Latent Intrinsics Emerge from Training to Relight
Authors: Xiao Zhang
William Gao
Seemandhar Jain
Michael Maire
David Forsyth
Anand Bhattad
Venue: NeurIPS 2024 spotlight (2024)
Keywords: Emergent Albedos
Latent Intrinsics
Relighting
Unsupervised Learning
Intrinsic Images
Primary area: machine_vision
TL;DR: Free albedo extraction from latent features in a relighting-trained model, without seeing albedo like images

Abstract: Image relighting is the task of showing what a scene from a source image would look like if illuminated differently.  Inverse graphic schemes recover an explicit representation of geometry and a set of chosen intrinsics, then relight with some form of renderer.  But error control for inverse graphics is difficult, and inverse graphics methods can represent only the effects of the chosen intrinsics. This paper describes a relighting method that is entirely data-driven, where intrinsics and lighting are each represented as latent variables.  Our approach produces SOTA relightings of real scenes, as measured by standard metrics.  We show that albedo can be recovered from our latent intrinsics without using any example albedos, and that the albedos recovered are competitive with SOTA methods.

Latent Intrinsics Emerge from Training to Relight
Xiao Zhang'
William Gao'
Seemandhar Jain? Anand Bhattad
Michael Maire'
D.A. Forsyth
University of Chicago
University of Illinois Urbana Champaign
TToyot Technological Institute at Chicago
zhang70uchicago edu
bhattadEttic edu
github... io/
(Zoom in chrome ball)
Input
Reference
Relight
Input
Reference
Relight
Input
Reference
Relight
Input
Albedo
Input
Albedo
Input
Albedo
Figure We describe purely data-driven image relighting model. Our model recovers latent variables representing scene intrinsic properties from one image, latent variables representing lighting from another, then applies the lighting to the intrinsies produce relighted scene (top row). There is no physical model of intrinsics, extrinsics or their interaction. Our model relights images of real scenes with SOTA accuracy and more accurate than current supervised methods. Note how, for the chrome ball detail in top center, the specular reflections on the chrome ball (which give an approximate environment map) change when the extrinsics are changed. Note how our model ascribes lighting to visible luminaires when it can (top right), despite the absence of any physical model. A physical model accounts only for effects in that model, and most physical models of surfaces are approximate; in contrast, a latent intrinsic model accounts for whatever produces substantial effects training data. Latent intrinsics yield albedo in a natural fashion (light the scene with an appropriate illuminant). Bottom row shows SOTA albedo estimates recovered from our latent intrinsics.
Abstract
Image relighting is the task of showing what a scene from a source image would look like if illuminated differently. Inverse graphics schemes recover explicit representation of geometry and set of chosen intrinsics, then relight with some form of renderer. However error control for inverse graphics difficult, and inverse graphics methods can represent only the effects of the chosen intrinsics. This paper describes a relighting method that is entirely data-driven, where intrinsies and lighting are each represented as latent variables. Our approach produces SOTA relightings of real scenes, as measured by standard metrics. We show that albedo can be recovered from our latent intrinsics without using any example albedos, and that the albedos recovered are competitive with SOTA methods.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Introduction
Relighting - taking an image of scene, then adjusting so it looks as though it had been taken under another light - has range of applications, including commercial art (e.g., photo enhancement) and data augmentation (e.g., making vision models robust to varying illumination). As a technical problem, relighting is very hard indeed, likely because how a scene changes in appearance when the light is changed can depend on complex surface details (grooves in screws; bark on trees; wood grain) that are hard to capture either in geometric or surface models.
One common approach to relighting a scene is to infer scene characteristics (geometry, surface properties) using inverse graphics methods, then render the scene with new light source. This approach is fraught with difficulties, including the challenge of selecting which material properties to infer and managing error propagation. These methods perform best in outdoor scenes with significant shadow movements but struggle with indoor scenes where interreflections create complex effects (Section 4.2).
As this paper demonstrates, purely data-driven method offers an attractive alternative. A source scene, represented by an image, is encoded to produce a latent representation of intrinsic scene properties. A source illumination, represented by another image, is encoded to produce latent representation of illumination properties. These intrinsic and extrinsic properties are combined and then decoded to produce the relighted image. As byproduct of this training, we find that the latent representation of intrinsic scene properties behaves like an albedo, while another latent representation acts as lighting controller.
Our model can capture complex scene characteristics without explicit supervision by capturing intrinsic properties as latent phenomena, making particularly appealing. In contrast to a physical model, we are not required to choose which effects to capture. This latent approach reduces the need for detailed geometric and surface models, simplifies the learning process, and enhances the model's ability to generalize to diverse and unseen scenes. This makes it highly applicable to wide range of real-world scenarios.
Contributions: We present the first fully data-driven relighting method applicable to images of real complex scenes. Our approach requires explicit lighting supervision, learning relight using paired images alone. We demonstrate that this method effectively trains and generalizes, producing highly accurate relightings. Furthermore, we demonstrate that albedo-like maps can be generated from the model without supervision or prior knowledge of albedo-like images. These intrinsic properties emerge naturally within the model. We validate our model on held-out dataset, applying target lighting conditions from various scenes to assess its generalization capability and precision in real-world scenarios (Section 4.2)
2 Related Work
Intrinsic Images. Humans have been known to perceive scene properties independent of lighting since at least 1867 2 20). In computer vision, the idea dates to Barrow and Tenenbaum [3] and comprises at least depth, normal, albedo, and surface material maps. Depth and normal estimation are now well established (eg (4)). There is a rich literature on albedo estimation (dating to 1959 B0)B). detailed review appears in [16], which breaks out methods as to what kinds of training data they see. Early methods do not see any form of training data, but more recently both CGI data and manual annotations of relative lightness (labels) have become available. Early efforts, such as SIRFS O. focused on using shading information to recover shape, illumination, and reflectance, highlighting the importance of modcling these factors for intrinsic image analysis. Recent strategies include: deep
networks trained synthetic data [33 23 [5]: and conditional generative models (29
The weighted human disagreement ratio (WHDR) evaluation framework was introduced by 5 using the IIW dataset. This is dataset of human judgments that compare the absolute lightness at pairs of points in real images. Each pair is labeled with one of three cases (first lighter, second lighter; indistinguishable) and a weight, which captures the certainty of labelers. One evaluates by computing a weighted comparison of algorithm predictions with human predictions; WHDR scores can be improved by postprocessing because most methods produce albedo fields with very slow gradients, rather than piecewise constant albedos. 110 demonstrate the value of "flattening" albedo (see also
39); ; [ employ a fast bilateral filter
2 to obtain significant improvements in WHDR.

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Using Intrinsic Images for Relighting. Bhattad and Forsyth [6] demonstrated that intrinsic images could be used for reshading inserted objects. This approach can be extended by adjusting the shading in both the foreground and background to eliminate discrepancies [1]. Intrinsic images and
geometry-aware networks have been used for multi-view relighting (41) StyLitGAN [9] introduced method to relight images by identifying directional vectors in the latent space of StyleGAN, but can
only relight StyleGAN generated images and requires explicit albedo and shading to guide relighting. It can be extended to real images using a GAN inversion, but does not generalize 77.. Lightlt [28] controls lighting changes in image generation using diffusion models, by conditioning on shading and normal maps to achieve consistent and controllable lighting. Like these methods, and extrinsics to relight, but ours are latent, with no explicit physical meaning.
intrinsics
Color Constancy. Image color is ambiguous: green pixel could be the result of white light on a green surface, or green light on a white surface. Humans are unaffected by this ambiguity (eg recent review in (.0). There is extensive computer vision literature; recent review appears We do not estimate illumination color but estimate single color correction (Section (.2).
21 4
32
Lighting Estimation and Representation. Accurate lighting representation is crucial for tasks like object insertion and relighting. Traditional methods used parametric models such as environment maps and spherical harmonics to represent illumination [3 42]. Debevec's seminal work [I3] on recovering environment maps from images of mirrored spheres set the foundation for many subsequent works, Methods by Karsch et al. [26] 27], Gardner et al. [17 [8]. Garon et al. [19 and Weber al. 49 advanced the field by using learned models to recover parametric. cemi-parametric or panoramic representations of illumination. Recent approaches include representing illumination fields as dense 2D grids of spherical harmonic sources (34 36 or learning 3D volumes of spherical Gaussians 48]. These methods can model complex dependent effects but require extensive Our approach diverges by not relying on labeled illumination representations or CGI data, instead producing abstract representations of illumination through deep features without specific physical interpretations.
CGI datasets for training 43 35
Image-based Relighting, Other works focus on portrait relighting using deep learning 45 55
40
44.. which are typically specialized to faces and trained on paired light-stage data. Self-supervised methods for outdoor image relighting leverage single-image decomposition with parametric outdoor illumination, benefiting from simpler lighting conditions dominated by sky and sunlight [54 37
22
introduced self-attention autoencoder model to re-render a source image to match the illumination of a guide image, focusing on separating scene representation and lighting estimation with self-attention mechanism for targeted relighting. Similarly, [5]] proposed a depth-guided image relighting, which combines source and guide images along with their depth maps to generate relit images. In contrast, our work shows that intrinsic properties relevant to relighting can emerge naturally from training to relight, facilitating complex scene relighting without the need for explicit lighting estimation. We compare with both 22 and [51] for relighting capabilities real scenes.
Emergent Intrinsic Properties. Bhattad et al. [8] and Du al. [44 demonstrate that intrinsic images can be extracted from generative models using small intrinsic image dataset obtained from pretrained off-the-shelf intrinsic image models. Our work explores how intrinsic image properties emerge as a result of training model for relighting, without the need for an intrinsic image dataset.
3 Learning Latent Intrinsic from Relighting.
Our relighting model can be seen as a form of autoencoder. One encoder computes a latent represen- tation of scene intrinsics from image of target scene; another computes latent representation of scene extrinsics from an image of a placeholder scene in the reference lighting. These are combined, then decoded into a final image of the target scene in the reference lighting. Losses impose the requirements that (a) the final image is right and (b) the latent intrinsics computed for a scene are not affected by illumination. The procedure for combining intrinsics and extrinsics is carefully designed to make it very difficult for intrinsic features of the placeholder scene to "leak" into the final image.
3.1 Model structure
Write I E "xxx3 for the input image, captured from scene with lighting configuration I. Training uses pairs Il and I representing the same scene under different lighting
Decoder setup:
3

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Constrained Scaling
Inference time: relight with arbitrary A
E
0
Zoom
I+a (anh(MLP(Z55)
1r
Share
Parameters
D
Inference time: estimate albedo for free
7 p
0 tunbMLL((
E
1 0)
(8
Figure 2: The network diagram of our relighting model. The model functions as an autoencoder, comprising encoder E and decoder D. Left Half: The encoder E maps input image I captured under scene and lighting I, to low-dimensional extrinsic features L' and set of intrinsic features map (S, The decoder D then generates new images based on these intrinsic and extrinsic representations. Right Half: We employ constrained scaling for the injection of L', utilizing 0 < & 1 to regularize the information passed from L', thereby enforcing parameterization of the extrinsic features. We train our system to relight target images given input paired with images captured under the same scene s. During inference, our model demonstrates the ability to generalize to arbitrary reference images for relighting and can estimate albedo for free.
conditions 4 and l2. The model does not see detailed lighting information (for example, the index of the lighting) during training, because standardizing lighting settings across various scenes is often impractical.
Write E for the encoder, D for the decoder. The encoder must produce the intrinsic and extrinsic representations from the input image. Write S R [I, W.)xC: for spatial feature maps yielding the intrinsic representation, with for the layer index, and L'2 R. for extrinsic features; we have:
E(T)) = (s.,,...'
(1)
We apply L2 normalization along the feature channel both sets of features. During training, we add random Gaussian noise to the input image to enhance semantic scene understanding capabilities:
E(I ae) (S (, L.
Decoder setup: The decoder D relights The using extrinsic features extracted from !!:
D L = F1 7-
(3)
We optimize the autoencoder using a pixel-wise loss on both relighted and reconstructed images:
Crelight Lepeel P:: I'r) + Lpisel T1 r!
(4)
where Cpixel represents the pixel-wise losses: L2 distance on pixels; structural similarity index (SSIM) 47|:
and 12 distance image spatial gradient (weights 10, 0.1 and respectively).
3.2
Intrinsicness
Our model should report the same latent intrinsic for the same scene in different lightings, so we apply the following loss to the encoder:
Intrinsicness:
Lintrinsic
Σ s skalla le-3 Crog (
(5)
where freg is regularization term on intrinsic features, defined follows:
(Sy = |R(S) R(S)
(6)
R(S) = log det I d
ST
(7)
nd=

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where R(S) is the coding rate [53 for a matrix S R"xd with each row 12 normalized, under distortion constant X. S is random matrix with the same shape of S and each row of $ is sampled from uniform hyperspherical distribution the start of learning. In Equ 5 R(S) serves as the optimization target of R( to encourage the S to uniformly spread out in the hyperspherical space. This strategy now widely used in self-supervised learning; without the regularization term, the model can minimize the feature distance by simply collapsing the distribution of S with small variance, which will not yield effective lighting invariance.
3.3 Combining intrinsics and extrinsics
The placeholder scene is necessary to communicate illumination to the model, but has important nuisance features. Intrinsic information from this scene could "leak" into the final image, spoiling results. We introduce constrained scaling, a structural bottleneck that restricts the amount of information transmitted from the leamed extrinsic features.
Write F E Rxxxe for the feature map fed to the decoder. Constrained scaling combines intrinsic and extrinsic features by
(8) where MLP, a series of fully connected layers with non-linear activation, aligns L to the latent channel dimension of F and a < 1 is a small non-negative scalar (we use 5e-3). This approach means that any single extrinsic feature vector has little effect on the feature for an effect, the extrinsics must be pooled over multiple locations. Illumination fields tend be spatially smooth, supporting the insight that enforced pooling is a good idea.
: F O (1+ tanh (MLP (E!))
Constrained scaling compresses latent vectors into very small numerical range, making learning difficult. We use a regularizer to promote a uniform distribution L which improves optimization. In particular, we have
Lextrinsic := Lreg (
we effectively push the lighting code uniformly spread over [-a, al] where the absolute value of each channel indicates the strength of the light. As side effect, by setting a = 0 to disable the contribution of the lighting code, we get image albedo estimation from our model for free.
By choosing < and training model with uniform regularization term Equ 9
Our final training objective is weighted combination of all individual loss terms:
C = Frelight + lc-1 Lintrinsic + lc-4 Lextrinsic
(10)
Experiments
We will first provide brief description of our experimental procedure (Sec 4.1
followed by discussion on how we evaluate the various relighting capabilities of our approach, including its strong generalization across datasets with different distributions (Sec|4.2) Finally, we will present the emergent albedo that is recovered from the latent intrinsic without using any albedo-like images (Sec
4.1 Experiment Details
Training Details We train our model using the MIT multi-illumination dataset (38 which includes images of 1,015 indoor scenes captured under 25 fixed lighting, totaling 25,375 images. We follow the official data split and train our model on the 985 training scenes. During training, we randomly sample pairs of images from the same scene under different lighting conditions and perform random spatial cropping, with the crop ratio randomly selected between 0.2 and 1.0, followed by resizing the cropped image resolution of 256x256. For further details, please refer to our appendix.
4.2 Evaluating image relighting
Relighting on the Multi-llluiination dataset: We relight images of scenes in the test set using reference images from the test set, then compare to the correct known relighting from the test set
5

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SAAE sup
SAAE
S3Net Depth
Ours
SAAE sup
SAAE S3Net
Ours
Input
Ref
unsup
unsup
Target
Input
unsup
Depth
unsup
Target
Figure 3: Our method outperforms all other approaches in estimating light and rendering the scene. The Unsupervised SA-AE [22] method fails by incorporating intrinsic elements from reference images. The S3Net [5]] approach struggles with rendering when using unpaired reference images. Right: A zoomed-in view of the chrome ball was used as probe to evaluate detail preservation in the environment map. Our method effectively retains the intricate room layout and accurately renders the appropriate lighting effects.
Methods
Labels
Output
Color Correction
Raw Output
Color Correction
RMSE.
SSIM
RMSE]
SSIMT
RMSE
SSIM
RMSE,
SSIM
Inpur Ime
0.384
0.438
0.312
0.492
0.471
0.287
0.352
0.407
SA-AE
22
Light
0.288
0.484
0.232
0,559
1e-2
0.314
0.444
0.238
0.546
SA-AE
0.443
0,300
0,317
0.431
5e-3
0.297
0.473
0.222
0.571
S3Net 51
Depth
0.512
0.331
0.418
0,374
1c-3
0.312
0.453
0.256
0.524
S3Net 51
0.499
0.336
0.414
0,377
Se-4
0.309
0.460
0.253
0.533
Ours(a 0)
0.326
0.232
0.242
0.541
Ours(wlo Crey)
0.315
0.462
0.232
0.550
Ours
0.297
0.473
0.222
0.571
Table 1: We assess the quality of image relighting using
Table 2: We analyze the impact of a relighting quality using the multi- illumination dataset [38]. Setting a to co, which removes the scaling constraints, results in poor relighting quality, indi- cating that restricting information from extrinsic sources significantly improves generation quality. Within limited pa- rameter search, 5e-3 yields the best re- sults.
the multi-lllumination dataset (38] Our method, when evaluated on raw output, significantly outperforms all other unsupervised approaches and achieves competitive results compared to the supervised SA-SA (22). which requires ground truth light supervision. When we correct the colors by eliminating global color drift caused by light ambiguity, our method surpasses all other approaches. Additionally. warming up the model as a denoising au- toencoder proves beneficial compared to when it is not
warmed up (a = 0).
using various metrics. For each input image, we randomly sample reference images from different scenes and lighting conditions. reduce the effect of randomness comparing different relighting strategies, we select 12 random reference images for each input image, and maintain the same image-reference pairs when evaluating different models. We report the results, measured RMSE and SSIM, in Table I We report these metrics both for absolute predictions and for predictions where any global color shift is corrected by a single, least-squares scale each predicted color layer (i.e. one scale for R; one for G; one for B). This color correction allows to distinguish between spatial errors and global color shifts; these appear to have a significant effect, possibly because there visible color shifts present in of the dataset images.
Table I we compare SA-AE [22 a model that requires ground truth light index for supervision, and S3-Net, which needs ground truth depth map as conditional input. For S3-Net, we use state-of-the-art depth estimator to provide pseudo-GT on the relighting dataset as input. For fair comparison to model, which does not require any supervision outside of the ground truth

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Figure 4: Latent extrinsics can be interpolated successfully; leftmost and rightmost columns are images from the multi-lllmmiation dataset, and intermediate images are obtained by linear interpolation on the latent extrinsics (light-dependent representations), then decoding. Note how the light seems to "move" across space.
relighting, we also report results for modified versions of the baselines trained without additional supervision. For SA-AE, we train their light estimation model and relighting model end-to-end by removing the loss from light supervision. For S3-Net [5T], we simply remove the depth from the model's 's input.
Without color correction, only lught-supervised SA-AE slightly outperforms our model, while all other baselines are significantly worse. The unsupervised version of SA-AE performs much worse because their light estimator struggles to distinguish the extrinsic from the intrinsic components. Specifically, SA-AE also parameterizes the extrinsic as a lower-dimensional representation but without the constrained scaling that our model uses. As result, the estimated extrinsic from their unsupervised model also carries intrinsic information, and one can see "leaks". S3-Net performs worse in both versions since they concatenate input and reference images before feeding them into the models, which significantly affects the model's generalization ability, especially during test time when we use images from different scenes as references.
On color-corrected images, our approach outperforms all methods, including the siph--supervieed version of SA-AE, indicating that, up the constant color drift, our extrinsic estimation network is at least as good as, or even better than, a light estimation network trained with supervision. Removing the denoising setup from our model (a = 0) results in worse performance in both cases due to inferior semantic scene understanding. We additionally provide ablation studies on the choices of a in Table and find a = 5e 3 produces the best results.
Each image in the multi-illumine iltiimmlmiinnionn dataset shows a chrome ball, which gives a good estimate of an environment map for that image. Correctly rendering the effects of lighting changes on these chrome balls appears to be extremely difficult; the changes are substantial, and concentrated a small region of the image (so correct representation of these changes has little effect typical image losses). Figure 3 a crop of our results around this chrome ball. Our method represents these changes well; we aware of no other results reported for this effect. Compared to other approaches, our model accurately preserves the room layout, even in cases of extreme light changes.
Unlike classical rendering models that use specific parameterized form to represent extrinsics, framework learns an implicit extrinsic representation. However, we can still parameterize the learned extrinsic representation to create new light sources. In Figure 4 we demonstrate this capability by rendering images using interpolated extrinsic representations.
Relighting synthetically relighted images from StyLitGAN: StyLitGAN [9] is a recent method that can produce multiple illuminations of single generated room scene by manipulating StyleGAN latents appropriately. In the multi-llumiintion dataset, reference light and target images tend to share a strong spatial correlation in light patterns. In contrast, StyLitGAN generates extremely challenging images where very significant changes in lighting occur. Furthermore, StyLitGAN images have visible luminaires. To relight the input, the model must infer high-level concepts rather than simply copying the spatially corresponding light patterns from the reference. We train our model using StyLitGAN images to evaluate generalization qualitatively (quantitative evaluation would be of dubious value, because StyLiGGAN images are generated rather than real). Figure shows results. Notice how our method successfully relights from references, achieves brighter illuminations by turning on luminaires (here bedside lights), achieves darker scenes by turning off luminaires, and is somewhat less inclined to invent luminaires than StyLitGAN is. The model knows that light must come from somewhere, and how the effects of light are distributed.
Zero-Shot Relighting: In Figure we show our model's 's strong generalization by applying the model solely trained
datasct-withoue additional training or fine-tuning- to
7

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StyleGAN
StyLiGAN Relight
StyleGAN Generation
StyLitGAN Relight
Generation
Ref
Ours
Ref
Ours
Figure 5: Qualitative results for relighting interior scenes using relighter trained images obtained from StyLitGAN (which produces multiple illuminations a generated scene). StyLitGAN has strong tendency to increase decrease illumination by adjusting luminaires, typically bedside lights but also light coming through French windows, etc. On the left, where the reference lighting tends be brighter and more concentrated, notice how for the two top images, our relighter has identified and "turned up" the bedside lights; for the third, it has resisted StyLitGAN's tendency invent helpful luminaires (there isn't bedside light where StyLitGAN imputed one, close inspection shows). On the right, where the reference lighting is much more uniform, our relighter has achieved this by "turning down" bedside lights. This an emergent phenomenon; the method is not supplied with any explicit luminaire model or labeled data.
Input
Ref
Relight
Input
Ref
Relight
Input
Ref
Relight
Figure Zero-Shot Relighting. Our relighting model, trained only on the multi-illumination dataset, generalizes well to duitt--dittiibution images, as shown the IIW dataset (first row) and StyleGAN images (second row). It accurately infers scene geometry and lighting. Note that it identifies and turns on the bedside lamps in StyleGAN images despite having training in bedroom images. This demonstrates the model's strong generalization ability and the model clearly "knows" something about light sources.
relight IIW and StyleGAN- images. Despite the significant distribution shift in lighting patterns and room setup, our model accurately identifies luminaires and relights images.
4.3 Zero-shot albedo evaluation
Constrained scaling allows us to infer albedo without any decoding (and without any albedo data!)
by setting 0 during inference. We benchmark these albedo estimates using the WHDR metric the IIW [S] dataset (Section 2). We use WHDR because it is widely used and allows comparisons, but
existing literature records significant problems in interpreting the measure [16 6 29]. Among other irritating features, the metric seems to prefer odd colors, and can be hacked by heavily quantized
albedo maps. As is standard, obtain lightness by averaging R, G. and B albedo and compute relative lightness of two pixel locations i1, d2 by comparing to confidence threshold 8:
1 if R,, Ri, 1 11+5
(R) =
if R Rd > 1 E otherwise
2
(11)

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Methods
labels
Flat
Tune 6
WHDR
WHDR
Intrinsic Diffusion 29
CG
No
No
22.61
0.1
optimal
Intrinsic Diffusion 29
CG
Yes
Yes
17.10
w/
w/o
F
w/o
Inverser Render 52
No
No
No
21.40
le-2
17.64
28.97
15,81
19.09
BBA 16
No
No
Yes
17.04
5e-3
18.93
31.81
16.02
19.53
Ours
No
No
No
28.97
le-3
18.00
29.77
15.84
19.13
Ours Ours
No
No
Yes
19.09
5c-4
18.04
29.62
15.85
19.12
No
Yes
Yes
15.81
Table 4: We conduct ablation experi- ments to assess the impact of on the quality of albedo. "w/F" and "w/o F" denote post-processing images with and without flattening [10), respectively. The setting of i = 0.1 and w/o F is the most affected by a. Despite this, values of achieve high performance in our optimal configurations.
Table 3: We benchmark our albedo esimation on test set of IIW dataset [5] and compare with others, though the reliability has been questioned by recent papers [[6]. Flat denotes postprocessing images with flattening [TO]- Despite our model never being trained on albedo maps CG data, our best configuration significantly outper- forms all other methods suggesting our model learns high-quality intrinsic representations
Input+
Ours + Intrinsic
Input + Flatten
Ours + Intrinsic
Input
Flatten
Ours
Flatten
Diffusion
Input
Ours
Flatten
Diffusion
IA
Figure 7: Qualitative Comparison of Emergent Albedo from Latent Intrinsics on the IIW Dataset. Although our model has never been trained on any albedo-like maps, it effectively removes the effects of external light and dark shadows from the input. In contrast, Intrinsic Diffusion 22 a supervised method trained on large computer graphics data, often produces color-drifted estimations, likely due to the domain shift between CG data and real images. Observe the subdued lighting around the mirrors (top row, right) in our recovered albedo. Also, pay attention to all the details inside the refrigerator, which are visible in our recovered albedos (bottom row; right) compared to intrinsic diffusion. For comparison, we also display naive flattening (in the second column), which by itself cannot effectively reduce the strong lighting effects.
The resulting classification (one lighter than two: two lighter than one: equivalent) then compared to human annotations using the confidence score w; for each annotation pair. We report WHDR on the IIW test split in Table 3 to facilitate comparison with other approaches. Since our model is not trained with any albedo maps or computer- gentrrted images, we need to adjust the threshold for the optimal performance. Following prior work, we optimize on the training split, which significantly improves our performance from 28.97 to 19.09. Additionally, we enhance our performance by post- processing our albedo map using flattening 10. an optimization technique to further reduce color variations. With this improvement, our results reach 15.81, substantially outperforming the intrinsic diffusion model [29]. a diffusion-
albedo regression model trained on computer graphics data. In Figure 7 we show some qualitative comparisons to intrinsic diffusion. We observe that our method effectively removes external lighting effects and does not suffer from color drift due to domain gap unlike intrinsic diffusion, which is trained on CG data.
Sensitivity to light changes: Albedo are scene properties that are independent of lighting changes. In Figure. 8, we qualitatively assess this characteristic by varying lighting conditions, comparing our approach with the -tate-of-the-art supervised method, Intrinsic Diffusion 22) Our method demonstrates consistent and accurate estimations that remain stable even under extreme lighting variations. In contrast, Intrinsic Diffusion [29] shows significant deviation from the natural color distribution and are sensitive to lighting changes.

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Image
Intrinsic
Diftusion
Ours
Figure 8: Qualitative comparison of albedo stability under varying lighting conditions. Images shown are from the multi-illmmintiion dataset test split. The top row features images under different lighting environments. The middle row presents estimated albedos obtained from Intrinsic Diffusion [29], while the bottom row shows the recovered albedos from the latent intrinsic representation. Intrinsic Diffusion has large color drift and is sensitive to changes in lighting. In contrast, the albedos recov- ered from latent intrinsics remain stable under lighting changes, even in extreme conditions.
Discussion, Limitations and Future Work
Our method presents an important advancement in image relighting by demonstrating that intrinsic properties such as albedo can emerge naturally from training on relighting tasks without explicit supervision. This finding simplifies the relighting process, eliminating the need for detailed geometric and surface models and enhancing the model's s ability to generalize across diverse and unseen scenes. By encoding scene and illumination properties as latent variables, achieve accurate and flexible relighting. Our findings will have implications for various fields such as virtual reality and cinematic post-production. This approach reduces the learning process's complexity and offers a new perspective on designing deep learning models to capture and utilize intrinsic scene properties. These findings can guide future research toward a more efficient and scalable relighting approach, encouraging the development of models that can handle various lighting conditions and scene complexities.
The current taxonomy of surface intrinsics trrrinils-ttpiccllll... depth, normal, albedo, and perhaps specular albedo and roughness- quite limiting (compare human language for surface properties [4].. Our method, which computes latent intrinsic and extrinsic representations from images and combines these to transfer lighting conditions across scenes, captures physical concepts like luminaire and albedo without explicit physical parametrization. This ability to represent significant image effects without choosing a surface model offers substantial flexibility.
However, method has several limitations. It relies on pairs of relighted data captured in the same scene, which can be resource-intensing to obtain. Additionally, does not cope well with saturated pixel values common in LDR images. The intrinsic information being latent is another limitation since many applications require explicit intrinsic information like depth and normals.
Nonetheless. there is good evidence that explicit intrinsic information can be extracted from our latent intrinsics. Our method clearly "knows" albedo, and this information can elicited without examples. Similarly, it "knows" something about luminaires, such as their locations and effects. It intriguing to speculate that it "knows" other information relevant relighting, such as depth or surface microstructure. Future work will pursue this line of inquiry and also focus on developing purely unsupervised framework to infer intrinsic and extrinsic properties from collections of in-the- wild images. This will include refining probing techniques for better extraction of explicit intrinsics and identifying additional intrinsic properties crucial for relighting that not align with the current taxonomy. We believe this will improve the applicability and robustness of our approach, making it suitable for wider range real-world scenarios.
Acknowledgment
AB thanks Stephan R. Richter for the discussions that led to the consideration of intrinsic images as latent variables. This material in part based upon work supported by the National Science Foundation under Grant No. 2106825, and by gift from Boeing.
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A Experiment Details
Training Details We train our model with batch size of 256 for 1,000 epochs using the Adam optimizer, with a constant learning rate of 2e-4 and a weight decay ratio of le-2. To improve the semantic representation, we corrupt images with Gaussian noise during the first 400 epochs and follow Karras et al. [25] to sample the standard deviation a with In(o) (.21,, 1. 1.2?). In the later
600 epochs, we turn off the Gaussian noise to focus on enhancing the image quality, We train our model with 4,400 and a complete training requires 40 hours.
Model Details Our autoencoder employs U-Net architecture, incorporating residual convolutional blocks as the fundamental components. Each block is composed of two convolutional layers, group normalization, and a nonlinear activation function. The structure specifies [1, 2. 4, 4, blocks at each resolution level, starting from a resolution of 256, with the resolution halving after each level. The corresponding configurations for latent channels at these levels are [32, 64, 128, 128, 256, 512].
The intrinsic features, denoted as S are gathered from the output of the final block at each resolution level, starting from resolution of 128x 128 down the bottleneck. For generating extrinsic features L' multiple MLP layers are applied to the bottleneck features of the encoder, followed by averaging across all spatial features. We limit the channel number of the extrinsic features 16 to prevent them from conveying hiquefryy components.
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Input
Ref
Ours
Target
Input
Ref
Ours
Target
Figure 9: We visualize more examples for the image relighting task in dataset [38 Right: Zoomed-in view the chrome ball used as a probe to evaluate detail preservation in environment map.
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Reviews:
[{"review_id": "cooVaePW5g", "reviewer": "Reviewer_VUQQ", "summary": "The paper proposes a 2D relighting pipeline based on latent space manipulation, which is purely data-driven without explicit intrinsic representations such as geometry and materials. Given a single image input, the proposed model recovers latent variables representing scene intrinsic properties and latent variables representing lighting, enabling applications like 2D relighting based on a single reference image and albedo estimation. The proposed methods are validated on a held-out dataset, demonstrating its generalization capability and precision in real-world scenarios.", "questions": "What if the authors use the trained model to test some unseen images on the internet? Will the model still show good relighting ability? Can the checkpoint trained on the Multi-illumination dataset be used to infer StyLitGAN images? Some results would be appreciated.", "limitations": "The limitations have been well discussed by the authors in the paper.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper is well-written and easy to understand.\n2. The idea of relighting images with latent latent intrinsic and without explicit explicit intrinsic representations is interesting and novel. The paper shows impressive results on the test datasets and outperforms the baselines for relighting and albedo estimation.\n3. The proposed method doesn't need explicit lighting supervision and is purely data-driven, which indicates the method may have the potential to be scaled up in the future.", "weaknesses": "1. using latent intrinsic to relight means the users lose the ability to control the relighting in a fine-grained way. For example, the user can't precisely control the lighting intensity and directions.\n\n2. The proposed method doesn't show enough generalization ability. In Sec. 4.2, the authors first mention that they train the model on the Multi-illumination training dataset and then test it on the Multi-illumination test dataset. Then, when the authors validate the method on StyLitGAN images, the authors mention that they train the model again on StyLitGAN images. (as said in L213). It seems to mean that the trained model can't be used to test out of the distribution images directly. To show enough generalization ability, the model should be able to test on unseen real images directly after training ends.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate Reviewer VUQQ's feedback and are grateful for their recognition of the novelty of our proposed method, our approach to unsupervised training, and the quality of our results and paper writing. We now address the specific concerns and questions raised by the reviewer.\n\n\n**Concern: Lack of Fine-Grained Control in Relighting because of latent lighting representation**\n\nWhile it is true that using latent lighting representation for relighting may limit the ability to achieve fine-grained control over specific lighting parameters such as intensity and direction, this limitation is not unique to our approach. Current explicit parametric methods, like spherical harmonics or spherical Gaussians, also face challenges in providing precise control over these aspects, often due to the complexity and computational cost involved.\n\nOur approach, however, offers a significant advantage by simplifying the relighting procedure. By learning a high-dimensional latent representation, we bypass the need for detailed manual tuning of lighting parameters, making the process more efficient and accessible. Additionally, we have demonstrated the ability to interpolate in the latent space to achieve various lighting effects, as shown in Figure 4. This capability suggests that while explicit fine-grained control is limited, our method provides a practical and scalable solution for many real-world applications where such precision may not be critical. Future work will explore ways to enhance control, potentially by mapping latent representations to explicit lighting parameters for users who require finer-detailed adjustments. \n\n\n**Concern: Generalization Ability**\n\nWe respectfully disagree with the reviewer's assessment regarding the generalization capabilities of our model. Our paper demonstrates two key aspects of the model's generalization ability, which we detail below:\n\n1) **Generalization to Unpaired Images:** While our model is trained on paired images captured under the same scene, we evaluate its performance on unpaired images. This is illustrated in the first two columns of Figure 3, where the input and reference images originate from different scenes. We opted for paired training data due to practical scalability considerations, as using unpaired data necessitates the calibration of exact extrinsic lighting conditions across different scenes. Our model significantly outperforms comparable approaches in accurately rendering relit patterns while preserving environmental details.\n\n2) **Generalization to Out-of-Distribution Images:** We trained our model on the Multi-Illumination dataset and directly tested it on the IIW dataset for albedo estimation, without any additional training or prior exposure to the IIW dataset. Despite the considerable distribution shift between these datasets, our method consistently outperforms other approaches in albedo estimation, even without using any albedo-like supervision.\n\n\nTo further substantiate our model's generalization capability, we provide additional results as suggested by the reviewer. We tested the relighting performance of models trained on different datasets, including demonstrating plausible relighting of StyLitGAN and IIW images using models trained on the MIT Multi-Illumination dataset. These experiments show the model's ability to generalize to unseen images using two sources of extrinsics: (a) estimating extrinsics from in-distribution images and (b) estimating extrinsics from out-of-distribution images. Visualizations of these results are included in the attached PDF.\nWhile these results demonstrate plausible relighting, we acknowledge that they are not perfect, primarily due to the distinct nature of the distributions. The MIT Multi-Illumination dataset does not feature visible luminaires or light sources, whereas StyLitGAN images may include visible luminaires, with the quality dependent on StyleGAN's generative capabilities. We believe that expanding our training dataset will enhance rendering quality, as our approach can easily scale due to its lack of reliance on supervision.\n\nWe appreciate the reviewer's feedback and questions, and we will incorporate these clarifications into the final version of our manuscript for improved clarity.", "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": "GuvEvFegMt", "reviewer": "Reviewer_t1Cn", "summary": "This paper proposes a fully data driven method to perform scene relighting that does not require any groundtruth lighting supervision. As such, it allows for scene relighting by training only on real paired images of the same scene under different illuminations rather than requiring synthetic data that contains the groundtruth lighting environments, which improves the generalizability and performance on real-world scenes. The method involves learning both intrinsic and extrinsic features from each scene, where the intrinsics represent the albedo and geometry and the extrinsics represent the lighting. The authors design a fairly simple but well thought out pipeline with several loss functions that encourage the intrinsics to be consistent for the same scene under different illumination, self reconstruction losses that reconstruct the original image using its own intrinsic and extrinsic features, and relighting loss functions that involve swapping extrinsic features between image pairs to perform scene relighting. The authors also design a constrained scaling mechanism that prevents too much information outside of the lighting information in the target image from being transferred to the source image. Experiments demonstrate that the relighting performance greatly outperforms other unsupervised methods that don't require groundtruth lighting and is comparable or slightly better depending on the metric than the existing state of the art supervised scene relighting methods. Ablations for the constrained scaling are also provided, which is helpful to understand the importance of this technical contribution.", "questions": "As I mentioned above, the experiments in this paper are quite thorough and I'm mostly interested to see whether the estimated albedo is consistent for the same scene under different illuminations. It would be good to compare this with other methods as well. Otherwise, please add the missing citations for the image-based relighting domain as there are many recent methods in that area.", "limitations": "The limitations section is well thought out and touches on many important points. Relighting often does not have significant negative societal impacts since only the illumination is edited, but as with any other editing work there is always the potential to generate fake content. Perhaps this could be mentioned in the potential negative societal impact section.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Experiments in the paper are quite thorough and demonstrate state-of-the-art performance for scene relighting, most notably being comparable to methods that require full light supervision and greatly outperforming unsupervised methods. Ablation studies on the constrained scaling are also helpful for understanding this contribution. \n\nThe model and pipeline itself is quite simple and straightforward yet effective and easy to follow. The loss functions align with intuition and are carefully designed. \n\nThe ability to estimate albedo from a scene is an important feature that has many implications for downstream tasks, and the authors achieve state-of-the-art performance for albedo estimation.", "weaknesses": "One experiment that would be interesting to see is the level of albedo consistency. Since the authors design a loss function that encourages intrinsic features of the same scene under different illuminations to be similar, it would be good to see how well that design performs empirically, both quantitatively and qualitatively. \n\nSome citations are missing for the image-based relighting domain, namely: \n\n1. Towards High Fidelity Face Relighting with Realistic Shadows (CVPR 2021)\n2. Learning to Relight Portraits for Background Replacement (SIGGRAPH 2021)\n3. Face Relighting with Geometrically Consistent Shadows (CVPR 2022) \n4. Lumos: Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation (SIGGRAPH Asia 2022)\n5. DiFaReli: Diffusion Face Relighting (ICCV 2023)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate Reviewer t1Cn's feedback and are grateful for their recognition of the simplicity and effectiveness of our proposed method, the thoroughness of our experiments, and our approach to unsupervised training. We are pleased to see the acknowledgment of our design choices, the clarity and straightforwardness of our paper's writing, and the practical implications of our method for downstream tasks. We now respond to the reviewer's questions.\n\n\n**Regarding Consistency of Our Albedo Predictions**\n\nWe conducted an experiment to evaluate the consistency of albedo predictions under varying lighting conditions. Specifically, we estimated albedo for the same scene under different lighting settings, as provided in the MIT dataset. Qualitative visualizations are included in the attached PDF. For comparison, we also evaluated the state-of-the-art supervised intrinsic estimator, Intrinsic Image Diffusion (IID) [1]. Notably, IID requires 10 independent estimations followed by averaging to produce a single image estimation. Due to the computational expense of this process, we assessed albedo consistency under only five different lighting settings. \n\nOur method, on the other hand, produces consistent results with just a single forward pass. Our findings demonstrate that our approach achieves stable albedo estimation despite not utilizing any albedo-like maps as supervision. Also, while IID often exhibits significant color drift due to its reliance on synthetic training data, our method maintains color fidelity, as shown in our qualitative figures. To quantify the albedo stability under varying lighting conditions, we report the mean deviation and standard deviation on albedo map, normalized per scene,\n\n|Results |Mean Deviataion| Standard Deviation|\n|---|---|---|\n|IID | 0.054| 0.063|\n|Ours | **0.046**| **0.053**|\n\nOur method produces more stable albedo estimations under varying lighting conditions compared to the supervised IID approach.\n\n\n[1] Kocsis et al., Intrinsic Image Diffusion for Indoor Single-view Material Estimation. CVPR 2024.\n\n**On Missed citations** \n\nThank you for bringing these papers to our attention. We will ensure that these citations are added to the related work section in our final draft.", "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": "CD4VAOeqKe", "reviewer": "Reviewer_n7pv", "summary": "This paper proposes a fully data-driven relighting method applicable to images and real scenes. The approach requires only paired images of the same scene under different illumination as inputs. The trained model can also produce albedo-like maps even though it is not trained without such supervision. The experimental results show that it significantly outperforms unsupervised baselines while being competitive with baselines that use supervision.", "questions": "Please see the weaknesses.", "limitations": "The limitations and potential negative societal impact are discussed properly.", "rating": 7, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The idea and the overall achieved goal are quite novel. Training models that can do relighting without any supervision on ground-truth intrinsic properties (albedo, depth, normal, lighting, etc.) is quite challenging and useful.\n\n2. The presentation is clear. The overall methodology is quite straightforward, with all the details (inputs, outputs, intermediate steps, losses) well-explained and self-contained.\n\n3. The experiments are sufficient.", "weaknesses": "1. The paper proposes several novel regularizations, e.g. Eq 6 and Eq 9, which are not ablated. It would be good to see how much these losses improve the model's accuracy.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate Reviewer n7pv's recognition of the novelty and usefulness of our work, particularly in training models for relighting without supervision on ground-truth intrinsic properties. We are also grateful for the positive feedback on the clarity and straightforwardness of our presentation, and the comprehensiveness of the methodology and experiments.\n\n**Regarding Ablation of regularization terms**\n\nThank you for suggesting additional ablation experiments. In response, we trained a model using our optimal configuration ($\\alpha=0.5$) for the relighting experiment, excluding the regularization terms in Eq. 6 and Eq. 9 from the training objectives. The results, presented in the attached tables, clearly show that the proposed regularization terms significantly improve performance in both relighting and albedo estimation tasks.\n\n|Relight Config |Raw Output|Color Correction|\n|---|---|----|\n| | RMSE SSIM| RMSE SSIM| \n|w/ regularization | 0.297 0.473 | 0.222 0.571|\n|w/o regularization | 0.315 0.462 | 0.232 0.550|\nTable 1: Ablation experiments of regularization term for image relighting. \n\n\\_ \n\n|Albedo Config |$\\delta = 0.1$|optimal $\\delta$|\n|---|---|----|\n|w/ regularization | 31.81 | 19.53 |\n|w/o regularization | 32.56 | 19.67 |\nTable 2: Ablation experiments of regularization term for unsupervised albedo estimation.", "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:
We thank the reviewers for their positive and constructive feedback on our paper. Reviewers consistently highlighted the "novelty" [VUQQ, n7pv] of our approach, particularly appreciating the method's ability to perform relighting using latent intrinsic properties without explicit representations, described as "interesting and novel" [VUQQ]. The paper was noted for being "well-written and easy to understand" [VUQQ], with a "clear" [n7pv] and "straightforward" [n7pv] methodology. Reviewers also commended the "thorough" [t1Cn] and "sufficient" [n7pv] experiments, which demonstrate "state-of-the-art performance" [t1Cn] in scene relighting and albedo estimation and "outperforms baselines" [VUQQ, t1Cn]. We are extremely delighted to receive such feedback. Thank you!

Further positive points include the "simplicity and effectiveness" [t1Cn] of our model and pipeline, the careful design of loss functions that "align with intuition" [t1Cn], and the important feature of estimating albedo, which has significant "implications for downstream tasks  [t1Cn]." The method's "data-driven" nature, which "doesn't need explicit lighting supervision," suggests potential for future scalability [VUQQ]. Overall, the reviewers appreciated the challenging and useful nature of training models for relighting without supervision on ground-truth intrinsic properties [n7pv].

With the clarifications provided (detailed responses to each reviewer below), we hope our paper stands as a meaningful contribution to the community. Here is a summary of our responses to the reviewers' questions and concerns, along with summaries of results and figures included in our rebuttal PDF:

- For VUQQ: We provide visualizations of relighting unseen images to demonstrate the generalization and robustness of our approach.

- For t1Cn: We offer results on albedo prediction and its variance under various lighting conditions to demonstrate the stability of the learned intrinsic representation.

- For n7pv: We present an ablation study excluding regularization losses, demonstrating their necessity for improved relighting and albedo prediction.

Meta-review: This paper initially received unanimous positive scores. Reviewers appreciated the clear presentation, the thoroughness of the evaluation, and they praised the approach to relighting using the emergent scene intrinsics. Reviewers also raised concerns related to, e.g., generalization to different datasets, consistency of albedo predictions, and the effect of regularization terms. The authors provided a rebuttal, and after the reviewer discussion all reviewers agreed that the paper should be accepted. The authors should include the clarifications from the rebuttal in the paper.
Decision: Accept (spotlight)