CroCoDiLight: Repurposing Cross-View Completion Encoders for Relighting
Authors: Alistair J Foggin
William A P Smith
Venue: ICLR 2026 Poster (2026)
Keywords: cross-view completion
relighting
intrinsic image estimation
albedo estimation
shadow removal
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: Disentangle CroCo latents into lighting and scene intrinsics, edit lighting for shadow removal, albedo estimation, relighting and lighting interpolation.

Abstract: Cross-view completion (CroCo) has proven effective as pre-training for geometric downstream tasks such as stereo depth, optical flow, and point cloud prediction. In this paper we show that it also learns photometric understanding due to training pairs with differing illumination. We propose a method to disentangle CroCo latent representations into a single latent vector representing illumination and patch-wise latent vectors representing intrinsic properties of the scene. To do so, we use self-supervised cross-lighting and intrinsic consistency losses on a dataset two orders of magnitude smaller than that used to train CroCo. This comprises pixel-wise aligned, paired images under different illumination. We further show that the lighting latent can be used and manipulated for tasks such as interpolation between lighting conditions, shadow removal, and albedo estimation. This clearly demonstrates the feasibility of using cross-view completion as pre-training for photometric downstream tasks where training data is more limited.

Published as a conference paper at ICLR 2026
CROCODILIGHT: REPURPOSING CROSS- VIEW COMPLETION ENCODERS FOR RELIGHTING
Alistair Foggin' & William A P Smith 'Department of Computer Science, University of York 2pxld.ai (alistair. foggii,, william. smith] ac.
ABSTRACT
Cross-view completion (CroCo) has proven effective as pre-training for geomet- ric downstream tasks such as stereo depth, optical flow, and point cloud predic- tion. In this paper we show that it also learns photometric understanding due to training pairs with differing illumination. We propose method to disentangle CroCo latent representations into a single latent vector representing illumination and patch-wise latent vectors representing intrinsic properties of the scene. To do so, we use self-supervised cross-lighting and intrinsic consistency losses on a dataset two orders of magnitude smaller than that used train CroCo. This com- prises pixel-wise aligned, paired images under different illumination. We further show that the lighting latent can be used and manipulated for tasks such interpo- lation between lighting conditions, shadow removal, and albedo estimation. This clearly demonstrates the feasibility of using cross-view completion as pre-training for photometric downstream tasks where training data is more limited. Project
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INTRODUCTION
Cross-view completion (CroCo) (Weinzaepfel al. 2022 2023) has recently emerged as a promis- ing pre-training proxy task for downstream problems in 3D geometric vision. The CroCo objective to complete missing patches in an image given second, overlapping view of the same scene, taken from different viewpoint (see Fig. [ left). In order to solve the task, the model must use cross attention to implicitly reason about correspondence, relative pose and depth in order to cross- project content from the complete to the masked image. CroCo is able to plausibly complete the missing patches while being trained on uncontrolled image pair collections with varying illumina- tions, suggesting that the model not only geometrically cross-projects but also relights the scene.
The hypothesis underlying our work (see Fig. D right) that the CroCo encoder must implicitly estimate illumination and encode it in the patch embeddings. The cross-view decoder then removes lighting from the second view (i.e. delights the scene contents), geometrically cross- projeccss and then applies the lighting estimated from the observed patches in the masked image (i.e. relights). Crucially, CroCo's training objective is unique among self-supervised vision models in requiring implicit relighting capabilities. While other pretrained vision models learn rich visual representa- tions, none are explicitly trained to handle the photometric transformations that CroCo encounters during its cross-view completion task. However, CroCo's oss patch embeddings contain this knowledge.
To test our hypothesis, we propose a model in which latent patch embeddings from the CroCo en- coder are explicitly disentangled into a single latent vector representing lighting and 2. lighting invariant per-patch latents representing static scene information (i.c. geometry and materials). Then a second model recombines a given lighting latent with local intrinsic latents back into the CroCo la- tent domain. We propose method to train these two networks using pairs of images from the same view with different lighting and the same structure. general, such fixed view/varying lighting im- ages are harder to acquire than the uncontrolled pairs required for training CroCo. We demonstrate that this disentanglement can be learnt with datasets two orders of magnitude smaller than CroCo's o's original training, suggesting that the underlying photometric understanding is already present and requires only extraction rather than learning from scratch. Finally, we train a single view CroCo de- coder to transform CroCo latent patches back into RGB space with high fidelity which can be done

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CroCo
Implicit relighting in CroCo
Cooaa
CrsCo Decodar
Eghning
Full gitiigg
Enoeder
Egtsiing
Frooder
projection
Figure I: Left: data flow through CroCo. Right: the relighting that we hypothesise CroCo must implicitly perform when presented with a pair of images of the same scene but with different lighting. In order to predict a masked patch (shown with "?"), the target illumination must be estimated from the unmasked patches (green). Patches containing the same scene content (blue) must be delit using the source lighting estimated from the second view (purple) and relit (orange) using the estimated illumination. We show how to make this disentanglement explicit.
with any arbitrary image dataset. We name our method CroCoDiLight (Cross-view Completion for Disentangling Lighting).
Once trained, we demonstrate the use of our model for applications of lighting interpolation (tempo- ral upsampling of timelapse videos) and relighting (timelapse illumination normalisation). We show that the learnt disentangling convincingly captures shading effects, cast shadows, coloured lighting, specularities and even local lighting. Going further, show how to learn translations of the lighting latent to enable single image shadow removal and albedo estimation. We evaluate these translations on standard benchmarks and show performance competitive with the current state-of-the-art.
RELATED WORK
CroCo (Weinzaepfel et al. 2022 2023) demonstrated the effectiveness of cross-view completion as pre-training objective for purely geometric downstream tasks. The cross-attention decoder was fine-tuned for stereo depth and optical flow models. Subsequently, DUST3R (Wang et al.) 2024) and MAST3R (Leroy et al. 2024) expanded upon this directly predict 31) point cloud using unposed frames. Our hypothesis is that the encoder from CroCo pre-training has also learnt lighting information from the scene, enabling photometric tasks such as relighting, shadow removal, and intrinsic image decomposition. Our work is similar in spirit to other efforts to repurpose foundation models for tasks where believed the original training objective means the model must already implicitly solve the task. For example, Kerssies et al. (2025) show that Vision Transformers implic- itly learn segmentation, while generative image models have been shown to learn monocular depth estimation (Ke et al.] 2024), complete intrinsic image decomposition (Ke et al. 2025) and zero-shot classification (Li et al. 2023a).
Relighting When it comes to relighting images, there are few main approaches. HDR panoramas can be used to condition changes to the lighting of an image as done in UniRelight (He et al. 2025). Text conditioning also possible
models such as Neural Gaffer (Tin et al. (024) and IC- LC-Light
(Zhngg et al. 2025). This is most effective in outdoor scenes or for relighting individual objects where the global lighting can vary according to the aforementioned inputs. Other methods based on generative models can better handle local image dynamics with indoor lighting. An example of this is LightLab (Magar et al.) 2025)) which enables detailed control of the colour and intensity of individual lights. LumiNet (Xing et al. 2025) predicts lighting from reference image and encodes it to a latent vector which used to relight another image. All of these approaches enable relighting, often for perceptually good generation of new lighting from conditioning, but our method aims at developing a general-purpor model to disentangle the real lighting of an image in order to carry out lighting interpolation and transfer, along with more clearly defined tasks.
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Intrinsic Image Decomposition Beyond directly relighting images, many methods such Ordi- nal Shading (Careaga & Aksoy (003)) and Lossless Intrinsic Image Decomposition (Sha et al. 2025) tackle the task of intrinsic image decomposition into albedo and shading images. These two images can be easily recombined to produce the original image. Other methods such as Diffusionnenderrr
(Liang et al. 2025) separate and recombine images into further components such as ambient occlu- sion, normals, and depth. However, the primary challenge for all of these methods is training data. Synthetic datasets such as CGIntrinsics (Li & Snavely 2018 and ML-Hypersim (Roberts et al.) 2021) render out dense decompositions but still leave the gap to real-world images. The Multi- Illumination dataset (Murmann et al., 2019) is a collection of fixed-camera real- world scenes, each with 25 controlled lighting conditions. BigTime (Li & Snavely (018a) on the other hand is a col- lection of in-the-wild timelapses with varying illumination conditions. While these provide training data, they don't t provide direct supervision. Intrinsic Images in the Wild (IIW) (Bell et al. (2014) and Shading Annotations in the Wild (SAW) (Kovacs et al., 2017) provide sparse annotations of lighting conditions for real-world images along with corresponding evaluation benchmarks.
Shadow Removal The task of shadow removal is challenging as shadows vary in their sharpness and in the way that they cast. They can be cast from occluders outside of the image, or self- occlusion from objects within the image. Datasets provide supervision for these various shadow types. These include SRD (Qu et al. 2017), WSRD (Vasluianu et al., 2023). and ISTD (Wang al., 2018) which provide image pairs of shadowed and shadow-free images that can be used for direct supervision. With these datasets, there are issues in colour and pixel alignment. To help tackle this, WSRD+ (Vasluianu et al. 2024) and ISTD+ (Le & Samaras, 2019) are modified versions which help fix those issues. Many current methods use input mask to remove a specific shadow as done in HomoFormer (Xiao et al. 2024) and ShadowFormet (Guo et al. (023), whereas other methods such OmniSR (Xu al. 2025a) remove shadows from the image directly without a mask.
3 RELIGHTING IN CROCO LATENT SPACE
Our method begins by encoding images with the pretrained (and frozen) CroCo v2 encoder (Wein- zaepfel et al. 2023). An input image X E #xxx is decomposed into set of N non-overlapping patches, each of size P x P. such that the patches cover the entire frame, i.c. = HW/ pa, The flattened to vectors, X2 R with p 1...N NN representing spatial position and K = 32 and embedded to the dimensionality D = 1024 used throughout the transformer models.
RGB patches
The CroCo v2 encoder operates on patches of size P = 16, applies RoPE positional embeddings
Su et al. 2024) to the patches and encodes with transformer:
2 Exx),
where patchify(X) = (xi........ .x.....,> XN) and [Zi,.... ZN with z; E R.D. The CroCo encoder was originally trained on images of resolution H W 224. However, since RoPE can generalise to patch coordinates beyond the positions seen during training, we operate resolution H = W = 448 throughout our method, similar to how DUST3R (Wang et al.] (024) scaled up resolution during training and used a sliding window approach for scaling up further.
Our approach (see Fig. 2 starts with a delighting transformer which disentangles illumination from scene intrinsic properties by translating the patch latents into intrinsic patches and estimating a light- ing latent vector that describes the appearance in that particular illumination environment. Second, relighting transformer which recombines lighting latent vector with intrinsic patches producing patch embeddings in the original CroCo latent space. Finally, to ensure high quality image synthesis we train single view decoder transform from CroCo latent space back RGB images.
3.1 SINGLE VIEW DECODER
The original CroCo decoder was trained for the difficult task of using cross-attention to predict masked patches in an image from a second view. As the decoder is binocular, the only way to decode single view is to remove masking and use the same encoded image as if it were the second view. It was also trained with supervision only on the reconstruction of masked patches, not the whole image. This means that the image quality is low when decoding single view (Appendix B)
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Intrinsic Petches
A
CroCo Encoder
I
R
D
ABA
CroCo Patches
Enerrinsic
Swap Lighting
X
CroCo Encoder
I
R
D
XAE
Lighting
Eeross light
Figure 2: The architecture of the model comprises four main components. First is the frozen CroCo encoder. Last is the decoder D which is separately pre-trained and then frozen to decode from CroCo latent space RGB. Then there are the delighting and relighting transformers, I and respectively, which disentangle lighting and intrinsics before recombining them. The training process here shows pairs of images encoded and relit to match the lighting of the other image.
Our downstream applications of relighting, shadow removal and albedo estimation all operate on single images and require high quality image output. We therefore train coder with an autoencoder objective: i.c. we train the decoder to reconstruct original images from embeddings given by the frozen CroCo encoder. Specifically, we train a decoder:
single view de-
X' DD(2,,
which uses self-attention with DPT head (Ranftl
2021 for reconstruction. The self-attention module consists of 12 layers each with 16 heads. Every layer is followed by a 2-layer MLP with hidden layer size of 2D. To ensure high-fidelity reconstruction, we train the decoder using combination of perceptual (LPIPS (Zhang et al. 2018) and mean squared error (MSE) losses:
Lima (X) XLLLISS (X, X') (1 CMSE (X, (X,,
where we use A = 0.5 throughout. This objective does not require paired images so can be trained on any image dataset, for which we use ImageNet (Deng al., 2009; Russakovsky et al. 2015).
3.2 DELIGHTING TRANSFORMER
In order to estimate illumination, we append learnable query vector 2n E RRP to the CroCo patch latent embeddings: & (Zo..... 3N}. We pass this augmented to the delighting transformer to disentangle lighting:
I=Z),
where $ (Su.....,.) SN). Here, So contains lighting information for the whole image while (Si..... Sv contains intrinsic information about the original image patches, i.c. with the effect of lighting removed. The architecture of closely follows the architecture of the CroCo encoder. It comprises 8 consecutive self-attention blocks, each with 16 heads. Every self-attention block is followed by 2-layer MLP with hidden size double the dimensions of a single patch. Each of the patches retains the same RoPE positional encoding used by the CroCo encoder. As the self-attention blocks expect positional encoding for every input patch, when we append Zp, we give it a unique positional encoding for the position
encoded patch will have that position.
3.3 RELIGHTING TRANSFORMER
Next, we train a relighting transformer that recombines a lighting latent vector with the intrinsic patch embeddings return the CroCo latent space:
(5) where 2' - (z/ 2'x We discard 2/ and retain only the patch embeddings in CroCo latent space, z' - [z!...., ,,yy Note that the input lighting latent vector, So, need not be from the original image but could be extracted from another image for the purposes of relighting. The architecture of R comprises an identical architecture to that of the delighting transformer, I, but only keeps z' as the predicted CroCo latent patch embeddings to be decoded.
á' = R(s).
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3.4 PAIRED IMAGE TRAINING
We now explain how I and R can be trained using paired images. While CroCo used pairs of images of the same scene from different viewpoints (possibly with different lighting), we use pairs of pixel-aligned images from the same viewpoint where the illumination, and therefore appearance, different. This provides two forms of supervision.
First, the two images XA A and X3 are encoded with the CroCo encoder and then disentangled by the delighting transformer into lighting latents, S/ and sy and patch-wise intrinsic latent embeddings, s and s Since we expect the intrinsic latent embeddings to represent static aspects of the scene such as geometry and materials, we use an MSE loss to encourage consistency between the intrinsic patches from the two images:
Lintrinsic
N S
Σ
(6)
Second, we can impose a cross-lighting constraint. Namely, we can relight the intrinsic patches from image A with the lighting estimate from image B:
XAA D(R(( si..... .N).))))))).. NN),
and similarly using the lighting from B with the intrinsic patches from to produce X'B.A. This allows us to define a cross-lighting loss:
L light = Limg (Xm, X B.A) + (i,,(XX, XAA
We train the delighting and relighting modules using weighted sum of the above two losses. To- gether, they force the delighting transformer to bottleneck lighting-related aspects appearance through the lighting latent vector while making the intrinsic patch embeddings invariant illu- mination. Mcanwhile, the relighting transformer is encouraged to combine lighting and intrinsic information such that decoded images closely match the originals.
3.5 SLIDING WINDOW HIGH-RESOLUTION RELIGHTING
In order to generalise the outputs of the model to high-resolution images, each image must be split into multiple overlapping 448 x 448 tiles that are fed into the model batch, relit with a lighting latent per-tile, and merged together by blending the overlapping pixels. While the RoPE embeddings do enable resolution-agnostcc self-attention, the lighting latent has been trained to compress the lighting information of the specified resolution. This means that despite the intrinsic latent vectors potentially being accurate for arbitrarily sized images, the lighting latent vector is only meaningful at the specified resolution. The sliding window ensures that the lighting latent is optimally used.
DATASET
Training the lighting modules requires pixel-aligned, paired images with illumination variation. In general, this type of data is challenging to collect, but as long as this requirement met, many differ- ent datasets can be incorporated in training, whether not illumination and shadows are controlled.
Uncontrolled lighting For uncontrolled illumination and shading, BigTime (Li & Snavely 2018a) Sa)) set of in-the-wild timelapses from static cameras. contains both indoor and outdoor scenes and helps models have a more general understanding of lighting. Despite its limited size, it provides valuable information and can be scaled up as static timelapses are often captured and put online. These can easily be incorporated into the training process. The main restriction in expanding this type of dataset is ensuring that the timelapses collected retain consistent structure, as often they are of the sky or of crowds of people, both of which have changing content beyond just lighting.
Controlled lighting The Multi- multi-llluiination dataset (Murmann et al. 2019) consists of around 1000 scenes each with set 25 controlled lighting conditions. All the scenes indoor but contain a variety of challenging materials to model, including transparent and highly reflective objects. From these datasets, kept the
test-set aside for evaluation in our experiments,
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and used the rest in training. For single epoch through these datasets, every single image was included once, and a random pair from the same scene was randomly selected every iteration. While this does mean that several images per scene may be seen multiple times in epoch, it is still relighting it to different illumination condition and it guarantees that every image is seen.
Beyond just arbitrary pairs of various lighting and exposures, albedo estimation and shadow removal datasets were beneficial. Three shadow removal datasets, SRD, ISTD+, and WSRD+ were included in training using pairs of shadowed and shadow-free images with which lighting was swapped. Synthetic image pairs of rendered and albedo images were included to encourage further extraction of shading information into the lighting latent, particular ML-Hyyperim and CGIntrinsics. The ML-Hypersim dataset includes series of images following a camera trajectory within scene, but for the purposes of this paper and reducing overlapping images, only the first frame of each camera trajectory was included. Overall, these datasets form 57k image pairs (36k real and 21 synthetic) used in training, which is two orders of magnitude less than the required data for CroCo v2 Weinzaepffl al. 2023) which used 2M synthetic pairs and 5M real pairs.
Augmentation order to help prevent over- wver-fitting to the training images, data augmentation was used. The primary augmentation random 448 448 crops of the original images. This meant that different sections of the image are seen every epoch, and that it has seen partial images which helps it generalise various resolutions when using sliding window. We also incorporated random conversion to greyscale to highlight differences between shadows and colour variation. In general, any augmentation that preserves the pixel-wise alignment possible.
MANIPULATIONS IN THE LIGHTING LATENT SPACE
The cross-lighting training described above disentangles intrinsic patch latent vectors from a single dynamic lighting latent vector. This opens up the possibility of editing images by manipulation within the lighting latent space. Given the aligned nature of the paired training images, the lighting latent explains lighting effects in image space as opposed to world space (see Appendix C. To further explore how works, several downstream tasks were explored.
5.1 LIGHTING INTERPOLATION AND RELIGHTING
The most obvious lighting manipulation is to relight images via interpolation between different lighting conditions. Given that the latent vector works in image space, the examples explored are
Figure 3: A series of time-lapse photos of a glacier with varying lighting conditions relit to match the lighting condition of the first image. The top row demonstrates transferring the lighting of the first image while still showing the intrinsic changes. The second row is of the original images, and final row of the magnified colour-coded patches.

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Figure 4: A series of images demonstrating capabilities of the latent-space in changing shadows and specular highlights. This is done with linear interpolation between two input lighting conditions.
using fixed camera positions with pixel-wise alignment. The main use of this manipulating the lighting in timelapses and other fixed-view sets of images with various lighting conditions.
Lighting stabilísation in timelapse One challenge with timelapses is that the lighting can change drastically from photo to photo, meaning it can be more challenging to see what intrinsic components of the underlying scene are changing. To tackle this, lighting conditions (c.g. well exposed, ambient as opposed to saturated) at the start, extract the lighting latent, and apply that lighting latent to all subsequent images in the timelapse shown in Figure (and see supplementary video). This provides consistent lighting throughout, with only the structure contained in the intrinsic latent patches changing.
take timelapse which has desirable
Temporal timelapse upsampling Other components of the lighting latent vector shadows and specularities. To better understand these components, the lighting latent vectors were extracted from various keyframes that have distinet lighting conditions. We then linearly interpolated between these latent vectors to compare with direct interpolation in RGB space. Figure/4demonstran which shadows, specularities, and lighting are embedded in the latent space. While the motion the shadows is not entirely smooth, it demonstrates that all the correct lighting conditions exist within that space and the trajectory can be learnt. We provide additional interpolation and relighting results in Appendix D. This lighting interpolation enables temporal upsampling to generate intermediate frames. This allows for extending the length of such timelapses quantitative results using FIoLPIPS (Danier et al.) 2022) in Table I in which we interpolate halfway between every 7th frame and evaluate the temporal perceptual quality.
the extent to
post-prrouction. We show some
Clock Shadows
Day-night Cycle
Indoor Shadows
Interpolation Method
FloLPIPS +
FloLPIPS
FloLPIPS J
Image Space
0.309
0.041
0.950
Latent Space (Ours)
0.286
0.043
0.923
Table A quantitative evaluation of temporal timelapse upsampling. Every 7 frames were sampled, and interpolated to halfway between the two, either in image space, or in lighting latent space. The triplet of ground truth frames on either side, and the interpolated frame compared the reference frame was evaluated using FIoLPIPS. These results were averaged across each timelapse. As we are testing lighting latent-space interpolation, intrinsics are fixed for each triplet, which means that any structural dynamics are detrimental as image-space interpolation can smooth those dynamics.
5.2 LEARNING LIGHTING LATENT TRANSFORMATIONS
Beyond lighting interpolation, temporal upsampling, and lighting stabilisation, more concrete down- stream tasks also exist to which our model be applied. As have seen, lighting and shad- ows can be manipulated using latent vector interpolation. Transformations latent space exist for shadow removal and albedo reflectance estimation which models can be trained to learn.

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Training
Xer
I
LMSE
Inference
X
CroCo Encoder
I
A/S
R
D
X"
Figure 5: Diagram describing the training process for both the shadow removal and albedo estima- tion components S and A to learn the transformations in the lighting latent space. We distinguish between which steps are only done during training and which are done in training and inference. We also show that all the components apart from S/A have frozen weights during training.
Shadow Removal For the task of shadow removal, a model S matching the architecture of R and initialised by R's weights was trained to map from a shadowed latent So to a shadow-free latent s while using the intrinsic latent patches s to guide the process. The difference to R. in processing s is that we keep the output z as s/ and discard z' now called s' (see S in Figure 5) The output latent then appended the original intrinsic latents to produce &' {s(, $1,- ... SN which can be subsequently re-entangled using R and decoded. The loss function is mean squared error (MSE) in the lighting latent space to match the encoded lighting latent from the ground truth shadow- image. At this point, only S was being trained. All the other components had frozen weights. The training process shown in Figure This was supervised on the training splits of the SRD, ISTD+, and WSRD+ datasets. We also compared the model to using optimal shadow-free latent which we extract from the shadow-fiee free image and apply it to the intrinsic latents the shadowed latent. While in practice, this does work as the shadow-free image is not available, it demonstrates that the mapping exists within the latent space and our pre-training task is capable produce competitive results compared models trained specifically on shadow removal.
For evaluating our method, we apply the sliding window, removing the shadow from the each tile. After recombining the tiles, we resize the output 256 256 done in previous methods before calculating the metrics. The metrics we report are LPIPS (Zhang et al.] 2018). Mean Absolute Error (MAB) in *b colour-space, structural similarity (SSIM), and Peak Signal-to-Nor to-oose Ratio (PSNR) as shown in [ There are several inconsistencies in previous methods in whether they were using Root Mean Squared Error (RMSE) or MAE, making results less reliable than SSIM and PSNR. We therefore re-evaluated all the methods that provided their output shadow-free images. Where these images were not available, we show the metrics reported in the paper.
Albedo Estimation
Shadow Removal
Figure 6: Examples of our albedo estimation and shadow removal.
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ISTD+
SRD
Method
Mask
LPIPS 1.
MAE J
SSIM 1
PSNR 1
LPIPS
MAE L
SSIM
PSNR
BM
Yes
0.027
1.78
0.967
33.92
0.047
2,36
0.944
31.92
ShadewFomer
Yes
0.022
1,60
0.971
35.37
0.958
32.90
Li al.
Yes
0.033
2.01
0.959
33.72
0.044
0.942
33.72
HomoFormer
Yes
0.022
0.968
35.26
0.955
35.33
DMTN
No
0.033
2.16
0.960
32.19
0.045
2.29
0.937
32.79
ShadowRefiner
No
0,043*
0.928
31.03*
OmniSR
0,025
0.966
33.30
0.042
2.41
0.941
31.96
StubleShadow Removal
0.968
33.24
CroCoDiLige Light (ours S)
No
0.038
2.86
0.929
30.17
0.041
3.01
0.931
30.01
CroCoDiLight (ours oracle)
No
0.028
2,00
0.936
33.41
0.034
2.25
0.937
32.47
Table 2: Shadow Removal evaluations on both the ISTD+ and SRD datasets. We distinguish between mask-based models and those that do not require shadow masks. All metrics are re-evaluated on the provided images from each model unless indicated otherwise. "*" indicates that the numbers are self reported and cannot be re-evaluated. "_" indicates that they did not report the metric.
The methods we compare against are split into two categories, masked and unmasked. The ma- jority of methods for shadow removal make use of an input mask to specify specific shadow to remove. Recent methods have also been tackling the more challenging mask-free shadow removal task. While the metrics do not put our method as state of the art, results in Figure 16 show ex- amples of more effective shadow removal than other methods. It is primarily subtle colour variation across each image that is detrimental to the metrics. Our pre- tre-training disentangles shadows into the lighting latent which then transformed to a shadow free latent. The oracle metrics demonstrate that better transformations do also exist. It is also worth noting that other methods fine-tune their models on case-by- case-by-case basis for each benchmark. This enables them to achieve better evaluation metrics, but limits the generalisability of their trained models. These differences due inher- ent visual biases in each dataset. Our method is jointly trained on all the aforementioned datasets and applied every benchmark produce a single general-purpos shadow-removal component S. The masked methods we evaluate are BMNet (Zhu et al.) (022). ShadowFormed (Guo et al. 2023),, the method from Li al. (2023b), and HomoFormer (Xiao al.) 2024). The mask-free methods we evaluate are DMTN (Liu et al. 2023), Shadow (Dong et al. 2024), OmniSR (Xu et al.)
2025a), and StableShado (Xu et 2025b).
Intrinsic Image Decomposition We also trained a latent-space transformation model for in- trinsic image decomposition in predicting albedo reflectance images from fully lit image. As with shadow removal, retains the same architecture maps the lighting latent vector So, as part of 8, to the new albedo latent s to be re-entangled with the intrinsic latents using R as shown Figure 5 For this task, we train the model using CGIntrinsics and ML-Hypersim datasets. Note that while this was only trained using synthetic data, the real data in pre- training enables generalisability.
R and is initialised with the same weights.
In comparing against other models, evaluate on the IIW WHDR benchmark using the test set defined
Method
WHDR (%)
by Narihira et al. (2015). In Table 3 we compare against CGIntrinsics (Li & Snavely] 2018b), NIID-
CGIntrinsies (2018)
17.8
NIID-Net (2020)
16.6
Net (Luo et al.) 2020). PIE-Net (Das et al.) (0022. Ordinal Shading (Careaga & Aksoy (003)) and In- trinsicDiffusion (Luo et al. (024). Our model pro- vides -tate-of-the-art results despite albedo estima- tion not being the primary task. We do not show methods trained on IIW for fair evaluation as they often over-fit to the WHDR metric, our method still very competitive against them despite not train- ing on IIW. GLoSH (Zhou et al. 2019) has a score of 15.2%, and Lossless Intrinsic Image Decomposi- tion (Sha et al. 2025) scores 13.8%. However, this evaluation metric does have some flaws which
PIE-Net (2022)
21.3
Ordinal Shading (2023)
24.9
IntrinsicDiffusion (2024)
17.9
CroCoDiLight (Ours)
15.4
Ordinal Shading + 0.5
15.3
CroCoDiLight (Ours) + 0.5
14.3
Table 3: Evaluations of the WHDR metric on the IIW test set for models that have not been trained on the IIW dataset.

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highlighted in Ordinal Shading. They demonstrate that despite having qualitatively good results, they perform poorly on the benchmark, but by doing a simple shift by adding 0.5 to the image RGB values shifting it out of the range of 0 to I, they get significant jump in performance. This same arithmetic shift gives us smaller jump in performance. Despite this potential for tuning a method performs well without doing Figure demonstrates
model to the benchmark, decomposition results beyond just metrics.
DISCUSSION
Ablation Study In order to determine the effectiveness of CroCo pre-training, we trained two comparison models with the same data, number of iterations, and proposed model components I and R. One was trained using the same architecture, with the CroCo encoder and our decoder, both jointly trained from scratch with I and R. The other was without the CroCo encoder simplify what the model had to learn by reducing the number of parameters. We instead added a simple en- coder which splits the image into patches and embeds them with simple linear and normalisation layer into z. Then to decode, a DPT head was attached to the relighting transformer R instead of us- ing D. The shadow removal and albedo estimation latent-space models 8 and A were also retrained for both comparisons. Table 4 demonstrates that when training everything from scratch, the simpler architecture does well. However, using the pre-trained CroCo encoder makes notable improve- ments in albedo estimation and as well as improvements in shadow removal. The improvements on these metrics show the effectiveness of cross-view completion as pre-training for relighting tasks, primarily in embedding scene intrinsics in latent space. This provides significant evidence for our hypothesis of the photometric and relighting capabilities of CroCo latent space.
IIW
ISTD+
SRD
CroCo
WHDR
WHDR (+0.5) |
SSIM
PSNR
SSIM 1
PSNR 1
19.1%
16.8%
0.924
29.71
0.928
29.87
27.7%
22.6%
0.795
25.34
0.824
22.98
15.4%
14.3%
0.929
30.17
0.931
30.01
Table 4: Ablation study the effectiveness of using the CroCo v2 encoder, with without the pre-trained weights, or using a simpler architecture trained from scratch.
Limitations and Future Work There are some limitations in our work, mostly deriving from the architectural decision to use single lighting latent for each tile. The compression of the small latent vector means that reconstructing sharp shadows is difficult. Some bottleneck necessary for disentangling lighting from intrinsics, but it inhibits the fidelity of lighting extraction. Tiling high- resolution images produces colour variations shadow removal, as shown in Appendix F..,, which is due lack of global image context per-tile. Pixel-aligned training also means that lighting is extracted in image-space without 3D reasoning, leading to sensitivity in misalignment, and a current lack of control of relighting.
Architectural ablation studies to explore various lighting representations and alternative encoders would help expand upon this work. Such lighting representations could consist of using mul- tiple lighting latents, varying their size, or using spatially varying lighting maps. Comparisons of the CroCo v2 encoder with other pretrained encoders and their pretraining objectives would high- light differences learning lighting invariance as opposed to understanding lighting changes. With exploring our model's i capabilities, lighting latent mapper could be trained to better guide interpo- lation when trained on videos. There is also the expansion to be done into user-controllable lighting from arbitrary images or other conditioning.
Conclusion All these various ways of manipulating the lighting latent space for the purposes of relighting, shadow removal, and intrinsic image decomposition clearly demonstrate the effectiveness of this pre-training method. By working within the CroCo latent space to disentangle and swap the lighting latent vectors, there many downstream photometric tasks that be carried out purely through manipulation the lighting latent. Our method provides a general general-purpose purpose lighting model that can easily be expanded with extra data or additional sub-modules.
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For reproducibility, we describe the architecture of the model in detail throughout Section 3 along with task-specific model components for shadow removal and albedo estimation in Section 5.2 The datasets included in training data along with augmentations discussed in Section and the hyperr-asameters are listed in detail in Appendix A The code is publicly available at ht htttpps//ggithit htw.:/hhhoooooceaammmmmmmme a stai cettaaiffooggo/oooooooooo
Reproducibility Statement
githt
Acknowledgments Model training was carried out on the Viking cluster, which is high per- formance compute facility provided by the University of York. We grateful for computational support from the University of York, IT Services, and the Research IT team.
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APPENDICES
A HYPER-PARAMETERS
To train the single-view decoder, we trained for 120k iterations on ImageNet with a batch size of 48 and learning rate of x 10-4 The components I and R. were jointly trained for 30 epochs (approx. 143k iterations) with batch size of 12 and the same learning rate of 1 x 10 The shadow removal and albedo estimation models S and A were both trained with a batch size of 32 and a learning rate of 5 10-5 Due to different task-specific dataset sizes, they were trained with different number of epochs. S was trained for 120 epochs (approx. 18.8k iterations), and A was trained for 40 epochs (approx. 27.5k iterations).
B CROCO DECODING RESULTS
To demonstrate the necessity of training own single-view decoder instead of using the CroCo v2 binocular decoder, we visualise the results of using each to encode and decode a single image. To the binocular encoder single-view encoder, feed the source image in twice. The CroCo
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patches are passed in both the primary and secondary view. Figure 7 shows the lack of fidelity in the CroCo decoder when compared to our single-veww decoder.
Input
CroCo
Ours
Input
CroCo
Ours
Figure 7: Comparison of the CroCo v2 binocular decoder compared single-view decoder.
C LIGHTING LATENT COMPRESSION
Figure 8 demonstrates how much lighting information is compressed and encoded into the lighting latent. To demonstrate this, the intrinsic latent patches are extracted from blank black image, and then relit with the lighting latent from a reference image, before being decoded. This demonstrates components the reference image that are embedded into latent space. We also demonstrate the extent of information in the intrinsic patch embeddings by taking them from the reference image and relight it with a lighting latent consisting of only zeros ensuring the only information shown is from the intrinsic patches.
Blank
Reference
Lighting
Reference
Intrinsics
Figure 8: Visualisation of information encoded into the lighting latent and intrinsic patches. Intrinsic patches are disentangled from the blank image, and relit using the lighting latent disentangled from the reference image, and decoded to produce the lighting image. The intrinsics of the reference image are then entangled with a lighting latent vector that is all zeros before also being decoded to produce the intrinsics image.
D RELIGHTING EXAMPLES
Here provide further examples of manipulating the lighting latent. In Figure we take the light- ing from one frame of a timelapse, and relight the rest of the frames to match We also demonstrate the opposite effect in Figure where every other frame. Finally we include a couple more examples linearly interpolating the lighting latent in Figure 11 We extract the lighting latents per-tile from frames, interpolate between
freeze the intrinsics of single frame and relight it to match
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them, and relight the intrinsics of the first with the interpolated latents. Table shows quantitative results of upsampling by interpolating halfway between every 7th frame in timelases. A video
demonstration of various lighting latent manipulations is included in the supplementary material.
Figure 9: An example of extracting the lighting from the fourth column and relighting the intrinsic patches the other frames match The first row of the ground truth input images, and the second row is of them relit accordingly.
Figure 10: Examples of taking the intrinsic patches from of the frames and relighting it with the lighting from other frames. These examples demonstrate preventing the clock from turning (which it does in the original frames), and keeping the same parked cars despite them changing throughout the day. The first row uses the intrinsics from the first column, but the second row uses the intrinsics from the second column due lack of detail in the first column.
Figure 11: Further examples of the capabilities of linearly interpolating between lighting conditions the lighting latent space.
E ALBEDO ESTIMATION RESULTS
To more clearly demonstrate the effectiveness of the albedo estimation module, include several examples of albedo estimation from the IIW dataset in Figure We extract & from the image, feed it through A produce the albedo latent s which is used to relight the intrinsic patches.
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000
Figure 12: Additional examples of albedo estimation IIW.
F SHADOW REMOVAL RESULTS
We also provide additional shadow-removal results on the SRD, ISTD+, and WSRD+ datasets in Figures| and 15 This demonstrates the effectiveness of our model at shadow removal, seem- ingly in contrast to the metric results Table To explore the reason for this, we compare specific examples of shadow removal against the results of previous methods. (6gs key examples where our overall image consistency of shadow removal is better than other methods. The difference maps highlight that the parts of the image outside of the shadow have an overall colour shift with greater difference than the other methods, but the difference in the shadowed part of the image is much less significant. This highlights that with improved colour mapping, our method could get boost in the metrics causing them to more clearly reflect the shadow removal effectiveness.
F.1 FAILURE CASES
While our method is generally effective at shadow removal as seen in the qualitative results, our model still has some failure cases as shown in Figure [7] Some challenging scenarios cause it to not remove the shadow. There are also failures caused by the use of the the sliding window. If tile is completely covered in shadow, does not have a big enough perceptual field to determine the necessary brightness and colour of the shadow-free regions. There several potential ways of fixing this issue. Poisson image editing (Pérez et al.) (003) can be used to combine the tiles using their gradients to match reference tile. The entire image can be resized to single tile
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Input
GT
Ours
Input
GT
Ours
Figure 13: Additional examples of our shadow removal on SRD.
Input
GT
Ours
Input
GT
Ours
Figure 14: Additional examples our shadow removal on ISTD+
Input
GT
Ours
Input
GT
Ours
Figure 15: Additional examples our shadow removal on WSRD+.
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Input & GT
Ours
Xu et al. (2025b)
Xu al. 2025a)
Xiao et al. (2024)
0.0
MAE: 2,22 SSIM: 0.913
MAE: 1,52 SSTM:00 PSNR: 33.51
MAE 182 SSIM: 0.947 PSNK 30.4
MAE: 1445
SSIM: 0.913
PSNK: 28.49
PSNK: 28.49
0.2
0.1
0.0
MAE: 5.75 SSIM: 0.SUS
MAE 4.35 SSIM: 0L007 PSNR: 27.84
MAE 667
MAE: 2.00
SSIM: 0,872 PSNR:2166
SSIM: 0,940
PSNR: 25.26
PSNR: 30.81
0.2
0.1
-01
-02
1.5L
SSIM: 0.933
SSIM: 0,990
SSIM: 0,983
SSIM: 0.985
PSNK 27.12
PSNK: 31.00
PSNK: 31.86
PSNK. 34.32
02
01
-01
-02
SSIM: (1962
SSIM: 0.937
SSIM: 0.911
PSNR: 30.54
PSNR: 30,94
PSNR: 29.20
PSKR:200
Figure 16: Some examples of shadow removal when compared to other methods. We show that our method produces effective results, while demonstrating subtle overall colour shift that is detrimen- tal to the metrics. Each pair of rows show the outputs along with a signed heatmap of the difference between the outputs and the ground truth averaged across channels and scaled up be more visi- ble. We also specify the metrics for each image. From left to right, the other methods we compare against StableShadowRRemor OmniSR, and HomoFormer.
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which can be processed and upscaled to be reference for adjusting the colour and brightness of the high-resolution tiles. Other methods may also be possible to allow self-attention between multiple
lighting latents per-tile, to allow model to adjust the lighting latents based the entire image.
Input
GT
Ours
Input
GT
Ours
Shadow removal failure cases. Such failures include varying colours in tiles due to the sliding window having small perceptual field, whereas others just fail to remove the shadows.
Figure 17:
F.2 MON-FAILURE CASES
During evaluation, noticed that certain shadow-removal results seemed to be effective in ways that could not accounted for by the metrics. Figure [8 demonstrates couple of these examples. The method is able to distinguish between reflections and shadows in such a way that the shadow can be removed while retaining the reflection. This however is detrimental to the metric as the ground truth shadow-free images do not have some of the reflections. Another example of it doing better than the ground-truth is where it removes additional shadows that were not accounted for. Finally, we also noticed that when removing direct shadows in the WSRD+ dataset, it replaces them with ambient occlusion rather than removing it altogether. This fits with incorporating WSRD+ into training due its design of turning an external light and off, while the other shadow-removal datasets would not have this bias as they cast external shadows.
Input
GT
Ours
Input
GT
Ours
Figure 18: Shadow removal cases that may match the ground truth shadow-free image, but working better (i.e. removing additional shadows or not removing reflections). We also include cases where replaces direct shadows with ambient occlusion.
F.3 INS DATASET
SubbeSShadowRemoval and OmniSR make use of the synthetic INS dataset created by Xu et al. (025a)) OmniSR to improve results. Although our original model did not include training, we trained our models again with it (while keeping all previously used datasets) to see if there were any improvements. We adjusted the epochs to account for the change in dataset size to ensure comparison models had been trained for approximately the same number of iterations. Table 5 demonstrates a slight improvement on most benchmarks when it is included in the training process for I and R, but including it in training of S detrimental. It is worth noting that some cases allowed for removal of shadows not accounted for in the benchmarks which is part of the cause of worse results (see Figure 19). Table T demonstrates that including INS in the training of I and
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R. was detrimental to albedo estimation. It was due this and minimal improvements in shadow removal that we did not include it in training our final model.
Ablations
I
SRD
WSRD+
INS
INS in and R. INS in S
SSIM
PSNR
SSIM 1
PSNR 1
SSIM 1
PSNR
SSIM
PSNR
0.929
30.17
0.931
30.01
0.922
25.30
0.869
21.87
0,934
31.23
0.932
30.55
0.924
25.31
0.918
27
0,932
31.07
0.927
29.38
0.914
24.49
0.934
28,30
Table 5: Ablation study on the effect on shadow removal of including the synthetic INS shadow removal dataset in training the main model components and R, and in training the shadow removal module S.
IIW
INS in and R
WHDR L
WHDR (+0.5) +
15,4%
14.3%
17.6%
14.9%
Table 6: Ablation study on the effect on albedo estimation of including the synthetic INS shadow removal dataset training the main model components and R.
Input
GT
Ours INS
Figure 19: Example of the model where I, R, and S were all trained on INS correctly removing shadows that SRD did not account for in the shadow-free image.
21


Reviews:
[{"review_id": "rBdw9c8s0z", "reviewer": "Reviewer_qx9H", "summary": "This paper explores whether CroCo encoders, originally trained for cross-view completion with geometric objectives, also implicitly learn photometric representations due to illumination variations in training pairs. The authors propose CroCoDiLight to make this knowledge explicit through a delighting transformer that disentangles CroCo patch embeddings into a single lighting latent and intrinsic patch latents. Also a relighting transformer R that recombines them, and a single-view decoder D for high quality RGB reconstruction. Training uses only 57k pixel-aligned image pairs with different illumination, two orders of magnitude less than CroCo's training data as claimed in the paper. The method demonstrates applications in lighting interpolation, timelapse stabilization, shadow removal, and albedo estimation. Results show state-of-the-art on IIW among methods not trained on IIW, though shadow removal and construction metrics are good but not convincing.", "questions": "Can you quantify performance degradation as shadow sharpness increases? Even a simple analysis binning test images by edge gradient magnitude or manually annotating hard vs. soft shadows would help establish the method's scope.\nWhy not implement the color correction solutions you mention (Poisson blending, global reference) and show results? This seems critical for addressing both the metrics gap and the tiling artifacts. \nA dimension ablation (dimensionality of S0) would help understand what information is being compressed.\nFor the world-space vs. image-space issue- did you try encoding light direction or position explicitly? Even rough geometric cues might help.\nHow does the method handle colored lighting vs colored surfaces? The disentanglement seems like it would be ambiguous\nLack of extensive ablation studies-\nMultiple lighting latents per image/tile (which would help with local lighting)\nLighting latent dimensionality (is 1024 dimensions necessary? wasteful?)\nSpatial lighting maps vs. single vector", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The hypothesis that CroCo learns implicit photometric understanding is intuitive and the paper validates it convincingly. The delight-relight framing is elegant.\nNeeding only 57k pairs versus CroCo's 5.3 million is a strong practical advantage, especially given that aligned multi-illumination data is scarce. The paper shows strong albedo results- achieving 14.3% WHDR on IIW without training on IIW is impressive and suggests the intrinsic latents capture meaningful scene properties.\nThe paper covers multiple downstream tasks and provides extensive qualitative results. The failure case analysis in Appendix F is honest and valuable. The timelapse stabilization and lighting interpolation demos are compelling and showcase practical utility though there are some limitations such as shadow motion not being entirely smooth and the method struggling with sharp shadow boundaries that move rapidly across scenes.", "weaknesses": "Sharp shadow handling (shading effects, cast shadows, etc) is inadequately addressed: This is my biggest concern. The method uses a single global lighting latent, which fundamentally cannot capture the geometric information needed for sharp shadow boundaries. Sharp shadows arise from point lights and hard occluders, they encode precise light direction, occluder position, and surface geometry. A single image-space vector cannot represent this information, especially when shadow boundaries need to move correctly across multiple frames. The evidence is scattered throughout:\n\nFig. 17 shows direct shadows being replaced with ambient occlusion\nSection 5.1 notes shadow motion during interpolation is \"not entirely smooth\" and Section F.1 admits tiles fully in shadow fail.\nMost successful examples (Figs. 3-4, 8-10) show soft shadows, diffuse lighting, or outdoor scenes with gradual illumination changes\nThe timelapse examples work well for slow sun movement creating soft shadow transitions, but would likely fail for a person walking past a lamp creating sharp moving shadows\n\nTiling artifacts is unresolved: Section 3.5 and Fig. 16 show color inconsistencies from the sliding window approach. Paper mention potential fixes (Poisson blending, global reference tile) but don't implement them. Why present solutions but not evaluate them? The lighting latent being \"optimally used\" at 448×448 is a fundamental design limitation. This significantly undermines the high-resolution claims. Shadow removal metrics don't really match qualitative results.\n\nFig. 17 shows cases where your method is \"working better\" by removing additional shadows, but this also suggests the model isn't learning what the benchmark defines as shadow removal in my opinion.\n\nLimited architectural justification- Why a single lighting latent and why D=1024? \n\nPaper didn't provide ablations on:\n\nMultiple lighting latents per image/tile (which would help with local lighting)\nLighting latent dimensionality (is 1024 dimensions necessary? wasteful?)\nSpatial lighting maps vs. single vector\n\nThe Table 3 ablation uses a much simpler baseline (just linear embedding + DPT head), making it unclear whether gains come from CroCo features or better architecture. A fairer comparison would use the same I/R architecture without CroCo pretraining.\nImage-space lighting is a fundamental limitation: Section 5 and Appendix C mention the lighting latent works in \"image space\" not \"world space\" but don't explore the implications. This means:\n\nThe method can't handle viewpoint changes\nIt can't reason about 3D light positions or directions\nIt's brittle to even small camera motion\nShadows will appear in wrong positions if the camera moves slightly", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "M1Y7HNrsBk", "reviewer": "Reviewer_FRcB", "summary": "This work presents CroCoDiLight, which leverages supposedly inherent lighting disentanglement capability within CroCo to modify & repurpose CroCo for de-lighting / relighting tasks. To achieve this, the authors introduce two networks that explicitly separate CroCo's patch embeddings into a lighting vector and lighting-invariant latents, then recombine them, demonstrating that photometric understanding is already embedded in CroCo's representations and can be efficiently extracted for explicit control and various relighting tasks such as interpolation between lighting conditions, shadow removal, and albedo estimation.", "questions": "Please see Weaknesses section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is well-written and easy to follow. Good writing.\n- The paper starts with a strong observation & hypothesis, recognizing the (possible) inherent capability within the original CroCo paper that its encoder implicitly encodes illumination information, which enables delighting & relighting during its novel view reconstruction task, and extending it to the hypothesis that this capability can be explicitly harnessed to achieve photometric tasks such as relighting/shadow removal/delighting. I believe the work is well-motivated and tackles an interesting question about the nature of CroCo and its representation.\n- The authors offer a simple and intuitive solution to the problem (though this might point to the lack of novelty, as I would mention in the weakness section) by including a latent vector that disentangles lighting from geometry during the training phase. The method is simple and straightforward, effectively achieving its goal of lighting disentanglement as shown in the results.", "weaknesses": "- The original CroCo paper was a representation learning paper, focused on pre-training the model to be generally more suitable for various 3D / NVS downstream tasks from a simple two-view reconstruction loss. However, it seems that this work is more focused on training a model towards each specific downstream task (relighting / shadow removal / intrinsic image decomposition), which makes this work more closely aligned with existing relighting methods, of which there are already many. However, in this view, the core method of this paper (adding a separate latent vector for style and teaching model to change 'style (lighting)' of the image) very closely resembles previous GAN methods that achieve similar goals in generative scene and seems to lack novelty. Is there a more general implication for this method that may be relevant to representation learning / other downstream tasks, as was the original CroCo?\n- The method requires datasets with identical geometry under different lighting, significantly limiting available training data. While synthetic datasets like HyperSim could be used, they introduce domain gap issues. How the authors address this fundamental limitation remains unclear.\n- Lighting manipulation requires \"walking\" through latent space, making it difficult to achieve specific desired lighting conditions. The paper lacks a demonstration of how users can specify target lighting or achieve reproducible, controllable results without another scene that has desired lighting - can this point be further elaborated?\n- What does this method have in advantage in comparison to Diffusion-based relighting methods, especially IC-Light (ICLR 2025), whose lighting can be controlled with text prompt as well as can be applied to various domains beyond scene imagery (i.e. including human faces, etc.)? Please elaborate.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CuEKwkSPoQ", "reviewer": "Reviewer_3fbg", "summary": "The authors introduce a method to disentangle CroCo latent representations into two components: a global latent vector capturing illumination, and patch-wise latent vectors representing the intrinsic properties of the scene. The model is trained in a self-supervised manner using pixel-wise aligned image pairs taken under different lighting conditions, guided by per patch cross-lighting and intrinsic consistency losses. They demonstrate that the disentangled latent space can be effectively leveraged for novel tasks such as interpolating between lighting conditions, shadow removal, and albedo estimation.", "questions": "I like the illustration and the narrative in Figure 1 as it effectively conveys how the CroCo model implicitly learns to extract content information from the second image and appearance information from the first, guided by the training data. This raises an interesting question: could the two models be integrated to jointly learn both the reconstruction and the disentangled latent representations assuming relevant training set (e.g. image triplets)? Such a unified approach—where mask content reconstruction and disentangled latent representations are jointly learned—could potentially enhance consistency and improve performance not only on the downstream tasks explored in this paper (e.g., shadow removal, albedo estimation, lighting interpolation), but also on geometric tasks such as 3D reconstruction.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "I found the approach of using an encoder-decoder architecture inspired by the Croco architecture to disentangle illumination from intrinsic scene representation both interesting and original. The design of the self-supervised training framework, particularly the proposed losses, appears well thought out and conceptually sound.\n\nThe paper demonstrates, through a range of tasks—including lighting interpolation, shadow removal, albedo estimation, and intrinsic image decomposition—that the proposed disentanglement approach is effective. The latent representations prove useful for handling these diverse downstream tasks. While the model does not outperform state-of-the-art methods specifically tailored for each task, the results are nonetheless promising, and the visual examples are rather convincing.", "weaknesses": "It is probable that leveraging the pretrained CrocoV2 model is beneficial, because the model was trained on a large set of image pairs captured under varying lighting conditions. Additionally, photometric augmentations applied during training likely enhanced the model to be robust to lighting changes. Still the experiments in the paper do not completely prove this.  How would the model work if instead of the Croco encoder the  MAE, DINOv2 or DINOv3 encoder is used and disentangled? Would the model perform less well on the downstream tasks? \n\nAlso, the ablation in Table 6 raises a concern: the architecture of the two models compared are no longer identical so while supposedly it is true, it is not directly shown that the gain comes from the pre-trained model and not from the architecture choice.  It would have been insightful to evaluate also a model that retains the CroCo encoder architecture but is initialized from scratch. While indeed the training dataset is smaller, the learning task seems simpler than the hidden content reconstruction, making such an experiment worthwhile. Note also that the performance gain with the CroCo pretraining is significant only for the Intrinsic Image Decomposition task, much less for the Shadow Removal.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eyqm9u52Or", "reviewer": "Reviewer_Zxsi", "summary": "The paper proposes CroCoDiLight, which repurposes the pre-trained CroCo encoder for photometric tasks. The authors hypothesize that CroCo implicitly learns lighting information through cross-view completion training on image pairs with varying illumination. The method is demonstrated on tasks including lighting stabilization in timelapse, temporal upsampling, shadow removal, and intrinsic decomposition, trained on datasets two orders of magnitude smaller than CroCo's original training data.", "questions": "- **Q1.** (Related to W1, authors may answer together) Have you experimented with other two-view foundation models like DUST3R or MAST3R (which build on CroCo)? What about single-view and multi-view models?\n\n- **Q2.** (Related to W2, authors may answer together) Could you provide quantitative metrics for lighting stabilization and temporal upsampling tasks? For example, comparing temporal coherence metrics or perceptual quality against baseline interpolation methods? Or is that something beyond the scope of this paper?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- **S1.** Novel insight that cross-view completion models implicitly learn photometric understanding. The hypothesis that CroCo must estimate and manipulate lighting to complete masked patches across views with varying illumination is interesting and well-motivated.\n\n- **S2.** Efficient learning paradigm requiring datasets two orders of magnitude smaller than original CroCo training. This demonstrates that photometric knowledge is already embedded in the pre-trained encoder and only requires extraction rather than learning from scratch.\n\n- **S3.** Demonstrates feasibility of repurposing cross-view completion foundation models for photometric tasks, opening a new direction for leveraging geometric pre-training for appearance-related downstream applications.", "weaknesses": "- **W1.** My main concern is the paper's positioning and the scope of investigation. The paper is framed as an application showcase (e.g., lighting stabilization in timelapse, temporal upsampling, shadow removal, intrinsic decomposition, etc.), but its core contribution is the insight into repurposing foundation models. It would be much stronger if repositioned as a systematic investigation (similar to Probe3D) into which and how pre-trained vision foundation models capture photometric properties, and why. The current study is confined to CroCo, missing a crucial comparative analysis against other foundation models. An investigation should include:\n    - Other two-view encoders (e.g., DUST3R, MAST3R) and matchers (e.g., RoMa, GIM).\n    - Single-view models known for strong correspondence (e.g., DINOv2, DINOv3).\n    - Multi-view models where two-view is a special case (e.g., VGGT, Pi-3).\n  - Such a comparison would provide more generalizable insights into how different pre-training objectives (cross-view, contrastive, etc.) contribute to learning photometric understanding.\n\n- **W2.** Mixed results on quantitative evaluations and missing some evaluations.\n    - We only have quantitative results for shadow removal (Table 1) and intrinsic decomposition (Table 2), while the other applications (lighting stabilization in timelapse, temporal upsampling) lack any quantitative benchmarks.\n    - The intrinsic decomposition results are state-of-the-art (Table 2), while shadow removal is not (Table 1). This is acceptable if the paper is repositioned as an investigation (per W1), where the goal is demonstrating feasibility rather than beating every SOTA. However, under the current paper's narrative, it is difficult to justify the advantage of CroCoDiLight over other specialized methods.\n    - That is, if we shift the paper's focus from application showcase to systematic investigation, we don't need to provide quantitative results for all applications, and we don't necessarily need to beat every SOTA.\n\nOverall, the paper's insight (cross-view completion models implicitly learn photometric understanding) is valuable, but the paper's positioning and scope are not strong enough to provide a comprehensive investigation of this insight. I welcome the authors' response to address these concerns.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)