Rethinking Unsupervised Cross-modal Flow Estimation: Learning from Decoupled Optimization and Consistency Constraint
Authors: Runmin Zhang
Jialiang Wang
Si-Yuan Cao
Zhu Yu
Junchen Yu
Guangyi Zhang
Hui-liang Shen
Venue: ICLR 2026 Poster (2026)
Keywords: Cross-modal flow estimation
Unsupervised learning
Multimodal and multi-spectral images
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A novel unsupervised cross-modal flow estimation framework that integrates a decoupled optimization strategy and a cross-modal consistency constraint.

Abstract: This work presents DCFlow, a novel self-supervised cross-modal flow estimation framework that integrates a decoupled optimization strategy and a cross-modal consistency constraint. Unlike previous unsupervised approaches that implicitly learn flow estimation solely from appearance similarity, we introduce a decoupled optimization strategy with task-specific supervision to address modality discrepancy and geometric misalignment distinctly. This is achieved by collaboratively training a modality transfer network and a flow estimation network. To enable reliable motion supervision without ground-truth flow, we propose a geometry-aware data synthesis pipeline combined with an outlier-robust loss. Additionally, we introduce a cross-modal consistency constraint to jointly optimize both networks, significantly improving flow prediction accuracy. For evaluation, we construct a comprehensive cross-modal flow benchmark by repurposing public datasets. Experimental results demonstrate that DCFlow can be integrated with various flow estimation networks and achieves state-of-the-art performance among unsupervised approaches.

Published as a conference paper at ICLR 2026
RETHINKING UNSUPERVISED CROSS-MODAL FLOW
ESTIMATION: LEARNING FROM DECOUPLED OPTI- MIZATION AND CONSISTENCY CONSTRAINT
Runmin Zhang' Jialiang Wang! Si-Yuan 2.34x Zhu Yu
Junchen Yuu Guangyi Zhang'
Hui-Liang Shen
"College of Information Science and Electronic Engineering, Zhejiang University
"Ningoo Global Innovation Center, Zhejiang University Tech University
"Jinhua Institute of Zhejiang University
(runmin.zar n.zhang, cao.siyuan) ju www.ju......om cn
ABSTRACT
This work presents DCFlow, novel self-supervised cross-modar flow estimation framework that integrates a decoupled optimization strategy and a cross-modal consistency constraint. Unlike previous unsupervised approaches that implic- itly learn flow estimation solely from appearance similarity, we introduce a de- coupled optimization strategy with task-specific supervision address modality discrepancy and geometric misalignment distinctly. This is achieved by collab- oratively training a modality transfer network and flow estimation network. To enable reliable motion supervision without ground-truth flow, we propose a geometry-aware data synthesis pipeline combined with an outlier-robust loss. Additionally, we introduce a cross-modal consistency constraint to jointly opti- mize both networks, significantly improving flow prediction accuracy. For eval- uation, we construct comprehensive cross-modal flow benchmark by repur- posing public datasets. Experimental results demonstrate that DCFlow can be integrated with various flow estimation networks and achieves state-of-the-as performance among unsupervised approaches. The source code available at https:///ittuu: com RM- wwwng/DCCaFFF
INTRODUCTION
Cross-modal flow estimation aims to establish pixel-wise correspondences between images cap- tured from different modalities. It crucial for various vision tasks, including multi-modal image fusion (Liu et al.) 2025), image restoration (Zhang et al., 2025), and depth estimation (Guo et al.) 2023). Due to the difficulty of acquiring cross-modal ground truth flow real-world scenarios, un- supervised cross-modal flow estimation, which does not require such annotations for training, has attracted increasing attention.
Existing unsupervised approaches typically address this task by minimizing appearance discrep- ancies between image pairs. To achieve this, a modality transfer network usually employed to translate images from one modality to another. For instance, NeMAR (Arar et al. (020) simultane- ously optimizes the modality transfer and flow estimation networks, while others Wang et al, 2022] Xu al. (022) adopt two-stage pipeline for separate training. Despite their different strategies, these methods share a fundamental limitation in implicitly learning flow estimation solely through appearance alignment. Consequently, they struggle particularly in textureless regions repetitive structures, and their performance substantially degrades under large viewpoint changes due to the lack of direct flow supervision. This naturally raises question: can we introduce reliable flow supervision using only unaligned cross-modal image pairs? Recent studies (Watson et al. 2020 Aleotti al. 2021 Han et al.) 2022. Liang et al. (023) have explored generating synthetic motion labels from single images via geometry- aware data synthesis, showing promising results in mono-
"Corresponding author.

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Published as a conference paper at ICLR 2026
(a) Overall training framework of DCFlow
(b) Comparison of different approaches
Decoupled Optimization via Task-specific Loss
Joint Optimization via Consistency Constraint
MS2 (RGB-T) 3,46
T
F
RNS (RGB NER) 1.90
T
(NIR-T)
+
T
F
Explicit Row Supervsion
Consstency Enfordin
Flow Data Synthesis from Single Images
Spatial Transform
MS2 (RGB-NOR) 0.96
"VTD (RGB-T) 3.65
Modality transfer
estimation
Trainable
Frozen
DCRlow MENINA
CrossRFFT NeMAR
UMF-CNGR
(a) Overall training framework of DCFlow, which integrates a decoupled optimization strategy and a cross-modat modal consistency constraint. (b) Comparison of the cross-modal flow estima- tion accuracy five datasets. EPEs (endpoint errors) of different approaches are reported. Our DCFlow achieves state-of-the-art performance.
Figure 1:
modal settings. However, whether such mono-modal supervision can benefit cross-modal scenarios, and how to effectively exploit it, remain largely unexplored.
To bridge this gap, this paper, we propose DCFlow, novel self-supervised framework for cross- modal flow estimation that introduces explicit motion supervision into the training process. In line with existing approaches, our framework consists of a modality transfer network and flow esti- mation network. As shown in Fig. [a), DCFlow integrates decoupled optimization strategy with task-specific supervision to train the two networks separately, as well as a cross cross-modal consistency constraint jointly optimize them. Within this framework, we leverage complementary sources of motion supervision through geometry-aware synthesis from single images and spatial transfor- mations cross-modal pairs. In this way, DCFlow learns in fully self-supervised manner by generating flow supervision through data synthesis, using only unaligned cross-modal images.
The decoupled optimization strategy separates the overall task into modality transfer and single- modal flow estimation, forming collaborative process where each network facilitates the optimiza- tion of the other. More importantly, it allows the flow network to be trained using only mono-modal flow supervision, while still contributing to accurate cross-modal alignment. To enable such super- vision, we adopt a geometry-avee
data synthesis pipeline that generates dense flow labels from single-view images. Considering that the synthetic data inevitably contains noise, we further adopt an outlier-robust loss to adaptively filter unreliable supervision based on residual magnitudes. These innovations enable effective flow network training without real-world labels, facilitating the decou- pled training scheme. Compared the conventional appearance-bast optimization, our decoupled optimization strategy reduces the endpoint error (EPE) by 15.43 on the MS² (RGB-T) dataset.
Furthermore, we propose a cross-modal consistency constraint to jointly optimize both the modality transfer and flow estimation networks. Specifically. we apply spatial transformations to cross modall image pairs, and enforce consistency between flow predictions before and after transformations. This constraint encourages direct learning of pross-modal flow, and strengthens the mutual promo- tion of the two networks, improving the EPE on the MS² (RGB-T) dataset from 4.81 to 3.46.
By integrating the above insights, DCFlow supports effective training of modern flow estimation networks such as RAFT (Teed & Dengl (020) and FlowFormer (Huang et al. 2022), offering a general and effective network-agnostic training framework for cross-modal flow estimation. For comprehensive evaluation, we repurpose public multi-modal datasets by projecting LiDAR points obtain ground-trute -ouuh flow, creating five diverse datasets covering RGB, mear-infrared (NIR), and thermal modalities. As shown in Fig. [b), DCFlow significantly surpasses existing unsupervised and large-scale pretrained approaches. In summary, our main contributions are as follows:
We propose DCFlow, general and network- self-supervised training framework for cross-mode cross-modal flow estimation. DCFlow achieves state-of-the-ar performance among all unsuper- vised approaches.
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We introduce decoupled optimization strategy that enables single-modal flow supervision to benefit cross-modee flow estimation, supported by a geometry-aware data synthesis pipeline and outlier-robust loss to reliably provide such supervision from single-view images.
We devise cross-modal consistency constraint to facilitate effective joint optimization of the modality transfer and flow estimation networks, significantly enhancing flow estimation accuracy.
We construct comprehensive cross-modal flow benchmark by repurposing publicly available datasets, covering diverse modalities such as RGB, NIR, and thermal.
RELATED WORK
Cross-modal image matching. Cross-modal image matching aims to establish spatial correspon- dences between images from different modalities, and has wide range of applications (Jiang al., 2021b Li et al.
2024 Liu et al. 2024; Jiang et al. 2024). Traditional approaches (Shen et al, 2014 Kim et al. 2015) design cross- modall invariant descriptors, but often suffer from high compu- lational cost. Recently unsupervised deep learning methods (Arar et al.] 2020; Wang et al. 2022 Xu al. 2022) have been proposed, typically consisting of modality transfer network and flow estimation network. Due to the absence of ground-trute correspondences, these methods rely on gapearance-based supervision, which often leads ambiguity in textureless repetitive regions. Alternatively, approaches such as CrossRAFT (Zhou et al., 2022) and MINIMA (Ren et al. 2025) synthesize cross-modal data using multi-view RGB images with known ground-truth displacements to provide direct motion supervision. However, the cynthttic-t--eal domain gap limits their gen- eralization real-world scenarios. Besides, we note that recent work SSHNet (Yu al. 2025) introduces split optimization framework for cross-modal homography estimation, which is lim- ited modeling global transformations. In contrast, we tackle the more challenging problem of dense pixel-wise correspondence estimation across modalities, which has broader applicability in real-world scenarios.
Flow estimation. Starting from FlowNet (Dosovitskiy et al. 2015), various network architec- tures (Teed & Deng 2020; Diang et al. 2021a Huang et al. 2022) have been proposed under supervised learning, with most ofate-of-thh-ar methods adopting an iterative prediction paradigm based on cost volumes. In the unsupervised setting, prior work Yu et al., 2016) introduces bright- ness constancy and motion smoothness fundamental constraints. Subsequent approaches further enhance these ideas through specifically designed regularization strategies (Liu et al.) [2020] Luo al. 2021 Yuan et al.] 2024), or design domain adaption techniques enhance performance in adverse weather (Zhou et al.] 2023a b). Other methods (Watson et al. 2020) Aleotti et al.) 2021
Liang et al., 2023] (025) attempt synthesize training data from single-viee images. However, most of the above approaches are designed for and evaluated on RGB image pairs, highlighting the
urgent need for effective solutions tailored to real- world pross-modal scenarios.
METHOD
3.1 PRELIMINARIES
This work tackles the problem of cross-modal flow estimation in an unsupervised setting. Given cross-modal image pair LA and Ig from modalities and B respectively, our goal is train network (-() to predict the dense flow F FBZA from IB to IA. N(()) is typically decomposed into two components, formulated as
(1) where (-(-) is a modality transfer network with learnable parameters o, which transforms IA into modality B, and F-) a mono-modal flow estimation network with learnable parameters 0. Ex- isting unsupervised approaches generally rely on photometric losses between the warped source and the target image for training, expressed as
FEZA = WV = FTT
argmin Lph (W[TA.T. FBZA), IB), 0,0
where IA, = (Ix) is the modality transferred image, W(-) denotes the warping operation, and Lph is photometric similarity metric such as L, distance or SSIM. However, such supervision
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stable, and Published as a conference paper at ICLR 2026
Optimization of the estimation network
Modsilty tronster
Flow estimetion
Fass
Image warping
Trai vable
T
F
Frouen
F,
Fn
Weiglt sharing
Date proaweig
hetwark forwerd
Tows computation
(b) Optimization of modality transfer network
Cross-modal consistecyy constrint
FizA
haandfor
Fish
F
up
Fiza
FIZZ
J
Tunnion oftic
Figure 2: Schematic diagram of DCFlow, which incorporates a decoupled optimization strategy (a, b) and cross-modal consistency constraint (c). (a) Optimization of the flow estimation network Fo. where Fo is optimized using direct flow supervision from two-branch intra-modal synthetic data. (b) Optimization of the modality transfer network TG. where To learns to align modality I with IB via perceptual similarity. (c) Cross-modal consistency constraint, where both networks are jointly optimized by enforcing flow consistency under known spatial transformations.
inherently ambiguous in texture-less regions repetitive patterns, and lacks direct motion cues, often leading to unsatisfactory results.
Motivated by these limitations, believe that a potential improvementi is to introduce motion super- vision without using the cross-modal ground-truth flow. To achieve this goal, we propose DCFlow, novel training framework that provides explicit flow supervision without requiring any labeled cross-modal data. We first decouple the overall task into modality transfer and mono-modal flow estimation, thus enabling mono-modal flow supervision benefit cross-modal flow estimation. Fur- thermore, to ensure effective interaction between the two components, we introduce the cross-modal consistency constraint. This constraint facilitates collaboration between the networks, leading to im- proved performance. Fig. 2 illlustrates the overall training framework of DCFlow.
3.2 DECOUPLED OPTIMIZATION
DCFlow adopts decoupled optimization strategy for the modality transfer network To and the flow estimation network Fe. which address the modality discrepancy and geometric misalignment between the input image pair separately, illustrated in Fig. [a)).). Each network is trained with task-specific supervision, forming self-reinforcing process where improvements in one network provide better guidance for the other. Consequently, the training process becomes converges to more accurate results.
Flow estimation. Fig. a illustrates the optimization process of the flow estimation network. In this stage, the weights of the modality transfer network To are frozen. The objective is to provide direct motion supervision for the flow network Fo. addressing the limitations of implicit appearance- based supervision. Inspired by recent works (Zhang et al.) 2024 Yu et al.) (025), we adopt a two- branch intra-modal supervision scheme using synthetic data from each modality. It enables the flow estimation network to process inputs from two different domains simultaneously at the start of train- ing- Under the multi-task learning paradigm (Caruanal 1997). this setup progressively enhances the generalization of the network to cross-modal inputs, and facilitates convergence in self-supervised manner.
Specifically, we construct two training triplets (I I, FA.s) and (Ig, I, FB.s), where I' I'A/B de- notes the rendered novel view from I A/BB, and F A/B,S is the corresponding synthetic flow label. The

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The flow network is trained by minimizing the L1 distance between the predicted and synthetic flows from both branches, formulated as
data synthesis pipeline is detailed in Sec. 3.3
argmin (F(FA, (FA, FA,)) + (r(FB, FB.S)),
where the predicted flows are defined as FA Fo (T (T T&(A))) and FB = Fo IB), and the loss Cp is computed as
(F, Fs)
Fsll
(4)
We implement Fo using RAFT TTedd & Deng, 2020). iterative flow estimation architecture, and apply the loss over all intermediate predictions. Notably, DCFlow is agnostic to the choice of flow network, and supports alternative architectures, demonstrated in Table
Modality transfer. Fig. (b) illustrates the optimization process of the modality transfer network. In this stage, the weights of the flow estimation network Fø are frozen. The modality transfer network Tg is optimized to translate the input image IA into the appearance of modality
Specifically, estimated flow F22 between the cross-modal image pair is used to warp the trans- ferred image IA,T, producing the warped output I = W(LA.T, (B2A). The optimization objec- tive is then given by
argmin LT (TX,T: In),
(5)
where LT is defined as the perceptual loss (Johnson et al. 2016), formulated
CT IB) = Σ Av (TT,) (;
(6)
with (-(-) denoting the I-th layer of pretrained VGG network, and A the corresponding layer weight. The perceptual loss captures high-level structural and semantic similarity, and is less sen- sitive to spatial misalignments than pixel-wise metries like L1 distance. This makes robust supervisory signal for the modality transfer network, even when the estimated flow is imperfect, thereby guiding the training process toward desired convergence. We implement To using a U- Net (Ronneberger al., 2015) to preserve both fine-grained details and global contextual features.
Under this decoupled training strategy, the two networks are trained independently with stable su- pervision. Mcanwhile, their outputs mutually reinforce each other, driving the entire framework toward continuous performance improvement. Better still, this strategy enables the flow network to be trained using only mono-modal supervision, while contributing to cross-modal alignment, funda- mentally addressing limitations of appearance- based methods.
3.3 DATA SYNTHESIS AND OUTLIER- -ROUUST LOss
To provide reliable supervision for the flow estimation network, we propose a geometry-aware data synthesis pipeline with an outlier-robust loss. The data synthesis process aims to generate a novel view image I' and its corresponding synthetic flow Fs from a single input image I. To achieve this, we introduce lifting and reprojection technique that projects 2D pixels into 3D space and reprojects them into a virtual camera view. produces photorealistic and geometrically consistent image pairs under realistic motion patterns, providing dense, geometry-groundes supervision without requiring multi-view images with ground-truth flow label. Fig. illustrates the overall pipeline.
We first estimate depth map D using pretrained monocular depth model, such as UniDepth (Pic- cinelli et al. 2024). Each 2D pixel x = [] is projected into a 3D point [x, 1, z] via
sampled intrinsic matrix K
X D(x) -
For simplicity, we omit the homogeneous coordinate form. We then sample a virtual camera pose T E SE(3), and re-project the 3D points into the novel view as
~ K T
(8)
where x' = [u', u']T, Then, the rendered image I' is obtained by sampling the corresponding pixel values from and the synthetic flow Fş is defined as the 2D displacement between and x'.
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estimation
consstency checking
Masching rolutarniig
View toa transformatonn
Imaage wourping
M
Figure 3: Illustration of the geometry-awar data synthesis pipeline.
To identify occluded and invisible regions introduced by viewpoint changes, we adopt photomet- ric consistency check to generate a binary valid mask M, where M = 1 indicates valid pixels. Specifically, we warp the rendered image I' back the original view using Fs, and measure the photometric error against I. Pixels with photometric errors exceeding a threshold are marked as invalid and excluded from loss computation. The flow loss then defined
LF = M
Σ M(x)=
[[(x)
Fx()))
While the valid mask effectively excludes occluded and invisible regions, some pixels may still yield unreliable supervision due to rendering artifacts or depth estimation errors. These regions, such as distorted boundaries or thin structures, may contain valid flow but poor appearance quality, making them unsuitable for flow supervision.
To address this, in line with previous approaches (Wang et al. 2021 Keetha al. 2025 Ding al. 2025), we adopt an outlier-robust loss that further filters noisy supervision by discarding a small fraction of pixels with the highest residuals. Specifically, valid regions, and remove the top-7% pixels for loss computation. This ensures that the supervision focuses regions with both accurate motion and relatively high appearance fidelity. The outlier- robust loss is defined as
sort the per-pixel L₃ flow error within
CF - 12
Σ [F(x)) Fs(x))l1
(10)
XEO,
where Ω2 denotes the set of valid pixels after excluding the top-r% largest residuals. provides more robust training signal by avoiding noisy supervision caused by uncertain depth or texture distortions, thereby further improving the stability and accuracy of flow learning.
3.4 CROSS-MODAL CONSISTENCY CONSTRAINT
Though the decoupled optimization strategy ensures stable convergence, it does not explicitly teach the network to perform cross-modal flow estimation. Instead, the model relies on the generalization ability of two independently optimized networks. To overcome this limitation, we introduce cross- modal consistency constraint that jointly optimizes both networks by enforcing flow consistency under known spatial transformations, as illustrated Fig. cc).
Given a cross- modsl image pair (IA, IB) and the corresponding flow prediction FBZA, we apply random affine transformations (e.g., scaling, rotation, and translation) to both images, yielding the augmented image pair (IA, IB). Since the transformations are known, we can derive transformed flow Fo B2A from FBZA- The augmented pair is then passed through the full network pipeline, pro- ducing a new prediction F FBZA = (T (IA). Ig). Based on the assumption that flow predictions should remain geometrically consistent under known transformations, the joint optimization objec- tive is defined as
argmin Lr B2A, B2a): 0.0
(11)
This self-supervised constraint enables direct learning of cross-mout mrdal flow estimation, encouraging mutual adaptation between the two networks and thus improving final performance.
where Lp is the outlier-robust loss in Eq 10

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Published as a conference paper at ICLR 2026
Irput themnal
RGs (2D bunaformaion)
(30 Gaussian splating)
(Guomddoy-avcre syrtheak)
(Geom aware synthess)
Figure 4: Qualitative comparison of different synthetic flow data generation strategies.
3.5 OVERALL TRAINING ORJECTIVE
The entire training objective can be formulated as
argmin (FA, (F,, + XTCr(TK,, + Ac B2A Fizz),, 6.8
(12)
where ÀT and Åc denote the loss weights for the modality transfer and cross-modal consistency components, respectively. We note that these three losses are jointly optimized within single gradient descent step, with each loss imposed on its corresponding set of parameters. Once com- bined, the decoupled optimization allows each network to learn its specific task robustly, while the consistency constraint enhances collaboration between two networks for better cross-modal flow estimation, ultimately leading to stable and effective self-supervised training.
For implementation. set ÀT 2.0 and Àc 0.05. We train the entire network from scratch for 30, 000 iterations with a batch size 4. The cross-mode consistency constraint is introduced after 10, 000 iterations, allowing the model to produce reliable flow predictions before joint optimization begins.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTING
Datasets. We evaluate DCFlow on three public datasets, i.e., MS² (Shin et al. 2023). ) VTD (uuo et al. 2023), and RNS (Kim & Back 2025). These datasets provide multi-modal data including RGB, near-infrared (NIR), thermal (T), and LiDAR, and consist of multiple video sequences. To adapt them for cross-modal flow evaluation,
repurpose the data using the following proce- dure. We first resize and crop images across modalities to achieve consistent effective focal lengths, based on the provided intrinsic parameters. Then, we project LiDAR points from one image to another using the known extrinsic parameters, and compute the ground-trui flow from the 2D dis- placements of valid projected points. Due the inherent sparsity of LiDAR, the resulting flow labels are sparse. For dataset splitting, we use the first 80% frames in each video sequence for training and the remaining 20% for testing. Notably, the MS² dataset simultaneously captures RGB, NIR, and modalities, allowing us to construct three sub-datasets for comprehensive evaluation under different modality gaps. We denote these as MS? (RGB-T), MS² (RGB-NIR), and MS² (NIR-T).
Metrics. We report the endpoint error (EPE) and the flow outlier rate (F1). EPE measures the aver- age Le distance between the predicted flow and the coound-truth, while Fl denotes the percentage of pixels with EPE greater than both 3 pixels and 5% of the ground-truth magnitude. Lower EPE and values indicate better performance.
4.2 ABLATION STUDY
Ablation studies are conducted on the MS² (RGB-T) dataset unless otherwise specifically stated.
Training strategy. Table la presents the ablation study on different training strategies of DCFlow. We start with the appearance- based optimization baseline, where the entire network is trained using only the photometric similarity loss in Eq. 2 As shown, this strategy leads to poor performance, reflecting the inherent limitations of relying solely on appearance cues. We then evaluate our de- coupled optimization strategy, which separately trains the modality transfer and flow estimation

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Table Ablation studies of DCFlow on the MS² (RGB-T) dataset.
(a) Ablation study on training strategies.
(b) Ablation study on data synthesis strategies.
Training strategy
EPE
F1
Data synthesis strategy
EPE
F1
appearance-basee optimization
21.23
98.45
transformation
13.12
95.21
Decoupled optimization
5,80
57.18
3D Gaussian Splatting
5.11
63.12
Outlier-robust loss
4.81
51.39
Geometry-ave synthesis (Ours)
3,46
35.89
Cross-modal consistency constraint
3,46
(c) Ablation study on inaccurate depth estimation.
Depth quality
of Gaussian noise
scaling noise
Kernel size of blurring
EPE
Network output
3,46
35.89
Small degradation
0.1
0.1
3.81
40.66
Large degradation
0.2
0.2
14
4.00
49.08
(d) Ablation study different depth estimation networks.
Depth network
EPE
F1
Metric3D v2
3.74
39.41
Depth Anything v2
3,45
34,81
UniDepth (Default)
3.46
35.89
(e) Ablation study different flow networks.
RAFT
GMA
Flowerrmer
SEA-RAFT
Training strategy
EPE
F1
EPE
F1
EPE
F1
EPE
FI
Appearance-based optimization
21.23
98.45
24.27
97.39
29.22
99.43
23,93
98.66
DCFlow (Ours)
3.46
35.89
4.13
48.04
3.66
37.97
3.57
38.63
Table 2: Ablation study on the direction of modality transfer.
Dataset
MS² (RGB-T)
MS² (NIR-T)
MS² (RGB-NIR)
Direction
RGB-T
T-RGB
NIR+T
T-NIR
RGB-NIR
NIR-RGB
EPE
3.46
3.68
4.53
4.80
0.96
0.94
F1
35.89
44.11
49.81
60.71
5,00
4.66
networks using task-specific objectives. In this setup, synthetic flow data is introduced to enable ex- plicit motion supervision for the flow network. This strategy yields stable convergence and achieves an EPE of 5.80. Introducing the outlier-robust loss further improves performance, reducing EPE by 0.99 and F1 by 5.79, demonstrating its effectiveness in suppressing noisy supervision from syn- thetic artifacts. Finally, incorporating the proposed cross-modal consistency constraint leads to the best performance, with an EPE 3.46 and F1 of 35.89. These results confirm that enforcing spatial consistency during joint optimization significantly enhances flow estimation accuracy.
Flow data synthesis. DCFlow introduces a geometry-aware synthesis pipeline to generate image pairs with dense flow labels from a single image. As alternatives, evaluate the 2D transfor- mation (e.g., homography) and feed-forward 3D Gaussian Splatting (Szymanowicz et al. 2025) as substitute data generation strategies. We present qualitative comparisons of these strategies in Fig. 4 The 2D transformation (Fig. b)) ignores scene geometry and produces unrealistic mo- tion patterns, which cause the model overfit to such distortions. The 3D Gaussian Splatting approach (Fig. 4c)) synthesizes novel views with 3D awareness, but often suffers from visual arti- facts and instability caused by imperfect Gaussian primitives estimation. In contrast, our proposed data synthesis pipeline (Fig. 4 d)) produces geometrically consistent and visually plausible novel views, offering more reliable training data for flow networks. Moreover, Fig. [ shows that our geometry- synthesis pipeline generalizes well to challenging modalities such thermal im- ages. Table [b]rrports the results trained under each data synthesis pipeline, further demonstrating that geometry-an synthesis significantly outperforms the alternatives.
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Discussion monocular depth estimation. In DCFlow, adopt a pretrained monocular depth model to estimate the depth of input images for the geometry-aware data synthesis pipeline. We note that DCFlow is robust to imperfect or biased depth estimation. In general, the estimated depth serves only as an intermediate variable for flow data synthesis. Although the depth prediction may not be perfect, it is sufficient to represent the relative distance relationships between different gions of an image. Since both the warped image and the corresponding synthetic flow are computed using the same depth prediction, the synthetic flow label can still accurately represent the motion between the source image and the warped image. In addition, the photometric consistency checking and the outlier-robust loss filter out invisible areas and regions with artifacts, which ensures that the supervision focuses on pixels with reliable motion and appearance. Moreover, the cross-modal con- sisteney constraint provides an additional source of flow supervision that does not rely on monocular depth. To further demonstrate the robustness of DCFlow under inaccurate depth, we add degrada- tions to the depth map estimated by UniDepth (Piccinelli et al., 2024) for flow synthesis, including Gaussian noise, scaling noise, and edge blurring, As shown in Table Ic DCFlow shows only slight performance drop even when the depth maps are heavily corrupted, and it still performs favorably compared with existing unsupervised baselines.
Besides, we note that the monocular depth for NIR and thermal images is obtained by directly apply- ing depth foundation models pretrained on the RGB domain in zero-shot manner. Although these depth models are trained on RGB data, prior works (Shin et al.] [023] Shin & Park] (025) show that monocular depth estimation mainly relies on geometry cues such perspective geometry, occlu- sion boundaries, and texture gradients, which are largely modality invariant. Moreover, these depth foundation models are trained on diverse datasets with strong augmentations such as brightness, contrast, and hue changes, which further enhance their generalization ability to unseen modalities. As result, models like UniDepth generalize reasonably well NIR and thermal inputs. We replace UniDepth (Piccinelli et al. (224)) with Depth Anything v2 (Yang et al. 2024) and Metric3D v2 (Hu) et al.) 2024) for flow synthesis, and the results in Table Td show that DCFlow consistently achieves strong performance across different depth networks.
Generalization ability for different flow networks. We replace RAFT (Teed & Deng 2020) with GMA (Jiang et al. 202 FlowFormer (Huang et al. a (2022), and SEA-RAFT (Wang et al. 2024), and report the results in Table Te Our DCFlow consistently achieves superior performance across different flow networks, demonstrating strong generalization and compatibility.
Direction of modality transfer. richer to less informative modalities could make the learning process easier. We select the transfer direction based on the observation that modalities such as RGB and NIR contain richer texture and structure than thermal, making the modality transfer easier and more stable than the reverse direction. As shown in Table 2 transferring from RGB/NIR to thermal outperforms the reverse direction. The performance difference between RGB and NIR mapping is relatively small.
Since some modalities contain more information, transferring from
4.3 COMPARISONS WITH EXISTING APPROACHES
Baselines. We evaluate our DCFlow with large-scale pretrained approaches including Cross- RAFT (Zhou et al. 2022) and MINIMA (Ren et af.] 2025), unsupervised approaches including NeMAR (Arar et al. 2020) and UMF-CMGR (Wang et al. (022), and supervised approaches in- cluding RAFT (Teed& & Deng 2020), GMA (Jiang et al. 202 10),a), FlowFormer (Huang et al. 2022). and SEA-RAFT (Wang et al. 2024). CrossRAFT and MINIMA are pretrained on large-scale syn- thetic datasets with ground-truth flow label. We evaluate their performance using publicly available checkpoints. We retrain all unsupervised and supervised baselines under the same settings as ours for faimess. The supervised approaches are trained using sparse ground-truth flow annotations.
Quantitative comparison. Table 3 reports the quantitative results on five cross-modal datasets. Among all unsupervised and large-scale pretrained approaches, our DCFlow consistently achieves the best performance on both metrics, demonstrating strong generalization ability across diverse modalities. The existing unsupervised approaches such as NeMAR and UMF-CMGR generally yield unsatisfactory results, which aligns with findings from prior studies (Zhang et all, 2024| Yu let al. 2025). This suggests that appearance-basee optimization struggles to converge under signif- icant modality discrepancy and geometric misalignment. In contrast, DCFlow produces stable and accurate flow estimation, highlighting the effectiveness of our training framework. Compared to

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(RGB-1)
(N3R-T)
MEA (RGB-NUR)
VTD (RGB-T)
RNS (AGB-NUR)
Input image
Input image
NoMAR
UMF OMGR
DroeRAFF
MINMM
DCFow (Ouns)
Figure 5: Qualitative comparison of DCFlow and other approaches. The first two columns show the input image pairs, and the remaining columns visualize the image from modality warped using the estimated flow from each approach. For clarity, cropped patches of the full-resolution images are shown. The red boxes highlight the distortion regions.
Table 3: Quantitative comparison of DCFlow and other approaches. The EPE and F1 are reported. The best results among all large-scale pretrained and unsupervised methods are highlighted in bold.
MS²
MS
MS²
VTD (RGB-T)
RNS (RGB-NIR)
Category
Method
(RGB-T)
(NIR-T)
(RGB-NIR)
EPE
F1
EPE
F1
EPE
F1
EPE
F1
EPE
F1
RAFT
1.70
14.76
1.80
16.50
0.23
0.57
1.14
8.45
1.63
9.32
GMA
1.67
14.58
1.80
16.55
0.24
0.61
1.15
8.67
1.53
6.53
Supervised
FlowFormer
1.65
14.28
1.78
16.22
0.25
0.77
1.20
8.43
1.87
13.88
SEA-RAFT
1.65
14.04
1.97
17.64
0.21
0.48
6.42
Large-scale
CrosSRAFT
6.21
70.20
7.06
73.06
4.32
29.54
89.33
2.04
15.53
pretrained
MINIMA
5.97
66.12
7.10
70.37
5.44
33.48
6.34
80.41
2.34
15.18
NeMAR
19.25
99.80
28.41
99.99
11.39
99.92
23.43
96.89
25.11
99.83
Unsupervised
UMF-CMGR
18.84
99.78
26.67
99.99
8.85
99.27
28.05
99.13
31.13
99.98
DCFlow (Ours)
3.46
35.89
4.53
49.81
0.96
5,00
3.65
48.49
1.90
13.05
large-scale pretrained approaches like CrossRAFT and MINIMA, DCFlow achieves significantly better results. For instance, DCFlow yields 42.0%, 36.2%, 82.4%, 42.4%, and 18.8% lower EPEs than MINIMA the five datasets, demonstrating the advantage of learning directly from unlabeled real-world data over relying on synthetic cross-modal datasets. When compared with supervised ap- proaches, DCFlow achieves competitive performance, despite the absence of using ground-truth cross-modal flow labels. These results highlight the effectiveness of DCFlow.
Qualitative comparison. Fig. 5 presents qualitative results five datasets. We visualize the im- age from modality A warped using the estimated flow to assess the accuracy of each method. As shown, previous unsupervised approaches such as NeMAR and UMF-CMGR struggle with large moosl misalignments. Although CrossRAFT and MINIMA produce coarse alignment, they still exhibit noticeable mismatches several regions, as highlighted by the red boxes. In contrast, DCFlow achieves more precise warping, demonstrating the superiority of our training framework.
5 CONCLUSIONS
We have presented DCFlow, a novel self-supervised framework for cross-modal flow estimation that combines decoupled optimization strategy and a cross-modal consistency constraint. The former tackles modality discrepancy and geometric misalignment with task-specific supervision, while the latter enables direct learning of cross-mout flow. Within this framework, we introduce geometry- synthesis pipeline with outlier-robust loss to provide reliable flow supervision from single- view images. Experiments on multiple datasets demonstrate that DCFlow achieves state-of-Ne ofte--of-the-nn the-art performance among unsupervised approaches.
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ACKNOWLEDGMENTS
This work was supported in part by the National Natural Science Foundation of China under grants 62301484 and 62571478, in part by the Ningbo Natural Science Foundation of China under grant 2024J454, in part by the Young Talent Fund of Zhejiang Association for Science and Technology under grant ZISKXQT2026135, under grant 2026-1-022.
in part by the Jinhua Science and Technology Bureau Project
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A APPENDIX
A.1 NETWORK ARCHITECTURES
In this paper, we propose DCFlow, novel self--uuprvvied framework for cross-modal flow estima- tion. It serves as a general and network-agnostic training paradigm, supporting the effective training of modern flow estimation networks. In our main experiments, adopt U-Net (Ronneberger al. 2013) for modality transfer and RAFT TTed & Deng, 2020) for flow estimation.
Modality transfer. As illustrated Fig. aa), the modality transfer network adopts an encoder- decoder structure with skip connections to preserve spatial details, and operates at four resolution scales. We use two convolutional layers with batch normalization and ReLU activations as the basic unit, max-pooling for downsampling, and bilinear interpolation for upsampling. Given the input image I IR HR x3, the feature maps across the four scales have sizes of H x W x C, g X W 8C, respectively. The U-Net architecture allows the
2 T X 2C, T x W 4C, and И network to translate images from one modality to another without any task-specific modifications, demonstrating the generalization ability and robustness of our training framework.
H W
Flow estimation. We adopt RAFT as the flow estimation network. As shown in Fig. 6 consists of feature encoder, context encoder, correlation layer, and an iterative flow decoder. Given the input image pair 1 I E ##*W'3 x3, the goal is to predict the optical flow E H The feature encoder extracts matching features from both images, while the context encoder processes only I to extract contextual information. The correlation layer then computes a 4D cost volume by taking the inner product between all pairs of matching features. Finally, the cost volume and context features are passed into the iterative decoder, which progressively refines the flow predictions. For more details, please refer to RAFT (Teed & Deng. 2020j.
A.2 DETAILS CROSS-MODAL CONSISTENCY CONSTRAINT
In the following, we describe the details on how the transformed flow is obtained. Given a cross- modal image pair (IA, IB) and the predicted flow F FBZA, apply the same random affine trans- formation A(x) = Ax + t to both images, where A encodes rotation and scaling, and t is the translation. We denote the original flow FRZA = XA Xp. After transformation, the corre- sponding points are given by XB A(XB) and XA- A(xA). The transformed flow F B2A can be formulated as
F B2A (XB) = X - XB = A(xa - xB) x
AFBzA(XA
(13)
We note that only the linear part A affects the flow, while the translation term cancels In practice, for each ŠB, we sample the corresponding B2A (xp) via bilinear interpolation and then multiply it by A obtain the transformed flow.
A.3 MORE IMPLEMENTATION DETAILS
We implement DCFlow using Py Torch. The channel dimension C' in the modality transfer network is set to 16. The number of iterations in RAFT is fixed to 6. The threshold of the photometric error in the data synthesis pipeline is set to 10, defined on 8-bit image intensities. The top-7% threshold in the outlier-robust loss for flow estimation set to 20%. For the cross-modal consistency constraint, we apply random affine transformations, including rotations within +:30, scaling factors the range of [0.95, 1.05], and translations within #24 pixels along both axes. We adopt the Adam W optimizer with a maximum learning rate of 0. 0.0004, and apply the cosine decay schedule during training. All experiments are conducted on NVIDIA RTX 4090 GPUs.
A.4 METRICS
We use the endpoint error (EPE) and flow outlier rate (F1) as quantitative metrics to evaluate flow accuracy.
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(a) Modality transfer network
(b) Flow estimation network
Context
encoder
I
Feature encoder
Correlation computing
Iterative flow decader
Convolution
Basic unit
L
Feature encoder
Figure 6: Illustration of the network architectures. (a) Modality transfer network, which adopts a U-Net architecture. (b) Flow estimation network, which adopts RAFT.
Table 4: The statistics of cross-modal flow datasets.
Dataset MS²
Modality RGB-T-NIR
Training
Testing
Resolution
3631
907
608x192
VTD
RGB-T
1536
392
512x320
RNS
RGB-NIR
1187
294
704x:522
The EPE is defined as the average L2 distance between the predicted optical flow F and the ground- truth flow FGT over all valid pixels, formulated as
EPE
Σ F(x) - FcT(x)ll2
(14)
N
X
where x indexes valid pixel locations, and N is the total number of valid pixels. Lower EPE indicates higher accuracy.
The F1 score measures the percentage of outlier pixels where the EPE exceeds both pixels and 5% of the ground-truth flow magnitude. A pixel x is considered an outlier if
[x(x)
For(x)ll2 > max(3, 0.05 F.05-||Fortle (x)
(15)
The F1 score is then computed
Σ 1 [x is an outlier] x 100%.
(16)
N
where [[:] denotes the indicator function. Lower F1 indicates better robustness large flow errors.
A.S DETAILS DATASETS
We evaluate DCFlow MS² (Shin et al. 2023), VTD (Guo et al. 2023), and RNS (Kim & Baek 2025) datasets. Specifically, the MS² dataset contains three modality pairs, namely MS (RGB-T), MS² (RGB-NIR), and MS² (NIR-T). Dataset statistics are summarized in Table In the following, we describe the process of dataset repurposing.
To ensure scale consistency across modalities, first standardize the image resolution, and unify the effective camera intrinsics. This ensures that all modalities share a common focal length scale, enabling accurate reprojection between viewpoints. Formally, adjust the intrinsic matrices KA and K of modalities A and B, respectively, shared intrinsic matrix K
(17) After aligning the intrinsics, we compute ground-truth optical flow between modality pairs using LiDAR depth and known extrinsic calibration. For each valid pixel xa = [u, v] modality B with depth we first back-project it into 3D space, expressed
K -. K. K K.
u
XB z z
(18)
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MSF (RGB-T)
MS* (NIR-T)
MS+ (RGB-NIR)
VTD (RGB-1) T)
RNS (RGB-NIR)
(Modality
(Modality B)
(Modality
Pair (Modality B)
Figure 7: Cross-modal input image pairs from each dataset. For each dataset, we present two exam- ples.
Table Ablation the value of in the outlier-robust loss.
EPE
F1
w/o outlier-robust loss
5.80
57.18
10
5.40
55.59
20 (Default)
4.81
51.39
30
5.01
50.22
5.59
58.29
We then transform the 3D point into the coordinate system of modality using the extrinsic matrix R] E SE(3), formulated as
TB-A
XA = R X3 t.
(19)
The corresponding 2D projection in modality A is given by
(K Xx),
(20)
where .-)) denotes the perspective projection. displacement between corresponding points as
The ground-truth flow then computed as
2D
F FIZA (xB) = XA XB-
(21)
Only pixels with valid depth and successful projections within image boundaries are retained. We present two cross-modal image pairs for each dataset in Fig.
A.6 ADDITIONAL. ABLATION STUDIES
Ablation on the value of r in the outlier-robust loss. We vary from 10 to 40, and list the results in Table When 7 is too small, the filtering of undesired regions becomes insufficient, and noisy pixels still contribute
supervision. When is too large, many pixels removed, and the supervision signal becomes weak. We choose 7 as 20 to provide a good balance between removing unreliable regions and keeping enough valid information for supervision.
Comparison with other robust loss functions. We compare our outlier robust loss with the Char- bonnier loss and the Mixture of Laplace loss used in SEA-RAFT (Wang al. 2024). As shown
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Table 6: Comparison with other robust loss functions.
Loss type
EPE
F1
L1 loss (baseline)
5.80
57.18
Charbonnier loss
6.02
63.62
Mixture-of-Laplan loss
5.94
56.21
Outlier-robust loss (Ours)
4.81
51.39
Table 7: Ablation on the affine ranges in the cross-modal consistency constraint.
Affine range
Rotation
Translation
Scaling
EPE
F1
w/o consistency constraint
4.81
51.39
Small
[-1,+1]
[-12, +12]
[0.98, 1.02]
3.61
36.08
Medium (Default)
[-3,+3]
[-24, +24]
[0.95, 1.05]
3.46
35.89
Large
[-5,+5]
[-36, +36]
[0.92, 1.08]
3.93
46.37
in Table 6, both alternatives lead inferior results, while our outlier-robust loss achieves the best performance by more effectively suppressing high error outliers.
Ablation the affine ranges in the cross-modal consistency constraint. We evaluate small, medium, and large affine perturbations for consistency constraint, and list the results in Table 7 It can be seen that small and medium perturbations already introduce sufficient geometric variation for the consistency constraint, while excessively large perturbations may create unrealistic deformations that harm the supervision quality. We choose medium perturbations default.
A.7 MORE EXPERIMENTAL. RESULTS
Generalization unseen domains. We present the cross-dataset generalization results between (RGB-T) and VTD (RGB-T) in 8 When trained on one dataset and evaluated on an- other unseen dataset, DCFlow achieves better performance than existing unsupervised approaches trained directly on the target dataset. This highlights the advantage of our framework over previ- ous unsupervised approaches that rely solely on appearance- supervision. Moreover, although large-scale pretrained approaches DCFlow achieves comparable or better generalization under the cross-dataset evaluation setting, while requiring far less training data.
substantially larger synthetic datasets with ground- flow,
Generalization to unseen sensor types. We evaluate the cross-modal generalization ability of DCFlow by testing models trained (SR(RGB-T) and (RGB-IIN the unseen (NIR-T) modality. As shown in Table 9 the model trained on RGB-T achieves reasonable performance on NIR-T, whereas the model trained RGB-NIR fails generalize. This can likely be attributed to the modality gap between RGB-T and NIR-T T is relatively closer, making the trans- fer from RGB-T to NIR-T more feasible. In contrast, the gap between RGB-NIR and NIR-T is substantially larger, which leads to poor generalization.
Modality transfer results. We present qualitative results of modality transfer on each dataset in Fig. 8 The translated images preserve the structural details of the source modality, while showing appearance characteristics aligned with the target modality. These results clearly demonstrate effectiveness of our proposed learning framework.
Visualization of synthetic flow data. Fig. presents examples of synthetic flow generated from single-view images. The results demonstrate that geometry- data generation pipeline pro- duces high-quality flow supervision, and generalizes well across different modalities.
Visualization of the valid mask used for flow supervision. We present examples of the valid mask used for flow supervision in Fig. 10 Though the imperfect depth estimation would lead to visual artifacts in the synthetic images, the photometric consistency checking and the outlier-robust loss effectively remove these regions for loss computation, enabling high-quality supervision of the flow network.
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Table Cross-dataset generalization results.
MS² (RGB-T)
VTD (RGB-T)
Method
Training dataset
EPE
F1
EPE
F1
CrossRAFT
Large-scale synthetic dataset
6.21
70.20
9.86
89.33
MINIMA
Large-scale synthetic dataset
5.97
66.12
6.34
80.41
MS² (RGB-TT
19.25
99.80
25.29
99.99
NeMAR
VTD (RGB-T)
20.05
99.76
23.43
96.89
MS² (RGB-T)
18.84
99.78
32.06
99.99
UMF-CMGR
VTD (RGB-T)
25.78
99.99
28.05
99.13
MS² (RGB-T)
3.46
35.89
6.65
76.25
DCFlow (Ours)
VTD (RGB-T)
5.70
68.97
3.65
48.49
Table 9: Generalization results from models trained RGB-T RGB-NIR image pairs to NIR-T.
Training Modality
EPE
Fl
RGB-T
10.20
86.23
RGB-NIR
27.66
99.99
Analysis for low-performing modalities. We note that the flow estimation accuracy of NIR-T modalities is lower than that of RGB-T. It is mainly caused by imperfect modality transfer. For instance, the NIR modality has a much narrower spectral range and only one channel, whereas RGB covers wider spectrum with three channels. This makes transferring from NIR thermal substantially more difficult than transferring from RGB, especially when perfectly aligned cross- modal pairs are not available for supervision. Fig. [ree[[[cmmprrr modality transfer and flow estimation on RGB-T and NIR-T pairs from the same scenes. Compared with the transferred images from RGB, the transferred images from NIR show blurrier structures and less distinct edges in some regions, which result in larger alignment errors.
Visualization the training process. We provide visualization the training process Fig. Fig. 12 a) shows the curves of four loss functions, where all losses exhibit stable decreases. Fig 12 presents the qualitative comparison the modality transfer and flow estimation results at different training iterations. As the network trains, both the transferred images and estimated flow gradually improve, confirming that the proposed framework effectively learns cross-modee flow estimation over time.
Qualitative comparison. We present additional qualitative comparisons between DCFlow and pre- vious methods, including NeMAR (Arar al. (020), UMF-CMGR (Wang et al. 2022). Cross- RAFT (Zoou et al., 2022)., and MINIMA (Ren et al. 2025), as shown in Fig. 13 It can be observed that DCFlow produces more accurate flow estimation results, whereas the other approaches exhibit noticeable mismatches.
Efficiency comparison. Table 10 presents the comparison in terms of inference time and mem- ory usage. It can be seen that DCFlow achieves competitive runtime and memory usage, while maintaining strong accuracy.
A.8 LIMITATIONS
While DCFlow significantly outperforms previous unsupervised approaches, slight performance gap remains compared to supervised methods. addition, DCFlow relies on a data synthesis pipeline to provide flow supervision from single-view images, which may increase training time. Despite these limitations, we believe DCFlow contributes to advancing unsupervised cross-modal flow estimation.
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Published as a conference paper at ICLR 2026
MSR (RGB-TT)
MS* (NIR-T)
MS? (RGB-MIR)
VTD (RGB-T)
RNS (RGB-NIR
Modality
Modality transferred
Modality
Figure 8: Visualization modality transfer results across different datasets.
VID
(ide KiP)
Ioout Imag (Modally
Siithee Frogo (Pouadtyy A)
Sentholic Flow (Node by
Ju Image (oolllty
Synthetic Irago (Pictaliy B)
Setthesc flow (Modelly
Figure 9: Visualization of synthetic flow data across different datasets.
B STATEMENT ON THE USE OF LLMS
We used large language models (LLMs) solely as a writing aid to improve grammar and wording. LLMs were not used for research ideas, experimental design, data analysis. All text written by the authors and carefully reviewed for accuracy and originality. The authors take full responsibility for the content of the manuscript.
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Published as a conference paper at ICLR 2026
Input image
Synthetic image
Synthetic flow
Mask for supervision
Figure 10: Visualization of the valid mask used for flow supervision the MS² (RGB-T) dataset. Thanks to the photometric consistency checking and the outlier-robust loss, invisible areas and re- gions affected by severe artifacts are excluded from loss computation.
Example
Example
inoutinage A, (KGB)
Trarsfored image (RGB) 1)
Warpol image A (RGB)
Jeput mage A (IK28)
Transfaned image (RG2 [T]
Warsed image (RGB)
Irpuc Image A (OO)
Transforeed Image à ("G
Warped Irage A (MOR)
Irput Image (MIR)
Transforred Image (MIR->T)
Whrped Image A (MGR)
Input image & (Thematt
Drout iage (Themna)
Comparison of modality transfer and flow estimation results for RGB-T and NIR-T pairs on the same scenes. For clarity, cropped patches of the full-resolution images are shown. The red boxes highlight regions where the modality transfer quality and the resulting flow estimation accuracy differ across modalities.
Figure 11:
Table 10: Efficiency comparison of different approaches.
Approach
SEA-RAFT
UMF-CMOR
(Ours)
Inference time (mm)
52.6
39.1
58.7
34,5
85.2
357.9
49.4
64.7
40.1
Inference memory (MB)
718
816
614
2250
7240
3056
2770
742
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Published as a conference paper at ICLR 2026
0.04
22
0.0
0.5
602
1.
035
25
045
Fow loss modality
Flow loss of modality
Frow loss of conditency constraint
Mocality transter loss
(a) Curves different loss functions
Triput RGB thermal images
modailty
results modallty transfer and flow estimation 1000 heration
The resuits of modally transfer and flow estimation 10000 keration
The resuits of modaity transfer and flow estimetion at 30000 iteretion
(b) example of training process
Figure 12: Visualization of the training process the MS (RGB-T) dataset. (a) Curves of loss functions used in DCFlow. All losses decrease with clear convergence as training progresses, indi- cating the effectiveness of our training strategies. (b) Visual examples of modality transfer and flow estimation at different training iterations. The results are shown at 100, 1000, 10000, and 30000 iterations.
(RGB-TT)
HS? (NUR-T)
HS? (RGB-MIR)
(RG0-1)
(RGB-MIR)
Image
UMF-CWER
CrossRAFT
MINOMA
DCFlow
Figure 13: Qualitative comparison of DCFlow and other approaches. The first columns show the input image pairs, and the remaining columns visualize the image from modality warped using the estimated flow from each approach. For clarity, cropped patches of the full-resolution images are shown. The red boxes highlight the distortion regions.
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Reviews:
[{"review_id": "4hBkBoFaki", "reviewer": "Reviewer_PNVY", "summary": "This work introduces a method for cross-modal optical flow estimation. It claims that different modalities of data have their own applicability. For example, during autonomous driving, different modalities of data may be required for day and night. In such cases, there may be issues such as misalignment of different modalities of data, making cross-modal optical flow estimation relatively difficult. Specifically, it proposes DCFlow, which significantly improves the model performance in an unsupervised manner based on the decoupled optimization strategy and synthetic supervision data.", "questions": "- How robust is DCFlow to failures in monocular depth estimation for the geometry-aware synthetic pipeline?\n- For low-performing modalities/gaps (e.g., MS^2 NIR-T), can you provide qualitative failure analysis or error breakdown by source (e.g., transfer vs. flow estimation vs. data synthesis)?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- DCFlow clearly addresses a key limitation in present unsupervised cross-modal flow methods by introducing direct, geometry-aware supervision and robust optimization.\n- By conducting separate training with single-modal optical flow supervision and modal transfer supervision, the problem of task coupling in traditional methods is resolved.\n- The experimental section is thorough, systematically demonstrating the effect of each technical choice.\n- This method circumvents the need for supervised data by using synthetic data, and it is effective and sound.", "weaknesses": "- The approach relies on pretrained monocular depth (Sec 3.3, Fig. 3) to generate synthetic flows. However, there is minimal discussion of the risk or variability this introduces. How does DCFlow handle inaccurate or biased depth prediction in unseen modalities, e.g., thermal-NIR? What happens if the depth model underperforms? This is especially noteworthy given the visual artifacts in certain synthetic example patches (Fig. 4/9). The ablation on the synthetic pipeline is strong, but further stress-testing (e.g., on failure cases with poor depth) is absent.", "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": "5v0dKq0ZaS", "reviewer": "Reviewer_NaNT", "summary": "The paper proposes DCFlow, an unsupervised framework for cross-modal flow that trains a modality-transfer network and a flow network with a decoupled scheme plus a cross-modal consistency objective. The flow net learns from explicit mono-modal supervision generated by a geometry-aware single-image synthesis pipeline with an outlier-robust loss; the transfer net learns via a perceptual loss while flow is frozen; and a consistency loss enforces equivariance under known spatial transforms to tie the two together.", "questions": "(1) How is monocular depth obtained for non-RGB modalities (Thermal, NIR), and how reliable is it for generating supervision?\n\n(2) Why was the top-τ% value fixed at 20%, and how sensitive are results to this choice or to alternative robust loss functions?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "(1) A decoupled optimization recipe that stabilizes training by supervising the flow net on synthetic mono-modal pairs while separately optimizing the transfer net, addressing modality discrepancy and geometric misalignment distinctly. \n\n(2) A geometry-aware single-image synthesis pipeline + top-τ% trimming that yields robust, dense motion supervision without cross-modal labels. \n\n(3) A cross-modal consistency constraint that jointly optimizes the two nets under known spatial transforms. \n\n(4) A repurposed benchmark over MS2/VTD/RNS with RGB/NIR/Thermal pairs and LiDAR-derived sparse flow.", "weaknesses": "1. The paper jumps quickly from identifying limitations of existing appearance-based methods to presenting its full “decoupled + consistency” solution. The reasoning that connects the problem to this specific design is underdeveloped and feels somewhat ad hoc.\n\n2. It seems that several key components, such as the fixed top-τ% robust loss, small affine ranges in the consistency constraint, and reliance on monocular depth synthesis, are justified only empirically with little or none sensitivity or ablation analysis.\n\n3. While quantitative results are strong, the paper can be refined by introducing an analysis of efficiency, runtime, and generalization to unseen domains or sensor types.", "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": "f5uRjWdP7K", "reviewer": "Reviewer_te33", "summary": "This paper proposes a cross-modal flow estimation framework (e.g., between RGB and thermal images) that does not require ground-truth optical flow across modalities. The key idea of the framework is to disentangle modality discrepancy and geometric misalignment. The architecture consists of a flow estimation network and a modality transfer network, which are trained both independently and jointly through a cross-modal consistency constraint that enhances cross-modal flow estimation. The proposed method is evaluated on a newly introduced dataset constructed from MS², VTD, and RNS, achieving superior performance compared to previous unsupervised cross-modal methods.", "questions": "1. In Section 3.4, could the authors include more details on how the transformed flow is obtained? In my opinion, while this operation seems feasible, it likely involves non-trivial preprocessing or transformation steps. It would be helpful to include a brief explanation or illustration of this process in the supplementary material.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The proposed architecture effectively disentangles modality discrepancy and geometric misalignment, while the cross-modal consistency constraint strengthens the connection between modalities. This design is well-suited for scenarios where cross-modal ground-truth data is scarce and demonstrates effective results. \n\n2. A comprehensive ablation study is conducted for each component, showing that every part of the proposed framework contributes to performance improvement on the evaluation dataset, thereby supporting the design choices.", "weaknesses": "1. Ambiguity in the use of the term unsupervised\n- Although the term “unsupervised” is used throughout the paper, in my opinion its meaning differs from how it is conventionally used in the optical flow field. In this paper, the method is described as unsupervised because it does not require cross-modal optical flow annotations between modalities. However, each individual modality is trained using synthetic data with ground-truth optical flow annotations. Just as a optical flow model (e.g., RAFT)  trained on the FlyingChairs synthetic dataset is typically categorized as a supervised method, it is questionable whether the proposed pipeline, which is trained on synthetic data with GT flow, should be classified as unsupervised. It would be better to use a more precise term that explicitly conveys the idea of training without cross-modal ground-truth flow, rather than referring to the method as completely unsupervised.\n\n2.  Novelty of outlier-robust loss and geometry-aware data synthesis pipeline\n- In Lines 81–82, the paper states “we further design an outlier-robust loss”, and in Line 253, it mentions “we propose a geometry-aware data synthesis pipeline with an outlier-robust loss.” However, both ideas appear to overlap with previous works. For the outlier-robust loss, several prior studies—such as WaterMono [a1], MapAnything [a2], and Regularizing Nighttime Weirdness [a3] already adopt a similar strategy by excluding the top percentile of high-error pixels during loss computation. Likewise, the idea of a geometry-aware data synthesis pipeline has been previously explored in [a4] and [a5]. It would be better for the authors to properly cite the relevant prior works to clarify the originality and positioning of these components.\n\n[a1] Ding et al., “WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth Estimation”, IEEE Transactions on Instrumentation and Measurement 2025.\n\n[a2] Keetha et al., “MapAnything: Universal Feed-Forward Metric 3D Reconstruction”, arXiv 2025.\n\n[a3] Wang et al, “Regularizing nighttime weirdness: Efficient self-supervised monocular depth estimation in the dark.”, ICCV 2021.\n\n[a4] Aleotti et al., “Learning optical flow from still images”, CVPR 2021.\n\n[a5] Han, Yunhui, et al., “RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos”, ECCV 2022.\n\n3. The proposed experiments are conducted on MS², VTD, and RNS datasets, using 80% of frames per video for training and 20% for testing. However, this evaluation setup may be insufficient to assess the model’s generalization capability, since both training and testing samples originate from the same video distribution. It would be more convincing if the authors could report results under a cross-dataset evaluation setting, where the training and evaluation datasets differ, to better demonstrate generalization performance.\n\n4. It would be beneficial if the paper provided a visualization of the training process for the proposed flow estimation network and modality transfer module.", "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": "0n9sp9ZEZf", "reviewer": "Reviewer_xLM8", "summary": "This paper proposes DCFlow, a decoupled cross-modal flow estimation framework that can jointly train its two components, a modality transfer network and a single-modality flow estimation, without requiring direct ground-truth supervision. Specifically, a geometry-aware data synthesis pipeline is used to synthesize new views as well as their flow to self-supervise the flow estimation network. Affine transformations are also applied to help refine both networks jointly for better cross-modal consistency. Experiment results show promising results for this method.", "questions": "1. Since the two modalities are symmetric. Have the authors tried to switch the two modalities and see how the results may change? For example, if the current framework trains a modality transfer network from modality A to B and a flow estimation network that estimates flow in modality B. It is also possible to switch modality A and B (modality transfer from B to A and a flow estimation in A). It would be interesting to see how this will change the results, or at least the authors should justify why the current modality choices of A and B should be the best one.\n2. Since the motion estimation network and the modality transfer network are optimized alternately, I assume the optimization should start with two decently working networks instead of training both from scratch. Could you discuss more on how to start this training?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The paper is well-structured. It is great to have an overview of the proposed method before diving deep into each component.\n2. The proposed method is simple and easy to understand. Each method design is explained in the text.\n3. The presented results look good, although it may be better if the authors can show more results for more insights (detailed below in weaknesses and questions).", "weaknesses": "1. Academic writing can be better polished. \n- Many references in the paper are cited using `\\citet`, disrupting the flow of the text, which should be replaced with `\\citep` for smoother readability. \n- Some text also lacks clearance. For example, Line 083-085 says decoupled optimization reduces EPE by 72%, and Line 089-090 says the cross-modal consistency constraint results in a 28% improvement in EPE. This is confusing. Does it mean these two strategies jointly reduce EPE by 100%, so you basically have 0 EPE?\n2. Eq (12) is not formatted correctly. \"Argmin\" stands for the argument values that can minimize the following expression, and adding these argument values does not make sense. Maybe the authors should put just one $\\arg\\min_{\\phi\\theta}$ outside the addition of these three loss components.\n3. Table 2(c) is a bit weak because it lacks a baseline to compare. It is hard to tell whether the four network results are consistently improved compared to their original results without the proposed strategies in DCFlow.", "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)