demonstrate that Relit-LiVE consistently outperforms state-of-the-art video relighting and neural rendering methods across synthetic and real-world benchmarks. Beyond relighting, our framework naturally supports a wide range of downstream applications, including scene-level rendering, material editing, object insertion, and streaming video relighting. The Project is available at https://github.com/zhuxing0/Relit-LiVE.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper reframes video relighting as single-pass joint diffusion of relit frames plus per-view environment maps conditioned on raw references, skipping intrinsics and pose.

read the letter

The paper's main point is to do video relighting by jointly learning the relit video and the environment video in a single diffusion process, conditioned on raw reference images rather than decomposed intrinsics. It also drops the requirement for known camera poses. The novelty lies in that joint prediction setup. Earlier approaches first break the video into albedo, normals, and such, then re-render under new lights, but that decomposition often goes wrong on real footage, causing bad materials and flickering. Here, by injecting the original frames directly and having the model output both the relit result and matching environment maps at the same time, they hope the model picks up the missing cues and keeps things consistent geometrically and over time. The abstract indicates this leads to more stable results on benchmarks and opens up uses in editing and streaming. That joint aspect is a solid idea on paper, and avoiding pose estimation is a real advantage for practical use. Still, there are soft spots. The method depends on the diffusion model implicitly capturing the right joint distribution for alignment. Without mentioned explicit losses for multi-view consistency or shadow matching, and no poses provided, it could generate outputs that look good locally but have drifting lighting or incorrect reflections across frames. The stress test concern about plausible but misaligned results seems plausible until more evidence is shown. Also, the reported outperformance would be more convincing with error bars, detailed ablations on the joint component, and analysis of when it still fails. For a reader in computer vision or graphics who deals with video editing or relighting pipelines, this offers a different angle worth considering. It could be adapted even if the full results need checking. I would bring this to a reading group to go over the conditioning and training details. The work shows clear thinking about the limitations of current methods and proposes a targeted change. It deserves peer review to get feedback on the empirical side and to see if the claims hold under scrutiny. Recommend sending it on for review rather than desk rejecting.

Referee Report

2 major / 1 minor

Summary. The paper introduces Relit-LiVE, a video relighting method that conditions a diffusion model on raw reference images and jointly predicts the relit video together with per-frame environment maps in a single denoising process. It claims this avoids unreliable intrinsic decomposition, produces physically consistent and temporally stable results without camera poses, outperforms prior video relighting and neural rendering approaches on synthetic and real benchmarks, and supports downstream tasks such as material editing and object insertion.

Significance. If the joint-prediction formulation reliably recovers scene cues and enforces geometric-illumination alignment, the work could meaningfully advance practical video relighting by reducing dependence on brittle intrinsic representations. The approach of directly feeding raw references and predicting aligned environment videos is conceptually appealing for handling dynamic lighting and camera motion, but its advantage over existing diffusion-based renderers remains to be demonstrated with rigorous controls.

major comments (2)

[Abstract] Abstract: the central claim that the single-pass joint diffusion 'enforces strong geometric-illumination alignment' rests on the assumption that the learned joint distribution will produce view-consistent environment maps and shadows without explicit multi-view consistency losses, pose conditioning, or light-transport constraints; the manuscript provides no mechanism or ablation showing why this holds when intrinsic decomposition already fails (e.g., specular surfaces or moving shadows).
[Abstract] Abstract: reported outperformance on synthetic and real benchmarks is stated without quantitative metrics, error bars, ablation tables, or failure-case analysis, preventing assessment of whether the gains are statistically meaningful or limited to easy cases.

minor comments (1)

[Abstract] The abstract mentions 'extensive experiments' and a GitHub link but does not indicate whether code, models, or evaluation protocols will be released with sufficient detail for reproduction.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and indicate the revisions we will incorporate.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that the single-pass joint diffusion 'enforces strong geometric-illumination alignment' rests on the assumption that the learned joint distribution will produce view-consistent environment maps and shadows without explicit multi-view consistency losses, pose conditioning, or light-transport constraints; the manuscript provides no mechanism or ablation showing why this holds when intrinsic decomposition already fails (e.g., specular surfaces or moving shadows).

Authors: We appreciate the referee highlighting the need for explicit justification of the alignment. Section 3.2 details how the joint denoising process operates on a shared latent space for the relit video and viewpoint-aligned environment video, with both conditioned on the same raw reference frames; this allows the model to learn direct correlations between observed geometry, appearance, and illumination without separate decomposition steps. To strengthen the evidence, we will add an ablation comparing joint versus decoupled prediction and include qualitative results on specular surfaces and moving shadows in the revised manuscript. revision: partial
Referee: [Abstract] Abstract: reported outperformance on synthetic and real benchmarks is stated without quantitative metrics, error bars, ablation tables, or failure-case analysis, preventing assessment of whether the gains are statistically meaningful or limited to easy cases.

Authors: We agree the abstract should better convey the experimental rigor. The full manuscript reports quantitative results using PSNR, SSIM, and LPIPS on synthetic and real benchmarks (Section 4), with error bars from repeated runs, ablation tables (Section 4.3), and failure-case analysis in the supplementary material. We will revise the abstract to summarize key metric improvements and explicitly reference these sections and tables. revision: yes

Circularity Check

0 steps flagged

No significant circularity; new conditioning and joint objective are independently specified

full rationale

The paper's central claims rest on an explicit architectural choice (feeding raw reference images) and a training objective (joint diffusion of relit video plus per-frame environment maps) whose benefit is asserted via external benchmark comparisons rather than any closed-form reduction to the inputs. No equations are presented that equate a derived quantity to a fitted parameter by construction, no uniqueness theorem is imported from prior self-work, and no ansatz is smuggled via citation. The derivation chain is therefore self-contained: the model is trained to minimize a joint denoising loss on the proposed outputs, and success is measured outside the training distribution.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on the assumption that pre-trained video diffusion models can be repurposed as neural renderers and that joint prediction of video plus environment maps supplies sufficient supervisory signal for physical consistency. No new physical entities are postulated.

axioms (2)

domain assumption Large-scale video diffusion models can be repurposed as neural renderers via intrinsic decomposition followed by forward rendering.
Stated in the first sentence of the abstract as the starting point for the new method.
domain assumption Raw reference images contain recoverable scene cues that intrinsic representations lose.
Central premise for introducing raw references.

pith-pipeline@v0.9.0 · 5590 in / 1271 out tokens · 26542 ms · 2026-05-08T12:21:18.834121+00:00 · methodology

discussion (0)

Reference graph

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