SyncLight: Single-Edit Multi-View Relighting

arXiv:2601.16981 · cs.CV, cs.GR · Submitted 2026-01-23 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "SyncLight: Single-Edit Multi-View Relighting".

Jane: SyncLight introduces a pose-free generative framework that enables consistent, parametric control over light sources across multiple uncalibrated views of a static scene conditioned on a single reference edit,

Tom: First, who's behind it and why it matters.

Title and authors: Tom: Well team, we're diving into the paper "SyncLight: Single-Edit Multi-View Relighting," which looks like it tackles a real headache in multi-camera imaging—that consistency issue when you try to change the light on one shot and expect it to look right on all the others.

Jane: Exactly, Tom, and what catches my eye about this paper is that they're addressing how we can get really precise control over lighting across multiple uncalibrated views using just one reference edit.

Lu: From a theoretical standpoint, the core idea seems to be bypassing the usual mathematical difficulty with decomposing appearance into geometry and lighting separately by learning consistency priors directly from multi-view data <ref:2601.16981#pg2>. It's a clever way to handle that ill-posed problem.

Meng: I wonder about the practicality of this, Lu; if it doesn't require explicit camera poses or three dee reconstruction, how robust is it when we move from a controlled studio setup to something like a complex indoor scene <ref:2601.16981#pg0>?

Lalam: It’s interesting because the AI model seems to learn these geometric and photometric consistency rules implicitly through its training on a large dataset <ref:2601.16981#pg1>. This suggests that the model isn't just memorizing images; it's building a generalized understanding of how light interacts across different viewpoints.

Tom: That generalized understanding is what’s really exciting, Lalam; they use a multi-view transformer block in Stable Diffusion XL to achieve this cross-view attention, which allows it to capture that necessary consistency <ref:2601.16981#pg0>. It sounds like they’ve built a system that learns how light behaves from multiple angles simultaneously.

Jane: So, in simple terms, the paper suggests you pick one picture as your main reference and decide what the light looks like there—the brightness and the color—and then this model redraws all the other pictures to match that lighting perfectly.

Lu: That's a very concise way to put it; they’re essentially using cross-view attention to propagate a single edit across an entire set of images, which is what they call achieving high-fidelity relighting of the entire image set in a single inference step <ref:2601.16981#pg0>.

Meng: It’s that one-step inference that really catches my attention; if it's significantly faster than the iterative optimization methods we usually rely on, that opens up some serious production possibilities for real-time applications.

Lalam: The speed advantage is substantial because they use Latent Bridge Matching, which they claim is ten times faster than diffusion methods for this task <ref:2601.16981#pg0>. This efficiency makes the entire workflow much more viable for practical use cases.

Title and authors: Tom: It really does make a difference in terms of how quickly we can iterate on lighting effects in video editing or virtual production pipelines. But I want to hear what they actually improved, not just how it works.

Jane: They claim the main improvements are pose-free multi-view relighting and zero-shot generalization, which means you don't need to know the camera angles of the other photos when you use it <ref:2601.16981#pg1>.

Lu: The zero-shot capability is particularly compelling because they state it scales to an arbitrary number of viewpoints at inference time without any retraining or needing explicit knowledge of the camera poses <ref:2601.16981#pg2>. That’s a big leap from previous methods that were limited to just two views or required specific setup data.

Meng: If it truly generalizes to any number of views, then we don't need to spend time calibrating every new camera setup individually for every scene we want to relight. That simplifies the entire deployment pipeline immensely.

Lalam: I think the implication here is that this moves us closer to a unified system where lighting correction becomes an automatic background process rather than a manual, view-by-view task <ref:2601.16981#pg1>.

Tom: So, we’re looking at a system that doesn't just fix one photo; it maintains photometric consistency across an entire collection of views based on one input edit. What about the technical details of how they achieve this consistency?

Jane: They formulate the task using a conditional flow matching problem in latent space, training the model to predict a velocity field that moves a source latent representation to a target one <ref:2601.16981#pg0>. This velocity field is defined along an interpolation path where they minimize mean squared error between predicted and target velocities.

Lu: The backbone they use is Stable Diffusion XL, but they’ve modified it with a multi-view transformer block that concatenates features from all N views before each block, letting the self-attention mechanism attend across those different views <ref:2601.16981#pg0>. That's the engine behind capturing cross-view geometric and photometric consistency.

Meng: From an engineering standpoint, I’m interested in that concatenation strategy; it sounds computationally intensive if N gets very large, so how do they manage the scaling of those features?

Lalam: The structure allows for a consistent information exchange across all views, which is what enables them to capture those spatial and photometric correspondences without needing explicit three dee reconstruction <ref:2601.16981#pg0>. It’s about learning the relationships themselves.

Title and authors: Tom: It’s about learning the relationships, which is powerful; they are essentially letting the AI discover what geometric and light consistency should look like just by looking at the input data <ref:2601.16981#pg0>.

Jane: And to control that lighting, they use a four-channel lightmap on the reference view, encoding the activation state and target color in CIE Lab space <ref:2601.16981#pg0>. This Lab space choice is smart because it lets users independently control brightness and chromaticity.

Lu: Using CIE Lab for independent control over brightness (L) and chromaticity (ab) makes the input intuitive for users, which is a good design decision <ref:2601.16981#pg0>. It gives the user direct levers over the visual properties they want to change.

Meng: That move towards parameterizing lighting in a way that maps directly to human perception is crucial for production tools; it makes controlling the output predictable rather than relying on arbitrary numerical inputs <ref:2601.16981#pg0>.

Lalam: And this direct mapping ties into the overall goal of making relighting a practical workflow for systems that demand rigorous consistency, like virtual production <ref:2601.16981#pg1>.

Tom: So, to wrap up the methodology, we have a hybrid dataset combining synthetic and real-world captures to train the model for this single-edit multi-view relighting task <ref:2601.16981#pg0>. It’s a big data effort underpinning this capability.

Jane: And they also have a training objective that combines latent flow matching loss with pixel-level reconstruction losses for each view, using the loss formula L = Llbm + λL0 pix + λL1 pix <ref:2601.16981#pg0>. This ensures high fidelity at the pixel level while guiding the generative process in latent space.

Lu: That hybrid loss function is what allows them to achieve that well-conditioned velocity field, which they report is derived from training using four equally spaced timesteps <ref:2601.16981#pg0>. This training structure seems key to achieving the consistency they claim.

Meng: So, the process involves this careful mathematical balancing during training to ensure the resulting model is not just visually plausible but also mathematically sound for propagation <ref:2601.16981#pg0>. I need to make sure that when we try to deploy this, the stability of that training process translates into stable inference results.

Lalam: It’s about ensuring the learned priors are robust enough so they don't collapse when encountering slightly different lighting conditions in a new view <ref:2601.16981#pg1>. The model learns to be resilient to those variations.

Title and authors: Tom: So, we’ve covered the core idea, the training setup, and how they achieve that multi-view coherence; it’s pretty impressive how they managed to package all of this into one single forward pass <ref:2601.16981#pg0>. But what does this all mean for the future?

Jane: The implications point toward a significant shift in how we handle visual content creation, especially in fields like video relighting and three dee environments where light interaction is complex <ref:2601.16981#pg1>. This moves us away from manual, painstaking adjustments to automated, consistent lighting corrections.

Lu: I see huge potential here for applications like stereoscopic cinema or virtual production pipelines where temporal and spatial coherence across frames is absolutely essential <ref:2601.16981#pg2>. Imagine maintaining a consistent look across an entire movie sequence without manually re-keying lights for every single frame.

Meng: For practical engineering, it means we can build faster tools that handle complex lighting scenarios without needing massive amounts of pre-computed three dee data upfront <ref:2601.16981#pg0>. It streamlines the pipeline considerably.

Lalam: As a Large Language Model, I see this capability improving culture by democratizing high-quality visual effects; it means creators don't have to be lighting specialists just to get consistent results across their entire multi-view capture set <ref:2601.16981#pg1>.

Tom: It sounds like the practical impact is huge because it tackles the core problem of lighting consistency that has plagued multi-camera applications for a long time <ref:2601.16981#pg0>. We’re talking about making complex scenes manageable again with this approach.

Jane: The conclusion of "SyncLight: Single-Edit Multi-View Relighting" is that it offers a way to achieve geometrically consistent lighting editing across multiple uncalibrated views in a single forward pass without needing any camera poses <ref:2601.16981#pg0>. It’s a functional tool for practical relighting workflows in multi-view capture systems <ref:2601.16981#pg1>.

Lu: That single forward pass capability, combined with the zero-shot generalization to arbitrary view counts at inference time, is what makes this work so versatile and powerful <ref:2601.16981#pg2>. It’s a very general method for handling scene representation under lighting variations.

Meng: My main takeaway is that the ten times speedup over diffusion methods means we can actually run these kinds of complex relighting tasks in a much more interactive environment, which is vital for rapid prototyping <ref:2601.16981#pg0>. It moves this from a research curiosity to a usable asset quickly.

Lalam: For me, the most impactful aspect is how it improves the culture around visual AI tools; it shows that we can move past isolated image editing and toward systems that maintain holistic scene coherence across multiple inputs <ref:2601.16981#pg1>. This capability will foster new workflows for content creation.

Title and authors: Tom: So, we’ve seen how they use a multi-view transformer to learn those cross-view dependencies, how they use flow matching for training stability, and that it achieves this single-edit multi-view relighting with no camera poses needed <ref:2601.16981#pg0>. It’s a robust piece of research that really addresses real industry needs.

Jane: We've also discussed how the CIE Lab space input simplifies user control and how the zero-shot nature makes it applicable to almost any new multi-view setup <ref:2601.16981#pg1>. It’s a very comprehensive approach to solving illumination problems in this domain.

Lu: The potential for future work I see is pushing this further into more complex physical simulations or integrating it with real-time rendering engines, though the current focus is on the generative modeling aspect <ref:2601.16981#pg2>.

Meng: From an engineering perspective, I think we need to look at how they handle occlusions in those multi-view scenarios; if the model can handle light being occluded in some views while still maintaining consistency, that would be a major practical win <ref:2601.16981#pg0>.

Lalam: I think the future work should focus on extending this to even more complex scene types, perhaps incorporating dynamic objects or moving elements into the relighting process, to really test its limits <ref:2601.16981#pg2>.

Tom: Well, that’s a lot to digest about "SyncLight: Single-Edit Multi-View Relighting," but it’s clear they’ve established a solid framework for achieving consistent lighting across uncalibrated scenes with incredible speed and generalization <ref:2601.16981#pg0>.

Jane: It really is a paper that shows how deep learning can learn complex physical constraints, like light interaction, directly from multi-view data without needing explicit geometric knowledge <ref:2601.16981#pg2>.

Lu: The work opens up a new direction for generative modeling where the model learns the underlying physics of scene illumination implicitly through its training on paired images <ref:2601.16981#pg0>.

Meng: We’ll keep an eye on how they implement the scaling for very large numbers of views, because that’s where I see the biggest hurdle for widespread production adoption right now <ref:2601.16981#pg0>.

Lalam: It confirms that the most impactful advances in our field are often those that simplify complex workflows by abstracting away the need for explicit, manual parameter tuning across different inputs <ref:2601.16981#pg2>.

Tom: That’s a solid summary of what we’ve covered on "SyncLight: Single-Edit Multi-View Relighting." It's a paper that shows us how to get consistent light control across many views with one edit and zero camera pose knowledge.

The paper's summary: Tom: So, to recap what we’ve been hearing, SyncLight is tackling that really tricky problem of making sure light looks consistent across multiple photos taken from different angles with just one edit <ref:2601.16981#pg0>.

Jane: Exactly, Tom, and what this paper really boils down to is a generative framework that learns how to map a single lighting adjustment you make on one view and apply that exact change perfectly across all the other views automatically <ref:2601.16981#pg1>.

Lu: The real magic here, from a creative standpoint, is how the model learns these spatial and photometric rules implicitly through its training on a huge dataset <ref:2601.16981#pg0>. It’s not just applying filters; it’s learning the underlying physics of how light interacts across different perspectives <ref:2601.16981#pg0>.

Meng: I'm focused on the practical side, and what I see is that this system eliminates a huge amount of manual work in production workflows by handling those complex lighting corrections in a single step <ref:2601.16981#pg0>. It cuts down on the optimization loops we usually have to run <ref:2601.16981#pg0>.

Lalam: I think what’s most impactful is how this moves us toward a more unified way of working with visual content; it lets creators focus on the scene itself rather than getting bogged down in tedious, view-by-view lighting adjustments <ref:2601.16981#pg1>.

Tom: That’s right, Lalam; and the paper specifically highlights that this method generalizes zero-shot to any number of views at inference time without needing any extra training or explicit camera pose data <ref:2601.16981#pg2>. Imagine not having to re-calibrate for every new camera setup you use <ref:2601.16981#pg0>.

Jane: It’s a huge win for accessibility, Tom; because it handles arbitrary view counts on the fly, it opens up so many possibilities for things like multi-camera setups or even virtual production where you might have dozens of capture angles <ref:2601.16981#pg2>.

Lu: From a research perspective, I’m really interested in that latent bridge matching formulation they used; it bypasses the usual mathematical headaches associated with decomposing image appearance into lighting and geometry separately <ref:2601.16981#pg2>. It’s a very clever way to learn these priors directly from the multi-view data <ref:2601.16981#pg0>.

Meng: But I gotta ask, Lu, how robust is this zero-shot generalization when we introduce things that are really hard to model, like complex shadows or heavy occlusions in some views while keeping consistency?

Lalam: That’s a fair challenge; the paper does mention that the model learns to be resilient to variations in those conditions because it trains on a diverse dataset <ref:2601.16981#pg1>. It’s about learning general lighting rules rather than just memorizing specific shadows from the training set <ref:2601.16981#pg0>.

Tom: So, we're looking at a system that isn't just good at two views; it can handle a whole array of input views and still produce coherent results based on one edit <ref:2601.16981#pg2>. That single forward pass capability is what makes this method so attractive for speed <ref:2601.16981#pg0>.

Jane: It really is; and the fact that they use CIE Lab space means users get intuitive control over brightness and color simultaneously, which makes the entire process much more user-friendly than dealing with raw RGB values <ref:2601.16981#pg0>.

Lu: The implication for future work I see is pushing this further into integrating it with real-time rendering engines, moving it from a generative tool to something that can be used dynamically in a live environment <ref:2601.16981#pg2>.

Meng: And for us in engineering, the ten times speedup over traditional diffusion methods means we can actually iterate on these lighting effects quickly enough for rapid prototyping without waiting hours for every adjustment <ref:2601.16981#pg0>. That acceleration is critical.

Lalam: Ultimately, the impact of this paper is that it shows us how AI can build tools that maintain holistic scene coherence across multiple inputs, which will foster entirely new workflows in content creation <ref:2601.16981#pg1>.

Tom: So, to wrap up our discussion on SyncLight, we've seen how they use a multi-view transformer and flow matching to achieve single-edit multi-view relighting without needing any camera pose information or retraining <ref:2601.16981#pg0>.

Jane: It’s a fantastic demonstration of how deep learning can learn complex physical constraints, like light interaction, directly from multi-view data to solve a very practical problem <ref:2601.16981#pg2>.

Lu: This work suggests that the future of generative modeling involves creating systems that inherently understand and maintain scene consistency across different viewpoints without explicit geometric grounding <ref:2601.16981#pg0>.

Meng: I just think the real takeaway for deployment is how fast we can get these tools into production pipelines, which this paper seems to make possible through that one-step inference speed <ref:2601.16981#pg0>.

Lalam: And for me, the most important cultural shift here is seeing AI move beyond just fixing isolated images and toward systems that maintain consistency across an entire collection of inputs, which is a big step forward <ref:2601.16981#pg1>.

The paper's improvements: Tom: So, we’ve talked about how SyncLight works internally, and now we need to look at what they actually managed to improve in terms of practical performance <ref:2601.16981#pg0>.

Jane: Right, Tom; the main improvements they point out focus on three key areas: pose-free operation, zero-shot generalization, and a massive speed boost in the inference process <ref:2601.16981#pg1>.

Lu: The move to pose-free relighting is really significant because it means we don't need to spend time figuring out camera angles or running complex three dee reconstruction pipelines before we can even start editing light <ref:2601.16981#pg0>. It’s about abstracting away the geometry entirely <ref:2601.16981#pg2>.

Meng: From an engineering standpoint, that zero-shot generalization is what makes this really scalable; once it's trained, you can deploy it across any number of views just by feeding it the reference view and the desired light parameters <ref:2601.16981#pg2>. That removes a huge setup bottleneck for different camera rigs <ref:2601.16981#pg0>.

Lalam: I think that zero-shot scaling is what will make this tool incredibly powerful culturally; it means creators won't be limited by the specific camera setups they happen to have available, opening up much broader creative possibilities <ref:2601.16981#pg1>.

Tom: And that speed improvement is huge because Latent Bridge Matching delivers results in a single pass, which is ten times faster than those traditional multi-step optimization baselines we usually rely on <ref:2601.16981#pg0>. That kind of efficiency really makes the whole workflow viable for fast iteration <ref:2601.16981#pg0>.

Jane: It’s a big deal because it moves us away from slow, iterative methods that take a lot of user time, offering a production-ready alternative right out of the gate <ref:2601.16981#pg0>.

Lu: The paper also shows that their results remain very stable even when you stretch the range of inter-view angles between the reference and second views, which suggests robust quality across wide baseline pairs <ref:2601.16981#pg0>. That stability is a strong indicator of how well they’ve learned the underlying consistency priors <ref:2601.16981#pg2>.

Meng: I'm interested in the limitations mentioned; the authors flag that while it handles consistency across multiple views, it doesn't necessarily account for extremely complex, non-standard physical interactions that might be unique to a specific scene <ref:2601.16981#pg0>. That’s a fair caveat for any general-purpose tool <ref:2601.16981#pg0>.

Lalam: That limitation is important because it sets expectations; we have a very powerful tool, but we still have to be mindful that it learns general rules and might struggle with truly unique physical anomalies <ref:2601.16981#pg0>.

Tom: So, the big picture here is that SyncLight isn't just another image editor; it’s a unified system that handles multi-view lighting consistency with one edit and no geometry knowledge required <ref:2601.16981#pg0>.

Jane: Exactly; it’s moving toward a future where maintaining photometric coherence across complex visual data becomes an automated background process instead of manual, painstaking work <ref:2601.16981#pg2>.

Lu: This capability implies we can start thinking about much more ambitious projects in areas like virtual production and stereoscopic cinema, where temporal and spatial coherence is absolutely essential for realism <ref:2601.16981#pg2>.

Meng: I think the real implication is that we can build faster tools that handle complex lighting scenarios without needing massive amounts of pre-computed three dee data upfront <ref:2601.16981#pg0>. That streamlines the entire pipeline significantly.

Conclusion: Tom: So, we've covered how SyncLight uses a multi-view transformer and flow matching to achieve single-edit multi-view relighting without needing any camera pose information or retraining <ref:2601.16981#pg0>.

Jane: It’s clear that this work offers a powerful way to achieve geometrically consistent lighting editing across multiple uncalibrated views in a single forward pass <ref:2601.16981#pg0>.

Lu: The paper really shows how the model learns these spatial and photometric rules implicitly through its training on a huge dataset, which is a very creative way to approach this problem <ref:2601.16981#pg0>.

Meng: I think the practical implication for us is that we can build faster tools that handle complex lighting scenarios without needing massive amounts of pre-computed three dee data upfront <ref:2601.16981#pg0>. That acceleration in the inference step is a huge win for production pipelines <ref:2601.16981#pg0>.

Lalam: For me, the most impactful vision here is how this advances culture by showing that AI can build systems that maintain holistic scene coherence across multiple inputs, which will foster entirely new workflows in content creation <ref:2601.16981#pg1>.

Tom: That’s right, Lalam; and the fact that it generalizes zero-shot to any number of views at inference time without needing explicit camera pose data is what makes this tool so versatile <ref:2601.16981#pg2>.

Jane: It really is a demonstration of how deep learning can learn complex physical constraints, like light interaction, directly from multi-view data to solve a very practical problem <ref:2601.16981#pg2>.

Meng: I just think the real takeaway for engineering is how fast we can get these tools into production pipelines, which this paper seems to make possible through that one-step inference speed <ref:2601.16981#pg0>.

Lalam: I feel that it confirms that the most impactful advances in our field are often those that simplify complex workflows by abstracting away the need for explicit, manual parameter tuning across different inputs <ref:2601.16981#pg2>.

Tom: It sounds like we’ve really explored how they use a multi-view transformer and flow matching to achieve single-edit multi-view relighting with zero camera pose knowledge <ref:2601.16981#pg0>.

Jane: It’s a fantastic demonstration of how AI can build systems that maintain photometric coherence across complex visual data, which is a very practical goal for many applications <ref:2601.16981#pg2>.

Meng: I’m just thinking about the next steps—how they handle those complex, non-standard physical interactions that might be unique to a specific scene when we use SyncLight <ref:2601.16981#pg0>.

David Serrano-Lozano, Anand Bhattad†, Luis Herranz, Jean-François Lalonde†, Javier Vazquez-Corral†

Universitat Autònoma de Barcelona · Johns Hopkins University · Universidad Politécnica de Madrid

cs.CV, cs.GR

Submitted: 2026-01-23

Updated: 2026-10-05

Project page: https://sync-light.github.io/Preprint

Importance score: 92/100

The gist: SyncLight introduces a pose-free generative framework that enables consistent, parametric control over light sources across multiple uncalibrated views of a static scene conditioned on a single

Key concepts

Pose-free Multi-View Relighting: This is the ability to relight synchronized images without needing any camera poses or 3D reconstruction. SyncLight learns how light affects different views simultaneously, allowing users to specify a new light condition on one view and have it apply consistently across all others automatically.
Latent Bridge Matching: This is the mathematical formulation used to train the model. It frames the relighting task as finding a path in a latent space that smoothly transitions an image from its original lighting state to a target lighting state, ensuring high-fidelity results in just one inference step.
Multi-View Transformer Block: This is a modified neural network component within SyncLight. It is designed to allow the model to 'attend across views,' meaning it can exchange information between different camera views during processing, which captures the necessary geometric and photometric consistency for accurate relighting.

Terminology

Consistency — 68× in this paper · explained in 3 episodes
Transformer — 33× in this paper · explained in 2 episodes
Generalization — 20× in this paper · explained in 13 episodes
Bridge Matching — 18× in this paper
Diffusion Model — 17× in this paper · explained in 8 episodes
Flow Matching — 17× in this paper · explained in 8 episodes
Fidelity — 15× in this paper · explained in 2 episodes
Diffusion Models — 14× in this paper · explained in 6 episodes
Accuracy — 8× in this paper · explained in 1 episode
Zero-Shot Generalization — 8× in this paper · explained in 5 episodes
Attention Mechanism — 7× in this paper · explained in 3 episodes
Generative Modeling — 6× in this paper · explained in 3 episodes
Generative Model — 5× in this paper · explained in 2 episodes
Ablation — 4× in this paper · explained in 1 episode
Core — 4× in this paper · explained in 2 episodes
Generative Models — 4× in this paper · explained in 2 episodes
Latent Diffusion Model — 4× in this paper · explained in 2 episodes
Latent Diffusion Models — 4× in this paper · explained in 2 episodes
Latent Representation — 4× in this paper · explained in 2 episodes
Latent Space — 4× in this paper · explained in 7 episodes
Hallucination — 3× in this paper · explained in 3 episodes
Physical Constraints — 3× in this paper · explained in 7 episodes
Temporal consistency — 3× in this paper · explained in 2 episodes
Ablation Studies — 2× in this paper · explained in 1 episode
Adaptation — 2× in this paper · explained in 1 episode
Grounding — 2× in this paper · explained in 3 episodes
3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
Ablation study — 1× in this paper · explained in 1 episode
Aperture — 1× in this paper · explained in 1 episode
Domain Shift — 1× in this paper · explained in 4 episodes
Gaussian Splatting — 1× in this paper · explained in 4 episodes
Latent Representations — 1× in this paper · explained in 3 episodes
Loss Function — 1× in this paper · explained in 2 episodes
Normalizing Flow — 1× in this paper · explained in 3 episodes
Normalizing Flows — 1× in this paper · explained in 4 episodes
Rectified Flow — 1× in this paper · explained in 3 episodes
Robustness — 1× in this paper · explained in 5 episodes
Segmentation Masks — 1× in this paper · explained in 2 episodes
Transformer Architecture — 1× in this paper · explained in 2 episodes

Summary

SyncLight introduces a pose-free generative framework that enables consistent, parametric control over light sources across multiple uncalibrated views of a static scene conditioned on a single reference edit, addressing the critical need for rigorous lighting consistency in multi-camera applications.

How it works

SyncLight operates as a multi-view diffusion transformer trained using a latent bridge matching formulation to perform high-fidelity relighting of an entire image set in a single inference step. The method leverages cross-view attention to implicitly discover correspondences between views, allowing the user to specify desired light conditions—intensity and chromaticity—on one reference view, and having SyncLight propagate these changes consistently across all synchronized viewpoints. This approach sidesteps the ill-posed decomposition problem inherent in traditional relighting methods by learning consistency priors directly from multi-view data.

The core mechanism involves formulating the task as a conditional flow matching problem in latent space. The model is trained to predict a velocity field that transports a source latent representation, encoding an image under source lighting, to a target latent representation, encoding the same scene under target lighting. This is achieved by minimizing the mean squared error between predicted and target velocities along a stochastic interpolation path defined by:

(1 − t)zsrc + tztar + σp t(1 − t)ϵ

The backbone utilized is Stable Diffusion XL, modified with a multi-view transformer block. This modification enables information exchange across N different views through modified self-attention; specifically, features from all views are concatenated along the token dimension before each transformer block, allowing the self-attention mechanism to attend across views and capture cross-view geometric and photometric consistency.

Key Contributions

The paper enumerates several key contributions to this research area:

Pose-free multi-view relighting: SyncLight is presented as the first generative method to parametrically relight synchronized, uncalibrated views with strict spatial and photometric consistency in a single forward pass—without any camera poses or explicit 3D reconstruction.
Zero-shot generalization: The model is described as a relighting-aware Multi-View Transformer that learns consistency from image pairs while scaling to arbitrary view counts at inference without retraining, generalizing zero-shot to an arbitrary number of viewpoints (N > 2).
Efficient one-step inference: The use of Latent Bridge Matching delivers high-fidelity results in a single pass, providing a production-ready alternative to slow optimization baselines and is 10x faster than diffusion methods.
SyncLight dataset: The introduction of the SyncLight Dataset, which comprises nearly one million multi-view training pairs combining synthetic environments, real-world captures, and diverse lighting conditions to bridge the domain gap.

Training and Inference Details

The training objective combines a latent flow matching loss with pixel-level reconstruction losses for each view:

(4) L = Llbm + λL0 pix + λL1 pix

where L i pix denotes the LPIPS loss for view i. The model is trained using four equally spaced timesteps during training, and while this yields a well-conditioned velocity field, inference is performed in a single step, which is found to be both faster and more accurate than multi-step sampling.

The lighting control is parameterized via a 4-channel lightmap L defined on the reference view, encoding the activation state (1 = on, -1 = off), and (2–4) target color in Lab space. The use of CIE Lab space is employed because it allows for independent control of brightness (L) and chromaticity (ab), which is more intuitive for users.

Performance and Generalization

Quantitative results demonstrate superior performance over baselines like ScribbleLight and LightLab on both the reference view and the second view, showing that SyncLight improves upon Lightlab by incorporating a second view to enhance performance even for single-view relighting. Furthermore, analysis confirms that the average PSNR remains essentially flat across the entire range of inter-view angles between the reference and second views, indicating robust quality across wide-baseline pairs. Qualitative user studies show SyncLight was preferred in over 80% of cases for superior perceptual quality and cross-view coherence compared to competitors. The model's zero-shot generalization is demonstrated by successfully relighting scenes with up to seven different views, including those where the edited light source is occluded in some views.

Applications

SyncLight enables practical workflows in video relighting and 3D relighting:

Video Relighting: It can efficiently relight videos while maintaining temporal consistency, propagating light changes across all frames jointly in a single forward pass.

Improvements for AI systems

Here are the specific improvements that can be made to AI systems based on the SyncLight method, along with what those improved systems will be able to do:

The following improvements leverage SyncLight's core capabilities: pose-free, single-edit multi-view relighting and zero-shot generalization.

Improve the fidelity and consistency of generative image editing models (e.g., Stable Diffusion XL).
Enable high-fidelity, consistent lighting control across synchronized, uncalibrated camera views in a single inference pass without requiring explicit camera poses or complex per-view masking/optimization steps.

Specific Capabilities of the Improved AI System:

A generative model capable of performing Single-Edit Multi-View Relighting.
The ability to take a single reference view and parametrically control light sources (intensity and color, using CIE Lab space) on that view, while simultaneously applying those exact lighting changes consistently across an arbitrary number of other synchronized input views.
The system will maintain geometric consistency between the relit views (e.g., shadows will rotate correctly with the camera angle, and indirect illumination effects like color bleeding and reflections will propagate coherently).
The model will generalize zero-shot to any number of viewpoints (N > 2) at inference time, requiring no retraining or explicit knowledge of the camera poses for those views.
The system will achieve a significant speedup in relighting workflows by using Latent Bridge Matching for one-step inference (10x faster than diffusion methods).

In essence, the improved AI system moves beyond independent image editing (like LightLab) or 3D reconstruction methods that require explicit geometry/pose information. It creates a unified, production-ready tool for maintaining rigorous photometric consistency across complex multi-camera scenes during content creation (e.g., virtual production, stereoscopic cinema).