Injecting Frame-Event Complementary Fusion into Diffusion for Optical Flow in Challenging Scenes
Authors: Haonan Wang
Hanyu Zhou
Haoyue Liu
Luxin Yan
Venue: NeurIPS 2025 spotlight ()
Keywords: optical flow estimation
event camera
diffusion models
Primary area: applications

Abstract: Optical flow estimation has achieved promising results in conventional scenes but faces challenges in high-speed and low-light scenes, which suffer from motion blur and insufficient illumination. These conditions lead to weakened texture and amplified noise and deteriorate the appearance saturation and boundary completeness of frame cameras, which are necessary for motion feature matching. In degraded scenes, the frame camera provides dense appearance saturation but sparse boundary completeness due to its long imaging time and low dynamic range. In contrast, the event camera offers sparse appearance saturation, while its short imaging time and high dynamic range gives rise to dense boundary completeness. Traditionally, existing methods utilize feature fusion or domain adaptation to introduce event to improve boundary completeness. However, the appearance features are still deteriorated, which severely affects the mostly adopted discriminative models that learn the mapping from visual features to motion fields and generative models that generate motion fields based on given visual features. So we introduce diffusion models that learn the mapping from noising flow to clear flow, which is not affected by the deteriorated visual features. Therefore, we propose a novel optical flow estimation framework Diff-ABFlow based on diffusion models with frame-event appearance-boundary fusion. Inspired by the appearance-boundary complementarity of frame and event, we propose an Attention-Guided Appearance-Boundary Fusion module to fuse frame and event. Based on diffusion models, we propose a Multi-Condition Iterative Denoising Decoder. Our proposed method can effectively utilize the respective advantages of frame and event, and shows great robustness to degraded input. In addition, we propose a dual-modal optical flow dataset for generalization experiments. Extensive experiments have verified the superiority of our proposed method. The code is released at <https://github.com/Haonan-Wang-aurora/Diff-ABFlow>.

Injecting Frame-Event Complementary Fusion into Diffusion for Optical Flow in Challenging Scenes
Haonan Wang', Hanyu Zhou²; Haoyue Liu', Luxin Yan
National Key Lab of Multispectral Information Intelligent Processing Technology,
School Artificial Intelligence and Automation, Huazhong University of Science and Technology
School of Computing, National University of Singapore [whn yaarora,ynnluxinng @hust. edu. en, hy sy.zhou@nus.o shho@nus.eddu.ge
Abstract
Optical flow estimation has achieved promising results in conventional scenes but faces challenges in high-speed and low-light scenes, which suffer from mo- tion blur and insufficient illumination. These conditions lead to weakened tex- ture and amplified noise and deteriorate the appearance saturation and boundary completeness of frame cameras, which necessary for motion feature match- ing. In degraded scenes, the frame camera provides dense appearance saturation but sparse boundary completeness due to its long imaging time and low dynamic range. In contrast, the event camera offers sparse appearance saturation, while its short imaging time and high dynamic range gives rise to dense boundary complete- ness. Traditionally, existing methods utilize feature fusion or domain adaptation introduce event improve boundary completeness. However, the uppearance features are still deteriorated, which severely affects the mostly adopted discrimi- native models that learn the mapping from visual features to motion fields and gen- erative models that generate motion fields based on given visual features. So we introduce diffusion models that learn the mapping from noising flow to clear flow, propose
which is not affected by the deteriorated visual features. Therefore, a novel optical flow estimation framework Diff-ABFlow based on diffusion mod- els with frame-event oopuarance-boundary fusion. Inspired by the appearance- boundary complementarity of frame and event, we propose an Attention- Appearance- Boruurance-Bouddary Fusion module to fuse frame and event. Based diffu- sion models, we propose a proposed method can effectively utilize the respective advantages of frame and event, and shows great robustness to degraded input. addition, we propose a dual-modal optical flow dataset for generalization experiments. Extensive experi- ments have verified the superiority of our proposed method. The code is released httph:://// //te:////ttttbtbt com/Haonan Haanaannnna-nn--a-a-a aurora/DifffAAF1...
Iterative Denoising Decoder. Our
Introduction
Optical flow estimation is a visual task that models pixel-wise displacements between adjacent frames. Existing methods [11, 17, 28] focus on conventional scenes, while challenging degraded scenes such high-speed and low-light scenes remain to be further explored. The motion blur of high-speed scenes and the insufficient illumination of low-light scenes both lead to weakened texture and amplified noise. These degradations severely deteriorate visual features and violate the photometric consistency assumption, which further brings about invalid motion feature matching.
*Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Comparison of Flow Results
Appearance Saturation V.S. Boundary Completeness
Bounday
Sparss
Frame
complexeness
Detse
Plos
Appesrance
saturation
Sperse
High-spool
Lene-light
High-spood
Low-light
Appear. Saturation
Bound. Completenes
(a) Problem of high-speed and low-light scenes
Noisy flow
Appearance saturation
Boundary
completeness
Fromss
Sparse
Appear- Complementary Fusion
Complementary
Condition
Sparse
Dense
Event
Appearance
Boundary
Appearance
Boundary
saturation
complcteness
Clear flow
Illustration of main idea
Figure Illustration of problem and idea. Motion blur in high-speed scenes and insufficient illumination in low-light scenes reduce the boundary completeness of frame images, resulting in un- clear boundary in optical flow. In this work, explore the oppearance-bounday complementarity of frame and event to guide the fusion of these two modalities. In addition, we introduce diffusion models to reconstruct the paradigm of optical flow estimation a denoising process from noisy optical flow to clear optical flow conditioned on fused visual features.
Typically, existing methods adopt either uni-modal visual enhancement dual-modal motion fusion to deal with high-speed and low-light conditions [8, 33, 38, 41]. The former, including deblurring and low-light enhancement improves the apparent visual effect, but the inner features remain deteri- orated, contributing nothing to the photometric constancy assumption and motion feature matching. cameras improve boundary completeness while the appearance features are still degraded and unqualified for motion feature matching. Specifically, appearance saturation refers to the abundance of appearance texture information within visual modality. reflects the de- gree of spatial variation in pixel intensity caused by fine-graines textures, shading, and color details. Boundary completeness denotes the continuity and integrity of object boundaries within modality. It evaluates how well the modality captures clear, coherent, and complete boundary structures.
The latter utilizes
To solve these problems, mainly explore in two aspects: sensors that improve visual features and models that are robust to degraded input features. As shown Fig. 1, on the one hand, we utilize the appearance-boundee
complementarity of frame and event obtain better visual features. The frame camera captures appearance with dense saturation, but due to its long imaging time and low dynamic range, the boundary captured under high-speed and low-light conditions shows sparse completeness. Thus, we introduce the event camera, a neuromorphic visual sensor [4], which offers dense boundary completeness because of its short imaging time and high dynamic range, despite its sparse appearance saturation. The optical flow with saturated appearance and complete boundary. On the other hand, utilize the paradigm of diffusion models to adapt to degraded input features. Discriminative and generative models both rely on high-quality visual feature input. The former learns the mapping from visual features to motion fields, and the latter leams the process of generating motion fields based on given visual features. Both are severely affected by the degradation of visual features. Different from those two, the paradigm proposed by DDPM [9] models the denoising process from noisy data to clear data. We apply to optical flow estimation and learn the mapping from noisy optical flow to clear optical flow, which demonstrates strong robustness to the degradation of visual features.
boundary complementarity enables us to estimate

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Based on these motivations, we propose Diff-ABFlow, novel diffusion- optical flow esti- mation framework guided by frame and event modalities. To exploit the appearance- complementarity of frame and event, we propose an Attention-based sion (Attention-ABFF module, which effectively combines the appearance feature of the frame and the boundary feature of the event to obtain fusion features with saturated appearance and complete boundary. Based on diffusion models, we propose a Multi-Condition Iterative Denois- ing Decoder (MC-IDD) as the optical flow backbone, including Time- Visual-Motion Multi-way Cross-Attention (TVM-MCA) module and a Memory-GRU Denoising Decoder (MGDD) module. TVM-MCA integrates the fused visual feature, motion feature and temporal embedding to obtain the comprehensive feature including information from three aspects. MGDD is an optical flow infer- ence module that combines the denoising paradigm proposed by DDIM and the iterative refinement method in optical flow estimation, which retains the generalization and robustness of diffusion mod- els while improving efficiency. In summary, our contributions are as follows:
Anpearance-Boundae Fu-
We propose a novel framework, Diff-ABFlow, which leverages diffusion models with frameevent complementary fusion strategy for accurate optical low-light scenes. To the best of our knowledge, this is the first work that utilizes dual-modal data input to guide diffusion models for optical flow estimation.
flow estimation in high-speed and
We propose the Attention-ABF module. Attention-ABF effectively utilizes the appearance- boundary complementarity of frame cameras and event cameras obtain fusion features with high-quality appearance and boundary information.
We propose the MC-IDD module. MC-IDD is an innovative optical flow backbone based on the DDIM paradigm and improved for optical flow estimation tasks, which combines visual features, motion features, and temporal embeddings to guide the denoising process.
We conduct extensive experiments on both synthetic and real-world datasets to comprehensively demonstrate that our proposed Diff-ABFlow achieves statc-of-the-ar performance in optical flow estimation under high-speed and low-light conditions.
2 Related Work
Optical Flow Estimation. Optical flow estimation methods have developed rapidly with the ad- vancement of deep neural networks. Earlier optical flow methods used a simple U-Net structure [3, 12]. Subsequent research gradually integrated modules such as feature pyramid and cost volume into optical flow estimation [23, 28, 35]. In addition, powerful techniques such as GRU [14, 29] and Transformer [1 1, 25, 34, 377 have been incorporated the backhone for optical flow estimation. However, these trame-based methods often suffer from the motion blur in high-speed scenes and in- sufficient illumination in low-light scenes. In contrast, the event camera with short imaging time and high dynamic range captures high-quality visual signals especially in boundary areas. Event-based approaches [5, 7, 16, 21, 42] mainly follow the frame-based framework and reconstruct the event stream into event frame as input. In this work, we utilize the appearance-boundayy complementarity of frame and event obtain better visual features for optical flow estimation in degraded scenes.
Degraded Scenes Optical Flow. To deal with the motion blur of high-speed scenes and the insuf- ficient illumination of low-light scenes, some researches directly perform visual enhancement such as deblurring and low-light enhancement. However, visual enhancement destroys the visual features and leads to invalid motion feature matching. Besides, a few methods use techniques such as feature fusion [8, 33] and domain adaptation [38, 39, 40, 41] introduce event cameras to improve visual features. These approaches can indeed utilize event cameras to improve boundary completeness, but the appearance features provided by frame cameras still degraded and unqualified for motion feature matching. The degradation of features severely affects discriminative models, which map from visual features to motion fields, and generative models, which generate motion fields given visual features. Therefore, we introduce diffusion models to reconstruct the optical flow estimation paradigm and reduce the impact of input visual feature degradation.
Diffusion Model. Diffusion models were originally proposed for image generation [9]. In con- trast to previous discriminative and generative models, they model the denoising process from noisy samples to clear samples. The paradigm of diffusion models has been widely used in various fields
3

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Tou Seep
moster
Autemion Guided Guided Appeorance Boundary Fustor Module
Malti Condition Iurrative Denoliing Decoder
77M-AC44
Drew
Dones
Spane
Synesien
Frant cicode
Aypnnane Packet
Prave
MGOD
Event
x
Weanday Punice
Hond
Spane 0
LG
Complonnns
Becellay
goont
MCC-10
Frams bourd
loval Fuider
Feod
Figure 2: Overall framework of Difo-ABFlow. Diff-ABFlow mainly contains two parts: Attention- ABF for feature fusion and MC-IDD for denoising. In Attention-ABF, we utilize the appearance- boundary complementarity to fuse frame and event. In MC-IDD, we first integrate time embedding. visual feature and motion feature in the TVM-MCA module based on multi-way cross-attention mechanism. Then in MGDD, we input the comprchensive feature and the optical flow of the current time step into multiple GRUs with memory slots for iterative denoising. We repeatedly run MC-IDD certain number of times on the noisy optical flow to obtain the clear optical flow.
of computer vision, such as semantic segmentation [1. 22, 30, 31, 36], depth estimation [15], tra- jectory prediction [13, 18]. The paradigm of these tasks has been reconstructed into the denoising process from noisy information to clear information with visual conditions. The practice in these fields has confirmed the strong robustness and generalization of diffusion models. Previous work has used diffusion models for optical flow estimation [17, 24], which reconstructed the task into a denoising process from noisy optical flow field to clear optical flow field, and achieved promising results. Therefore, we combine the appearance-bounder the paradigm of diffusion models to propose a novel optical flow estimation framework that achieves state-of-the-arr performance with strong generalization and robustness in degraded scenes.
complementarity of frame and event, and
3
Our Diff-ABFlow
3.1
Overall Framework
We propose framework based diffusion models with frame-event appearance-boundary fusion, which reformulates optical flow estimation as a denoising process from noisy flow to clear flow conditioned on frame and event. As shown in Fig. 2, the whole framework can be divided into two parts, one for feature fusion, and the other for iterative denoising based on multi-condition inputs. Based on the image pair and event stream, we extract the frame feature zy and event feature xe respectively, and input them into the Attention-ABF module to obtain the fused feature I fusion which is then used to construct 4D cost volume Ioo. The time step t is encoded into the time embedding e through Sinusoidal Embedding [32] and MLP, and is then input into the TVM-MCA module together with the fused visual feature 2 fusion and motion feature Tev to obtain the enhanced feature TTV M, which is finally input into the MGDD module together with the current optical flow fe for iterative denoising. The MC-IDD module is repeatedly executed to obtain clear optical flow.
3.2 Attention-Guided Appearance- Fusion Module
To verify the gepcarance-boundee complementarity of frame and event, we design an analysis experiment to study the complementarity between frame and event at the feature level. We use the Sobel operator to extract the boundary of frame and event respectively and use K-means clustering to analyze the distribution of appearance and boundary features of frame and event. As shown in Fig. 3, the frame provides dense appearance saturation and sparse boundary completeness, while the event is the opposite. This verifies the appearance- -ruundaace-buuddryy complementarity of frame and event, which motivates us to design the Attention-
Appearance Boundaryy Fusion Module.
In the Attention-ABF module, for the input frame features x and event features Te, we first utilize appearance and boundary extractors to obtain appearance and boundary representations: [m (t)b and [Zea: Zeo]. Then obtain features with dense appearance saturation Tappear and features with

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Framoo
Event
Frame
Event
Appearance- Boundary Feature Distribution
Apperrance
Deasc
Frene feilro
Foer Sunce
Appeaveen Succr
Deundrry foore
boundary caing cmmplennees
Boundary
appoinecc
pemplesnou
High-speed scene
Low-light scene
Figure 3: Appearanc-boundary feature distribution of frame and event in high-speed and low- light scenes. We use K-means clustering to analyze the distribution of appearance and boundary features from frame and event features. The frame image has dense appearance saturation but sparse boundary completeness due to the motion blur of high-speed scenes and the insufficient illumination of low-light scenes. On the contrary, the event stream provides complete boundary in such degraded scenes while its appearance saturation is sparse. This motivates us to design a feature fusion module to fuse the two modalities utilizing the appearance-bounday complementarity.
dense boundary completeness Thound based on two cross-attention modules:
Tappeor CAtten( fax Tea)) Thound CAtten(I Teb).
Subsequently, we utilize the self-attention mechanism fuse appearance and boundary information from two features: I fusion = SAtton SAtton(Eappear: Tbound)
3.3 Multi-Condition Iterative Denoising Decoder
To intuitively demonstrate the superiority of diffusion models with deteriorated input features, we select Transformer-basee discriminative method and GAN-based generative method to study the robustness to degraded inputs. Given degraded visual inputs, we utilize t-SNE to analyze the visual features and corresponding motion labels from three models. As shown in Fig, 4, both discriminative models and generative models have a certain degree of deviation when accepting degraded inputs, while the denoising process of diffusion models is almost unaffected, which motivates to design an optical flow estimation backbone based on diffusion models, called MC-IDD.
MC-IDD utilizes the fused visual feature I fusion. motion feature Tev and time embedding e, as con- ditions to denoise the optical flow field f, at the current time step. MC-IDD includes two main parts, where TVM-MCA is used integrate three conditions to obtain the feature ITVM that contains temporal, visual, and motion information, while MGDD uses the comprehensive feature TTV M to guide the denoising process based on GRU with memory slot.
Time- Visual-Moiion Multi-War Cross-Attention Module. The TVM-MCA module mainly uses two-way cross attention and gated fusion to effectively fuse time, vision, and motion features. Based on time embedding, we split it into visual query vector Q and motion query vector Qm. each of which fused with the visual features and motion features using cross-attention, to obtain time-visual attention features A, and time-motion attention features Ami
1QK Vd
Qm Vd
A Soflmar
Vu, Softmax
Vm.
where K,, Vo and Km, Vm are obtained by flattening the visual feature I Fusion and the motion feature Ten, and projecting them through linear layers, respectively. d denotes the number of feature dimensions. Then we utilize the learnable MLP to calculate the weights g of the two attention features and perform weighted fusion obtain TTV
TTVM g A. + g) Am.
which will be used to guide optical flow denoising later.
Memory-GRU Denoising Decoder Based on the paradigm of diffusion models, we first perform forward diffusion process the ground truth to obtain noisy optical flow, which gradually adds
5

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Discriminative models
Generative models
Diffusion models
Misclassification
Missampling
Narmal features
Deteriorated leulures
Fealures of motion mode
Features of motion mode 2
Figure 4: t-SNE of visual features and corresponding motion labels from three different models. Obviously, when inputting degraded features into those models, there exist misclassifications in discriminative models and missamplings in traditional generative models, while diffusion models demonstrate strong robustness to degraded inputs. This motivates us to introduce the paradigm of diffusion models and design denoising decoding module for optical flow estimation.
Gaussian noise to the flow T times using Markovian chain. The process is formulated as:
q(f, fo) = N (fe Vārfo. (1 -ā))). t (0.1....,T); where fo indicates the ground truth of optical flow and f, denotes the noisy flow. og is defined as Ge == II Og = Io 32), where 3g is the pre-defined noise variance schedule, indicating the degree of Gaussian noise applied each step.
(4)
When it comes to the reverse denoising process, our proposed MGDD module utilizes Gated Recur- rent Unit (GRU) with a memory slot to iteratively denoise the optical flow f, First, B and the stored memory are jointly input into GRU as conditions and are encoded into latent features together with the optical flow. The latent features are then used update the memory slot and input into the flow head to obtain the coarse flow prediction fg. The memory slot used to store hidden features in the current iteration, which helps retain feature details each noise level. Then follow the DDIM [26] paradigm to calculate the denoised optical flow as Eq. 5. After N iterations, the optical flow f of the next time step is obtained as:
f = Ta + /I a4-1 Eli
(5)
where é denotes the predicted noise at time step t: és = f--vatt""
For the noisy optical flow fr. we run the MGDD module K times to obtain the denoising sequence (fr, fr-
vT-at
3.4 Optimization
For each prediction of optical flow in the denoising sequence ff-,fr-1, kr-K], introduce three loss functions to supervise the learning of the network. To supervise the optical flow prediction with the ground-truth data, we adopt an L1 loss between the predicted flow and the ground-trun flow for which formulated as:
Lftow lifo fll.
(6)
which directly minimizes the endpoint displacement error. Then we utilize the frame to add smoothness loss with boundary-aware term, which encourages the predicted optical flow to be spatially smooth while preserving the boundary of optical flow:
Lamonth [(Vai(a,)) ee V( + v (x, ((
where [xx 3) denotes the first input frame and the weight of coundary-wward term. Finally we utilize event data E, (I., y) to introduce event consistency loss, which encourages the consistency between flow and event thus improving the accuracy of optical flow the boundary area:
Levent
Σ [x, v) Ext1(x (2., v). y + (.(xv))))
(8)
2.9

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Table Quantitative results on synthetic and real datasets, where VE denotes Visual Enhancement.
Discriminative model
Generative model
Method
GMA [14]
FF [F[25] [7] BFlow [8]
ABDA-Floo FD [17]
Ours
w/o VE w/vE
w/o VE w/vE
Input
Frame
Frame
Event
Frame-event
Frame-event
Frame Frame-event
EPE
1.71
1.73
0.69
2.49
0.55
0.53
1.02
0.62
0.46
HS-KITTI
Fl-all L 11.44
12.08
2.18
16.99
1.90
1.81
3.27
1.94
1.12
EPE 1.98
1.83
0.71
3.08
0.68
0.69
0.64
0.67
0.59
LI-KITTI Fl-all 4 12.36
11.76
2.85
19.21
2.54
2.53
2.46
2.43
2.23
EPE 2.21
2.25
1.61
2.72
1.15
1,25
1.85
1.17
1.09
HS-DSEC Fl-all 4 9.65
10.45
7.32
13.87
4.13
4.93
10.43
4.78
3.83
EPE 2.43
2.41
1.70
3.49
1.73
1.76
1.62
1.69
1.50
LL-DSEC Fl-all
12.78
12.02
9.65
18.56
6.48
6.97
5.69
6.03
4.39
High Hlghhsspun speee
Low- light
(a) Frames
(b) Events
(c) FlowFornert
(d) BFlow
(e) Flow Diffuser
(e) Ours
Figure 5: Visualization results on real high-speed and nighttime images of HS-DSEC and LL-DSEC.
where (z, y) and (a, y) respectively denotes the horizontal and vertical components of the flow. In summary, the total loss function is formulated as:
Lilo + Asmooth Lsmooth + Aevent Levents
(9)
where Aemooth and devent are the weights for corresponding losses.
4 Experiments
4.1 Experiment Setup
Dataset We conducted extensive experiments on both synthetic and real datasets. The synthetic datasets, HS-KITTI and LL-KITTI, are derived from the KITTI2015 dataset [20] by applying mo- tion blur and low-light processing, respectively, where the v2e model [10] used generate corre- sponding event streams. The real datasets include HS-DSEC, obtained by applying motion blur to the DSEC dataset [6], and LL-DSEC, which consists of nighttime segments from the original DSEC dataset. In addition, we propose High-Speed Frame-Event Flow Dataset (HS-FEFD) and Low- Light Frame-Event Flow Dataset (LL-FEFD), which are collected by our custom-built frame-event co-optical axis imaging device in various scenes. Note that our proposed datasets are intended for generalization evaluation and are not used for training.
Implementation Details For model parameters, set the diffusion step number T as 50 for forward diffusion following DDIM [26], the iterative decoding number in the MGDD module and the denoising step number K as for reverse denoising, During the training phase, we first pre-
train the model on AutoFlow [27], FlyingChairs [3], FlyingThings [19], and MPI-Sintel [2]. Then we fine-tune it on the training sets of HS-KITTH LL-KITTI, HS-DSEC, and LL-DSEC respectively. Finally, we conduct comparison and generalization experiments with the trained models on these datasets. All training and evaluation performed on single RTX 3090 GPU.

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Table Quantitative results on the proposed unseen HS-FEFD and LL-FEFD datasets.
Discriminative model
Generative model
Method
GMA [14] [25] E-RAFT [7]
BFlow [8]
ABDA-Flow [38] FD [17]
Ours
Input
Frame
Frame
Event
Frame-event
Frame-event
Frame Frame-event
EPE +
8.99
7.82
16.85
6.18
8.35
6.72
4.69
HS-FEFD
Fl-all
58.11
57.24
72.35
45.84
57.96
49.51
28.77
EPE +
11.07
9.41
15.79
7.53
6.90
7.45
5.23
LL-FEFD
Fl-all
65,82
59.36
75.89
55.73
43.31
52.04
31.49
High speeedeeeed
Low- light
(a) Frames
(b) Events
(c) Flowerrmett+
(d) BFlow
(e) FlowDiffuser
(e) Ours
Figure 6: Visualization results on the proposed unseen HS-FEFD and LL-FEFD datasets.
Comparison Methods We select multiple methods with different input settings and paradigms for comparison. For methods based on frame, we choose GMA [14] and Flrmertt (FF) [25] that use discriminative models and FlowDiffuser (FD) [17] that uses generative models. For meth- ods based on event, we choose E-RAFT [7] and for methods based on frame-event, BFlow [8] is chosen. These methods all use the same training process ours to ensure fairness. In addition, we add deblurring and low-light enhancement to some of these methods test the impac visual enhancement methods optical flow estimation. For evaluation, we choose End-Point-Error (EPE) and the percentage of erroneous pixels (Fl-all) as metrics for quantitative evaluation.
Comparison Experiments
Comparison on Synthetic Datasets. In Table 1, we list the evaluation metrics of the proposed method and all the comparison methods on synthetic datasets. Obviously, our proposed method significantly outperforms all competing methods with different input data and different paradigms. In addition, the visual enhancement method does not significantly improve the metrics of optical flow estimation, and sometimes even makes the results worse.
Comparison on Real Datasets. In Table and Fig. 5, we compare the proposed method with competing methods in real high-speed and nighttime scenes. First, can conclude that the method with frame-event input performs much better than those with only frame or event input, which con- firms that the complementarity of frame and event is beneficial for optical flow estimation. Second, for the methods with the same frame input, the method using diffusion models is significantly bet- ter than those using discriminative models, which verifies the excellent performance of diffusion models. Finally, the metrics and the visualization results have demonstrated the superiority of our proposed method based on diffusion models with frame-event complementarity fusion.
Generalization for Unseen Datasets. In Fig. 6, we compare the generalization on our proposed datasets. Under unseen real high-speed and low-light conditions, diseriminative method with frame input fails to estimate optical flow while the frame-event method performs slightly better. The generative method performs well overall, the blurry boundary still exist. On the contrary, our proposed method works well for both appearance and boundary, reflecting strong generalization.
4.3 Ablation Study
Effectiveness Input Modality. In Table 3, we conduct an ablation study on the input modalities. Obviously, utilizing the complementarity of frame and event data significantly improves the accuracy

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Table 3: Ablation study on modalities.
Table 4: Discussion on Fusion strategiies.
Table Discussion on flow estimation backhones.
Modality
EPE
Fl-all 4
Fusion Strategy
EPE + Fl-all
Flow Rackbonn
EPE: 4 Fi-all 4.
w/ Concatenating
Discriminative
FlowFormer
Event
0.65
2.13
w/ Weighting
0.55
1.79
GAN
0.65
2.97
Generative
Frame+Event 0.46
1.12
w/ Attention Guided
0.46
1.12
Diffusion Models
0.46
1.12
Table Ablation experiments proposed modules. Anention-BBF TVM-MCA Memory Slot
Table Discussion diffusion settings.
Methed
Fl-all Inference Timo/mns
x
X
X
0.93
4.73
U-Net
0.63
2.75
97.5
X
X
0.63
2.59
Diflusion Module Conditional-RDD
0.57
1.74
63.2
8
V
0.78
3.87
MGDD
0.46
1.12
64.6
V
0.59
2.09
I
0,61
2.05
37.5
V
X
X
0.73
2.98
2
0.52
1.61
46.4
V
x
0.54
1.63
Denoising Steps
3
0.49
1.35
55.6
V
2.07
0.46
1.12
V
0.46
1.13
of the flow estimation results, achieving much better performance than using single modality alone. This demonstrates that the two modalities provide mutually beneficial information and lead to more robust and precise flow estimation under challenging scenes.
Influence of Proposed Modules. In Table 6, modules reveal the effects of each module. The frame-event fusion module Attention-ABE plays the most important role in improving the results and TVM-MCA follows closely behind. The mem- ory slot GRU also makes a positive contribution.
we conduct ablation experiments on the proposed
4.4 Discussion
How does Feature Fusion Module work? In Fig. 7, in order to reveal the role of the feature fusion module Attention-ABIE we construct 4D cost volumes from frame features, event features, and fusion features respectively and analyze the response intensity histograms corresponding to different gradients to reflect the feature distribution in appearance and boundary areas. Moreover, we provide the flow results from the three cost volumes. On the one hand, the responses of the cost volumes from frame and event are concentrated in low-gradient and high-gradicns intervals, i.c., the appearance and boundary regions, respectively, while the cost volume from the fusion features is evenly distributed in different gradient intervals. On the other hand, the flow inferred from frame has dense appearance saturation but sparse boundary completeness, and the flow inferred from event is the opposite, while the flow obtained by the fusion cost volume has dense appearance saturation and boundary completeness. This shows that our proposed fusion module effectively combines the appearance saturation and boundary completeness from frame and event.
Impact of Feature Fusion Strategies. In Table 4, we discuss the impact of various feature fu- sion strategies, including simple concatenation, weighted fusion, and our proposed attention-guided fusion. The results clearly demonstrate that the attention-guided fusion strategy significantly outper- forms the other two. This superiority arises from its ability effectively utilize the characteristics of frame and event features in appearance and boundary respectively.
Analysis on Optical Flow Backbone. In Table 5, we analyze the impact of different optical flow backbone architectures, including discriminative models (e.g., FlowFormer). traditional gen- erative models (e.g.. GAN-based methods), and the diffusion-bart work. From the results, observe that discriminative and traditional generative models exhibit comparable performance, showing no significant differencee. In contrast, diffusion models achieve substantially better accuracy and generalization in optical flow estimation.
models adopted in our frame-
Choices of Diffusion Settings. In Table 7, we conduct experiments to select the best diffusion module and denoising step. The results demonstrate that our proposed module MGDD outperforms other modules with similar inference time. In addition, the inference time increases linearly with the number of denoising steps. Thus, we set the denoising step number K achieve the best possible results without causing excessively long inference time.

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Table Discussion on computational efficency.
Methods
Modules
Parameters(M) Memory Consumption (GB) Inference Time (ms)
FlowFormer++ [25]
Overall
17.6
13.2
141.2
BFlow [8]
Overall
5.9
10.9
148.7
FlowDiffuser [17]
Overall
16.3
15.4
1 186.9
Attention-ABF
4.5
3.9
52.3
TVM-MCA
3,8
3,5
46.8
Ours
MGDD
8.9
8.4
99.4
Overall
17.2
15.8
198.5
Frame
Frame-cost volume
Event-cost volume
Fusion-cost volume
Response intensity
Event
Gradient amplitude
Gradient amplitude
Gradient amplitude
Flow results
Figure 7: Analysis on featuree fusion module. To analyze the role of the feature fusion module, count the response intensity at different gradients of three cost volumes constructed from frame, event, and fusion features and compare the flow results from those cost volumes. The results demon- strate that the Attention-ABF module effectively utilizes the appearance- bounddary complementarity of frame and event, and obtains fusion features with saturated appearance and complete boundary.
Discussion on Computational efficiency. For the purpose of verifying the computational effi- ciency of each module in proposed method, we conduct some additional experiments on images from HS-DSEC and LL-DSEC with a resolution of 640 x 480 to test the number parameters, memory consumption, and inference time of the modules and other optical flow methods, using single RTX 3090 GPU as the inference platform. As shown in Table 8, the computational cost of each module in proposed method remains within reasonable range. Morcover, approach achieves substantial performance gains with only a minor increase in computational cost.
Limitations Our proposed model performs well under both high-speed and low-light conditions, but fails to estimate optical flow when facing textureless planes. Neither frame nor event cameras can capture discriminative visual signals for the textureless planes since there exist spatial brightness changes. In future work, we plan to incorporate another visual sensor, LiDAR, to perceive the distance from sensors the planes thus obtaining the optical flow.
5 Conclusion
In this work, we propose novel diffusion-based framework with event-frame appearance-boundary fusion for optical flow under both high-speed and low-light conditions. We are the first to utilize the paradigm of diffusion models with fused frame and event to solve the problem of optical flow in degraded scenes. We design the effective appearance-boule bounddrry fusion module Attention-ABF to lead the fusion of frame and event, taking advantage of their respective characteristics. In addition, we propose the innovative diffusion-basse optical flow backbone MC-IDD, which aggregates infor- mation from multi-aspects including time step, visual features, and motion features, to guide the denoising process. Ours proposed method Diff-ABFlow achieves state-of-the-aar performance far ahead previous methods. believe that the multi-conditio guided denoising diffusion paradigm we proposed can be used not only in the field of optical flow estimation, but also many other fields of computer vision such as depth estimation and semantic segmentation.
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Acknowledgments and Disclosure of Funding
This work was supported in part by the National Natural Science Foundation of China under Grant U24B20139. The computation is completed in the HPC Platform of Huazhong University of Science and Technology. We also thank the reviewers for their constructive comments and suggestions.
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Reviews:
[{"review_id": "L76drgUbEm", "reviewer": "Reviewer_dJAt", "summary": "This paper proposes a diffusion-based method for event-guided optical flow estimation. Its main contribution is a novel feature fusion strategy that employs triple-branch cross- and self-attention to condition the DDIM process, along with innovative designs in the diffusion denoiser incorporating GRUs, memory slot, and other elements. Experimental results demonstrate the effectiveness of the proposed method.", "questions": "1. What is meant by “appearance saturation”? Does it refer to the abundance of appearance textures within a modality? If so, what do “dense” or “sparse” appearance saturation specifically mean — do they indicate an abundance or lack of appearance textures? Although these terms do not obscure the paper’s overall motivation (i.e., using events for boundary information and frames for appearance features, which are complementary for optical flow estimation), they can be confusing upon first reading.\n\n2. Why do the authors apply cross-attention between the time embedding and the two modality features? The time embedding primarily encodes the diffusion step index and does not appear to share semantic information with the modality features. Is the intention for the time embedding to act as a bridge between the two modalities? Clarifying this design choice would be helpful.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n  1. The application of diffusion models to event-based optical flow has not been widely explored.\n  2. The experimental results appear promising.\n  3. The graphical illustrations are clear and helpful.\n\nWeaknesses\n  1. There are other works that employ diffusion models for optical flow estimation, such as FlowDiffuser (CVPR 2024), which also uses a GRU-based denoiser. What distinguishes this work from FlowDiffuser? Simply adding an event modality with a cross-attention fusion mechanism may not constitute a significant innovation.\n  2. The clarity of the paper could be improved. For instance, terms like sparse/dense appearance saturation are confusing. Saturation is typically used to describe colors, and its usage here is unclear and distracting.", "was_revised": true, "final_justification": "I have read the authors' rebuttal, which addresses most of my concerns. Therefore, I have raised my score.", "rebuttal": "We thank the reviewer for affirming our contributions: the pioneering exploration of diffusion models for event-based optical flow, promising experimental results, clear and helpful graphical illustrations.\n\nRegarding the raised comments, we summarize them into three aspects: the difference from FlowDiffuser, clarification of some concepts, and methodological details.\n\n### Q1:Differences between the proposed method and FlowDiffuser.\n\n**A:** It is emphasized that our method is different from FlowDiffuser in three typical aspects: task, motivation and framework. \n\n(1) Regarding the task, FlowDiffuser is designed for optical flow estimation in conventional scenes. In contrast, our proposed method explores the temporal and spatial degradation of frame images caused by motion blur in high-speed scenes and texture loss in low-light scenes, which in turn leads to inaccurate optical flow estimation.\n\n(2) Regarding the motivation, FlowDiffuser introduces diffusion models with promising performance for optical flow estimation to obtain a more generalizable and robust model. In addition, we introduce event modality and exploit the appearance-boundary complementarity between frames and events to solve the temporal and spatial degradation of frame images in high-speed and low-light scenes.\n\n(3) Regarding the framework, FlowDiffuser simply designs an optical flow iterative denoising decoder based on diffusion models. Differently, we propose Attention-ABF and MCIDD. The former adopts cross-attention module to focus on the appearance information of the frame modality and the boundary information of the event modality, respectively, and subsequently fuse them through the self-attention module to obtain better visual features. The latter integrates information from time steps, visual features, and motion features to guide the denoising process.\n\nOverall, the task with optical flow estimation in high-speed and low-light scenes, the motivation with frame-event appearance-boundary complementarity, and the framework with diffusion models guided by fused frame and event features jointly constitute a significant innovation of our method.\n\n### Q2: Clarification of “appearance saturation”.\n\n**A:** We apologize for causing your misunderstanding on the concept of appearance saturation.\n\nIn this paper, appearance saturation refers to the abundance of appearance texture information within a visual modality. It reflects the degree of spatial variation in pixel intensity caused by fine-grained textures, shading, and color details. Dense appearance saturation refers to the abundance of fine-grained textures, shading, and color details, while sparse appearance saturation is the opposite. Modalities with dense appearance saturation, such as frame images, preserve abundant structural and photometric cues, which are helpful for establishing correlations between two consecutive frames and further estimating optical flow. In practice, appearance saturation can be quantified by measuring the variance of local gradient magnitudes, emphasizing the presence of detailed visual patterns:\n$$\nS_{appear} = \\mathrm{Var} \\left( \\sqrt{ \\left( \\frac{\\partial I}{\\partial x} \\right)^2 + \\left( \\frac{\\partial I}{\\partial y} \\right)^2 } \\right),\n$$\nwhere $ I $ denotes frame image or event frame constructed from original event stream.\n\nIn addition, we also provide the definition of boundary completeness. Boundary completeness denotes the continuity and integrity of object boundaries within a modality. It evaluates how well the modality captures clear, coherent, and complete boundary structures. Dense boundary completeness means that the visual modality has clear, continuous, and complete boundaries, while the boundaries of a visual modality with sparse boundary completeness may be blurred, interrupted, or even missing. For modalities with dense boundary completeness, such as events, the captured boundaries are clear and tightly connected in space, which plays an important role in obtaining optical flow with clear and complete boundaries. To quantitatively calculate boundary completeness, we first obtain the boundary map through the Sobel operator, and then calculate the ratio of the number of pixels of connected boundaries in the boundary map to all boundary pixels as the boundary completeness:\n$$\nS_{bound} = \\frac{ \\sum_{c=1}^{C} L_c }{ \\sum_{i=1}^{N} L_i}，\n$$\nwhere $ L_c,L_i $ denote the pixel length of connected boundaries and all boundaries, respectively. $C,N$ are the number of connected boundaries and the number of all boundaries, respectively.\n\nDue to motion blur in high-speed scenes and insufficient illumination in low-light scenes, frame cameras with long imaging time and low dynamic range have dense appearance saturation but sparse boundary completeness, while event cameras with short imaging time and high dynamic range have dense boundary completeness but sparse appearance saturation. Therefore, we exploit the complementarity between frames and events in appearance saturation and boundary completeness to design a fusion module to obtain better visual features.\n\n### Q3: Explanation of the design of using cross-attention mechanism between time embedding and motion/visual features.\n\n**A:** It is worth noting that we actually apply the cross-attention mechanism between time embedding $e_t$ and motion/visual features $x_{cv}/x_{fusion}$, \n\nrather than between time embedding and two modality features. Rather than serving as a bridge between the two modalities, the time embedding acts as a controller, enabling the denoising module to perform adaptive denoising behavior at different time steps. Specifically, in the initial stage with more noise, the module should remove as much noise as possible, while in the later stage with less noise, it should consider preserving the optical flow details while removing noise. Based on this motivation, we use the cross-attention mechanism between time embedding and motion/visual features in order to enable the model to learn to adaptively extract features according to the denoising time step. The features are subsequently fused to guide the denoising process, thereby realizing the regulation of the denoising behavior by the diffusion time step."}, {"review_id": "EYvlZNZy41", "reviewer": "Reviewer_qEsh", "summary": "The paper presents a framework called Diff-ABFlow for optical flow estimation in high-speed and low-light scenes. The authors propose using diffusion models combined with frame-event appearance-boundary fusion to address the challenges posed by motion blur and insufficient illumination. The framework leverages the complementary strengths of frame cameras (dense appearance saturation) and event cameras (dense boundary completeness) to improve optical flow estimation. Key components include the Attention-Guided Appearance-Boundary Fusion module and the Multi-Condition Iterative Denoising Decoder . Extensive experiments demonstrate the superiority of the proposed method over existing approaches.", "questions": "1.\tPaper states: ‘Kv, Vv and Km, Vm are obtained by flattening the visual feature xf usion and the motion feature xcv, respectively.’ Does this imply that no additional projection layer is applied—so that K_v is actually identical to V_v, and likewise K_m to V_m, with the different symbols used only for notation?\n2.\tHow does the proposed method perform in other challenging conditions, such as foggy or rainy environments? It would be valuable to test the framework in a wider range of scenarios.", "limitations": "The authors have discussed the limitations of their work, particularly the difficulty in estimating optical flow for textureless planes. They propose incorporating LiDAR sensors in future work to address this issue. However, the paper does not explicitly discuss potential negative societal impacts.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n+ The Attention-ABF module combines the strengths of frame and event cameras, leading to improved visual features. Dual modal features are used to guide diffusion models for optical flow estimation.\n+ Multi-Condition Iterative Denoising Decoder are used to progressively denoise the optical flow. Detailed ablation studies are conducted to demonstrate the contribution of each modules.\n+ The authors conduct extensive experiments on multiple datasets, including synthetic and real-world scenarios, to validate their method.\n\nWeaknesses:\n- The proposed framework is complex, involving multiple modules and iterative processes, which may pose challenges for implementation and computational efficiency.\n- The method is primarily tested on high-speed and low-light scenes, and its generalizability to other challenging conditions is not thoroughly explored.", "was_revised": true, "final_justification": "The paper presents a framework called Diff-ABFlow for optical flow estimation in high-speed and low-light scenes. Most of my questions are addressed in the rebuttal. Therefore, I maintain my score.", "rebuttal": "We thank the reviewer for affirming our contributions: the pioneering exploration of utilizing dual modal features to guide diffusion models for optical flow estimation, detailed ablation studies, and extensive experiments.\n\nRegarding the raised comments, we summarize them into three aspects: implementation and computational efficiency of the method, generalization under other challenging conditions, and clarification of some notations.\n\n### Q1: Implementation and computational efficiency of our proposed method.\n\n**A:** The framework seems complex, but is simply modularized into two components: Attention-ABF and MCIDD. The former performs appearance-boundary complementary fusion of frames and events through cross-attention mechanism and self-attention mechanism, and the latter adopts cross-attention mechanism to aggregate information from multiple conditions, including time steps, visual features, and motion features, which is subsequently used to guide the optical flow denoising process.\n\nIn order to compare the computational efficiency of these modules with different optical flow methods, we conduct some additional experiments on images from HS-DSEC and LL-DSEC with a resolution of 640×480 to test the number of parameters, memory consumption, and inference time of the methods, using a single RTX 3090 GPU as the inference platform.\n\nAs shown in the table below, we can observe the following:\n\n- The computational efficiency of each module in our proposed method is within a reasonable range.\n- Resulting from the additional introduction of event modality and the nature of iterative denoising of diffusion models, although the overall metrics of our method are slightly higher than those of existing methods, it achieves a significant improvement in performance.\n\n|   Methods    |    Modules    | Parameters (M) | Memory Consumption (GB) | Inference Time (ms) | EPE  |\n| :----------: | :-----------: | :------------: | :---------------------: | :-----------------: | :--: |\n| FlowFormer++ |    Overall    |      17.6      |          13.2           |        141.2        | 1.67 |\n|    BFlow     |    Overall    |      5.9       |          10.9           |        148.7        | 1.46 |\n| FlowDiffuser |    Overall    |      16.3      |          15.4           |        186.9        | 1.44 |\n|     Ours     | Attention-ABF |      4.5       |           3.9           |        52.3         |  -   |\n|     Ours     |    TVM-MCA    |      3.8       |           3.5           |        46.8         |  -   |\n|     Ours     |     MGDD      |      8.9       |           8.4           |        99.4         |  -   |\n|     Ours     |    Overall    |      17.2      |          15.8           |        198.5        | 1.23 |\n\n\n\n### Q2: Generalization of the proposed method for other challenging conditions.\n\n**A:** It is mentioned that our method is designed to solve the problem for optical flow estimation in high-speed and low-light scenes. Our main idea is to exploit the  the complementarity between frames and events in appearance saturation and boundary completeness to obtain better optical flow in high-speed and low-light scenes.\n\nFollowing the reviewer's comment, we conduct additional experiments on rainy, foggy, large-displacement, and overexposed scenes. For rainy and foggy scenes, we use the Weather-KITTI2015 (Rain-KITTI, Fog-KITTI) and Weather-GOF (Rain-GOF, Fog-GOF) introduced by Zhou et al. [1], with synthetic events generated using the v2e model [2]. For large displacements, we constructed the LD-DSEC dataset by sampling one frame every three frames and re-segmenting events from the original DSEC, using the method of Ce Liu et al. [3] to generate optical flow ground truth. For overexposed scenes, we built the OE-DSEC dataset by selecting frames with intense car headlights or streetlights from DSEC. Each dataset includes 800 randomly selected images for generalization testing. We additionally include CH$^2$DA-Flow [1], an unsupervised method for optical flow in adverse weather, as a comparison baseline.\n\nAs shown in the table below, we can observe the following:\n\n- From the overall perspective, our proposed method performs well in large displacement and overexposed scenes, but perform not so well in the rainy and foggy scenes.\n- In large-displacement and overexposed scenes, our method significantly outperforms others. The fixed temporal resolution and exposure time of frame cameras cause temporal discontinuity and texture loss in bright regions, while event cameras, with their high temporal resolution and dynamic range, capture continuous motion and preserve scene details under such challenging conditions.\n- In rainy and foggy scenes, our proposed method performs poorly, since raindrops and fog not only blur the frame image but also introduce a large number of noise points in the event stream, making it difficult for our method to obtain good visual features.\n\nIn summary, our proposed method performs better under conditions where we can obtain good visual features through the appearance-boundary complementarity between frames and events. However, under conditions where both frame cameras and event cameras are limited, we will explore solutions such as domain adaptation in the future.\n\n|   Scenes   | Metric | GMA [4] | FlowFormer++ [5] | E-RAFT [6] | BFlow [7] | CH$^2$DA-Flow [1] | FlowDiffuser [8] | Ours  |\n| :--------: | :----: | :-----: | :--------------: | :--------: | :-------: | :---------------: | :--------------: | :---: |\n| Rain-KITTI |  EPE   |  8.02   |       7.54       |   18.73    |   6.27    |       5.78        |       4.97       | 4.23  |\n| Rain-KITTI | F1-all |  46.31  |      41.35       |   75.72    |   37.55   |       33.59       |      26.72       | 24.92 |\n|  Rain-GOF  |  EPE   |  6.03   |       4.97       |   14.57    |   4.22    |       3.54        |       3.28       | 3.19  |\n|  Rain-GOF  | F1-all |  35.73  |      31.62       |   57.35    |   21.74   |       22.75       |      18.94       | 17.36 |\n| Fog-KITTI  |  EPE   |  7.59   |       6.75       |   17.54    |   6.19    |       5.98        |       5.72       | 4.89  |\n| Fog-KITTI  | F1-all |  43.62  |      38.91       |   67.91    |   37.29   |       38.94       |      32.47       | 28.74 |\n|  Fog-GOF   |  EPE   |  7.14   |       5.47       |   15.12    |   4.68    |       3.87        |       3.79       | 3.65  |\n|  Fog-GOF   | F1-all |  44.29  |      36.84       |   59.68    |   26.91   |       24.52       |      24.77       | 22.83 |\n|  LD-DSEC   |  EPE   |  5.45   |       4.23       |    8.62    |   3.52    |       4.18        |       3.79       | 2.67  |\n|  LD-DSEC   | F1-all |  34.82  |      30.52       |   47.34    |   21.64   |       28.96       |      24.83       | 12.45 |\n|  OE-DSEC   |  EPE   |  5.68   |       3.91       |    9.43    |   3.04    |       3.96        |       3.25       | 2.38  |\n|  OE-DSEC   | F1-all |  37.04  |      28.79       |   55.86    |   17.28   |       27.43       |      18.42       | 10.94 |\n\n### Q3: Clarification of $K_v, V_v$ and $K_m, V_m$.\n\n**A:** I apologize for not clarifying how we obtain $K_v, V_v$ and $K_m, V_m$. Specifically, we first flatten the visual feature $x_{fusion}$ and motion feature $x_{cv}$ into tokens, and consequently project them into $K_v, V_v$ and $K_m, V_m$ with the same dimension as $Q_v$ and $Q_m$ by learnable linear layers, respectively. Therefore, $K_v$ is actually not identical to $V_v$, and likewise $K_m$ to $V_m$. \n\n### Reference\n\n[1] Hanyu Zhou, et al. Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation. *TPAMI* 2024.\n\n[2] Yuhuang Hu, et al. v2e: From video frames to realistic dvs events. *CVPR* 2021.\n\n[3] Ce Liu, et al. Human-Assisted Motion Annotation. *CVPR* 2008.\n\n[4] Shihao Jiang, et al. Learning to estimate hidden motions with global motion aggregation. *ICCV* 2021.\n\n[5] Xiaoyu Shi, et al. Flowformer++: Masked cost volume autoencoding for pretraining optical flow estimation. *CVPR* 2023.\n\n[6] Mathias Gehrig, et al. E-raft: Dense optical flow from event cameras. *3DV* 2021.\n\n[7] Mathias Gehrig, et al. Dense continuous-time optical flow from event cameras. *TPAMI* 2024.\n\n[8] Ao Luo, et al. Flowdiffuser: Advancing optical flow estimation with diffusion models. *CVPR* 2024."}, {"review_id": "Wh8hzRhh3a", "reviewer": "Reviewer_NzT1", "summary": "The paper proposes Diff-ABFlow, a novel diffusion-based framework for optical flow estimation in high-speed and low-light scenes using complementary fusion of frame and event data. It introduces two key modules: Attention-ABF, which enhances feature fusion by leveraging appearance boundary complementarity, and MC-IDD, an improved DDIM-based backbone tailored for optical flow tasks. Extensive experiments demonstrate that Diff-ABFlow achieves state-of-the-art performance across multiple datasets.", "questions": "1. It is recommended that the authors provide optical flow estimation results under specific weather conditions, which may help to better understand the robustness of the proposed model.\n2. Why do some comparison methods in Table 1 show performance degradation after visual enhancement? Please provide an explanation.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. This paper utilizes the appearance-boundary complementarity of frame and event data to obtain better visual features for optical flow estimation in degraded scenes, and the motivation is clear.\n2. The experiments in the paper demonstrate that the proposed method performs well on both synthetic and real-world data, forming an effective validation loop in the experimental setup.\n\nWeaknesses:\n1. This work shares a similar motivation with ABDA-Flow (ICLR '24), as both use RGB and event data for complementary appearance and boundary information, leading to overlap in novelty.\n2. In lines 198–199 of the paper, the authors claim to have proposed the HS-FEFD and LL-FEFD datasets. However, the visualized images in Figure 6 are identical to those in Figure 6 of ComST-Flow (CVPR '25). The authors are requested to clarify this.\n3. It is recommended that the authors include a comparison with ABDA-Flow (ICLR '24) in Table 1 and add its visualizations in Figure 5.", "was_revised": true, "final_justification": "The authors' rebuttal resolved my concerns. Therefore, I have raised my score", "rebuttal": "We thank the reviewer for affirming our contributions: the pioneering exploration of the appearance-boundary complementarity between frames and events, clear motivation, and abundant experiments.\n\nRegarding the raised comments, we summarize them into five aspects: difference from ABDA-Flow, clarification of the visualized images, comparison experiments with ABDA-Flow, generalization under other challenging conditions, and explanation of the performance degradation of some methods after visual enhancement.\n\n### Q1: Differences between the proposed method and ABDA-Flow [1].\n\n**A:** It is emphasised that our method is different from ABDA-Flow in three typical aspects: task, motivation and framework. \n\n- Regarding the task, ABDA-Flow focuses on spatial degradation in low-light scenes caused by the global exposure of frame cameras, which leads to texture loss in dark regions. In contrast, our method additionally addresses temporal degradation in high-speed scenes, where fixed exposure times cause motion blur in fast-moving objects.\n\n\n- Regarding the motivation, while ABDA-Flow leverages the high dynamic range of event cameras to mitigate spatial degradation, it does not resolve temporal degradation. Our work explores the appearance-boundary complementarity between frames and events to handle both spatial degradation in low-light scenes and temporal degradation in high-speed scenes.\n\n\n- Regarding the framework, ABDA-Flow employs a unidirectional transfer from the event to the frame domain to enhance spatial quality, leveraging the high dynamic range of events. In contrast, our proposed method exploits the appearance-boundary complementarity between frames and events and introduces a bidirectional complementary fusion strategy, effectively addressing both types of degradation.\n\nOverall, the task with temporal and spatial degradation in high-speed and low-light scenes, the motivation with frame-event appearance-boundary complementarity , and the framework with a bidirectional complementary fusion strategy and diffusion-based module jointly constitute a significant innovation of our method.\n\n### Q2: Clarification of the visualized images in Fig. 6.\n\n**A:** Thanks to the reviewer for pointing out this issue. As described in the supplementary materials, the HS-FEFD and LL-FEFD datasets used for generalization testing in Fig. 6 of our paper were collected by our frame-event dual-mode system and subsequently processed. Regarding the appearance of similar images in related studies, it's possible that the authors used the same data source as ours to construct their own datasets. We can confirm that the datasets used in our generalization experiments were collected by ourselves in real-world scenes. Further details about these datasets will be made public upon acceptance of the paper.\n\n### Q3: Additional comparison with ABDA-Flow [1].\n\n**A:** We did not compare with ABDA-Flow before because the author did not open source the code. During the rebuttal period, we contacted the author by email to obtain the source code of ABDA-Flow and conducted additional comparative experiments. As shown in the table below, we can observe the following:\n\n- ABDA-Flow performs well in low-light scenes but fails in high-speed scenes since the method is designed to focus on nighttime conditions.\n- Our proposed method achieves state-of-the-art performance in both high-speed and low-light scenes.\n\n|  Scenes  | Metric |  GMA  | FlowFormer++ | E-RAFT | BFlow | ABDA-Flow [1] | FlowDiffuser | Ours |\n| :------: | :----: | :---: | :----------: | :----: | :---: | :-----------: | :----------: | :--: |\n| HS-KITTI |  EPE   | 1.71  |     0.69     |  2.49  | 0.55  |     1.02      |     0.62     | 0.46 |\n| HS-KITTI | F1-all | 11.44 |     2.18     | 16.99  | 1.90  |     3.27      |     1.94     | 1.12 |\n| LL-KITTI |  EPE   | 1.98  |     0.71     |  3.08  | 0.68  |     0.64      |     0.67     | 0.59 |\n| LL-KITTI | F1-all | 12.36 |     2.85     | 19.21  | 2.54  |     2.46      |     2.43     | 2.23 |\n| HS-DSEC  |  EPE   | 2.21  |     1.61     |  2.72  | 1.15  |     1.85      |     1.17     | 1.09 |\n| HS-DSEC  | F1-all | 9.65  |     7.32     | 13.87  | 4.13  |     10.43     |     4.78     | 3.83 |\n| LL-DSEC  |  EPE   | 2.43  |     1.70     |  3.49  | 1.73  |     1.62      |     1.69     | 1.50 |\n| LL-DSEC  | F1-all | 12.78 |     9.65     | 18.56  | 6.48  |     5.69      |     6.03     | 4.39 |\n\n### Q4: Generalization of the proposed method for other challenging conditions.\n\n**A:** It is worth noting that our method aims to address the optical flow estimation problem in high-speed, low-light scenes. Our main idea is to exploit the  the complementarity between frames and events in appearance saturation and boundary completeness to obtain better optical flow in high-speed and low-light scenes.\n\nFollowing the reviewer's comment, we conduct additional experiments on rainy, foggy, large-displacement, and overexposed scenes. For rainy and foggy scenes, we use the Weather-KITTI2015 (Rain-KITTI, Fog-KITTI) and Weather-GOF (Rain-GOF, Fog-GOF) introduced by Zhou et al. [2], with synthetic events generated using the v2e model [3]. For large displacements, we constructed the LD-DSEC dataset by sampling one frame every three frames and re-segmenting events from the original DSEC, using the method of Ce Liu et al. [4] to generate optical flow ground truth. For overexposed scenes, we built the OE-DSEC dataset by selecting frames with intense car headlights or streetlights from DSEC. Each dataset includes 800 randomly selected images for generalization testing. We additionally include CH$^2$DA-Flow [2], an unsupervised method for optical flow in adverse weather, as a comparison baseline.\n\nAs shown in the table below, we can observe the following:\n\n- From the overall perspective, our proposed method performs well in large displacement and overexposed scenes, but perform not so well in the rainy and foggy scenes.\n- In large-displacement and overexposed scenes, our method significantly outperforms others. The fixed temporal resolution and exposure time of frame cameras cause temporal discontinuity and texture loss in bright regions, while event cameras, with their high temporal resolution and dynamic range, capture continuous motion and preserve scene details under such challenging conditions.\n- In rainy and foggy scenes, our proposed method performs poorly, since raindrops and fog not only blur the frame image but also introduce a large number of noise points in the event stream, making it difficult for our method to obtain good visual features.\n\nIn summary, our proposed method performs better under conditions where we can obtain good visual features through the appearance-boundary complementarity between frames and events. However, under conditions where both frame cameras and event cameras are limited, we will explore solutions such as domain adaptation in the future.\n\n|   Scenes   | Metric |  GMA  | FlowFormer++ | E-RAFT | BFlow | CH$^2$DA-Flow [2] | FlowDiffuser | Ours  |\n| :--------: | :----: | :---: | :----------: | :----: | :---: | :---------------: | :----------: | :---: |\n| Rain-KITTI |  EPE   | 8.02  |     7.54     | 18.73  | 6.27  |       5.78        |     4.97     | 4.23  |\n| Rain-KITTI | F1-all | 46.31 |    41.35     | 75.72  | 37.55 |       33.59       |    26.72     | 24.92 |\n|  Rain-GOF  |  EPE   | 6.03  |     4.97     | 14.57  | 4.22  |       3.54        |     3.28     | 3.19  |\n|  Rain-GOF  | F1-all | 35.73 |    31.62     | 57.35  | 21.74 |       22.75       |    18.94     | 17.36 |\n| Fog-KITTI  |  EPE   | 7.59  |     6.75     | 17.54  | 6.19  |       5.98        |     5.72     | 4.89  |\n| Fog-KITTI  | F1-all | 43.62 |    38.91     | 67.91  | 37.29 |       38.94       |    32.47     | 28.74 |\n|  Fog-GOF   |  EPE   | 7.14  |     5.47     | 15.12  | 4.68  |       3.87        |     3.79     | 3.65  |\n|  Fog-GOF   | F1-all | 44.29 |    36.84     | 59.68  | 26.91 |       24.52       |    24.77     | 22.83 |\n|  LD-DSEC   |  EPE   | 5.45  |     4.23     |  8.62  | 3.52  |       4.18        |     3.79     | 2.67  |\n|  LD-DSEC   | F1-all | 34.82 |    30.52     | 47.34  | 21.64 |       28.96       |    24.83     | 12.45 |\n|  OE-DSEC   |  EPE   | 5.68  |     3.91     |  9.43  | 3.04  |       3.96        |     3.25     | 2.38  |\n|  OE-DSEC   | F1-all | 37.04 |    28.79     | 55.86  | 17.28 |       27.43       |    18.42     | 10.94 |\n\n### Q5: Explanation of the performance degradation of some methods after visual enhancement.\n\n**A:** Actually, we mentioned the relevant explanation in lines 34-35 of the paper. The visual enhancement such as motion deblurring and low-light enhancement indeed improves the apparent visual effect of frame images. However, such improvement may have a negative effect on optical flow estimation since visual enhancement may destroy the pixel matching relationship between adjacent frames, resulting in incorrect optical flow results.\n\n### Reference\n\n[1] Hanyu Zhou, et al. Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow. *ICLR* 2024.\n\n[2] Hanyu Zhou, et al. Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation. *TPAMI* 2024.\n\n[3] Yuhuang Hu, et al. v2e: From video frames to realistic dvs events. *CVPR* 2021.\n\n[4] Ce Liu, et al. Human-Assisted Motion Annotation. *CVPR* 2008."}, {"review_id": "Udnzfq6ZeQ", "reviewer": "Reviewer_2Evi", "summary": "This paper proposes an optical flow estimation method that fuses frame images and event streams, addressing the challenges posed by extreme scenarios such as high-speed motion and low-light environments. The authors argue that frame features exhibit dense appearance saturation but sparse boundary completeness, while event features are the opposite. To this end, a fusion module based on an attention mechanism is proposed to complementarily combine frame and event features. Then, based on a diffusion model, the paper introduces an iterative denoising strategy called MC-IDD to decode motion information from the fused features. Experiments on both synthetic and real datasets demonstrate the effectiveness of the approach.", "questions": "This paper introduces a novel idea of complementarity between frame and event data in terms of appearance and boundary information. However, the experiments are limited to selected extreme scene datasets, with no evaluation on common, non-extreme scenarios to verify generalization and applicability. Additionally, details such as hyperparameters, inference time, and module specifics are lacking. For these reasons, I give a borderline accept as my initial rating.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**:\n\n(1)The paper clearly identifies that frame features possess dense appearance saturation but weak boundary completeness, while event features can compensate for this. The fused features contain more complete information than single-modality features.\n\n(2)The authors propose an attention-based Frame-Event fusion module that enhances both appearance and boundary representation in the fused features.\n\n(3)A diffusion model is used to decode motion information from the fused features via iterative denoising, and experiments show that diffusion models are more robust than discriminative and generative models.\n\n**Weaknesses**:\n\n(1)The authors use the Sobel operator to extract boundaries from both frames and events. However, the Sobel operator typically requires a threshold hyperparameter for non-maximum suppression, and no ablation study is provided regarding this hyperparameter.\n\n(2)In lines 121–122, the authors mention constructing a 4D cost volume using the input frame features. However, according to RAFT [29], the cost volume is constructed by computing correlations between features of consecutive frames. Are these two methods different? If so, a comparison between them should be included.\n\n(3)For experiments on real-world data, the authors only select extreme scene subsets (HS-DSEC and LL-DSEC) but do not specify how these sequences are split from the original DSEC dataset. To verify generalization, the method should also be evaluated on the publicly available DSEC benchmark and compared with other methods.\n\n(4)The authors introduce two new real-world datasets: HS-FEFD and LL-FEFD, claiming them as part of their contributions. The supplementary materials mention that these datasets contain optical flow ground truth, so quantitative generalization experiments should be conducted.\n\n(5)The inference time of Attention-ABF, MIGDD, TVM-MCA, MGDD, and the overall pipeline on KITTI and DSEC datasets should be reported and compared with other optical flow methods (e.g., BFlow [8]).\n\n(6)In the TVM-MCA module, time embeddings from time step t are used. However, the meaning of \"t\" is ambiguous — is it the timestamp from the event stream and frame, or the time step in the MC-IDD denoising process? This should be clarified.", "was_revised": true, "final_justification": "The author has addressed my concerns regarding the generalization ability of the proposed method in general scenarios. Therefore, I have raised my final rating to Accept. I also suggest that the author include inference time metrics for each method in Table 1 of the main paper.", "rebuttal": "We thank the reviewer for affirming our contributions: the pioneering exploration of the appearance-boundary complementarity between frames and events, well-designed modules, and convincing analysis experiments.\n\nRegarding the raised comments, we summarize them into six aspects: explanation of the usage of the Sobel operator, details of constructing the 4D cost volume, clarification of dataset construction and additional comparison experiments, additional quantitative generalization experiments, comparison of computational efficiency, and explanation of some notations.\n\n### Q1: Explanation of the usage of the Sobel operator.\n\n**A:** Thank you for your detailed review comments. Actually we did not perform ablation experiments on the threshold of the Sobel operator because the Sobel operator was only used to obtain boundary maps in the analysis experiment in Fig. 3. In practice, we set the threshold of non-maximum suppression in the Sobel operator to 50 to get a good boundary map visualization result. If necessary, we will provide boundary map visualizations for different thresholds in the final version of the paper to validate our choices.\n\n### Q2: Details of constructing the 4D cost volume.\n\n**A:** The method we use to construct the 4D cost volume is similar to RAFT [1]. The difference between the two lies in the input. RAFT uses two consecutive frames as input to calculate correlations. Our input $x_{fusion}$ is the visual feature obtained by fusing the features of two consecutive frames and the features of the event stream at the corresponding timestamps through Attention-ABF module. Then, we calculate correlations between the fusion visual features $x_{fusion}$ at two timestamps to obtain the 4D cost volume as the motion feature.\n\n### Q3: Clarification of dataset construction and additional experiments on the original DSEC [2] dataset.\n\n**A:** Actually we mentioned the relevant explanation in lines 196-197 of the paper. The HS-DSEC is obtained by applying motion blur to daytime segments from the original DSEC dataset, and the LL-DSEC is composed of nighttime segments from the original DSEC dataset. Moreover, in order to verify the generalization of our proposed method, we conducted some additional experiments to compare the performance of our proposed method with other comparison methods in optical flow estimation on the original DSEC dataset. Specifically, we randomly separated 3888 images from the original DSEC dataset as the training set to train our proposed method and all comparison methods, and the remaining 432 images were used as the test set to test the optical flow estimation performance metrics of each method. As shown in the table below, our proposed method achieves the state-of-the-art performance on the original DSEC dataset for optical flow estimation.\n\n|  Scenes  | Metric | GMA [3] | FlowFormer++ [4] | E-RAFT [5] | BFlow [6] | FlowDiffuser [7] | Ours |\n| :------: | :----: | :-----: | :--------------: | :--------: | :-------: | :--------------: | :--: |\n| DSEC [2] |  EPE   |  1.43   |       1.25       |    2.54    |   1.12    |       1.07       | 0.95 |\n| DSEC [2] | F1-all |  5.89   |       5.38       |    9.84    |   4.26    |       3.54       | 2.73 |\n\n### Q4: Additional quantitative generalization experiments on HS-FEFD and LL-FEFD.\n\n**A:** Following the reviewer's comment, we conduct additional quantitative generalization experiments on HS-FEFD and LL-FEFD for all methods. As shown in the table below, owing to the advantages of event camera to capture clear and complete boundaries in high-speed and low-light scenes, and the strong generalization and robustness of diffusion models, our proposed method achieves the best performance.\n\n| Scenes  | Metric | GMA [3] | FlowFormer++ [4] | E-RAFT [5] | BFlow [6] | FlowDiffuser [7] | Ours  |\n| :-----: | :----: | :-----: | :--------------: | :--------: | :-------: | :--------------: | :---: |\n| HS-FEFD |  EPE   |  8.99   |       7.82       |   16.85    |   6.18    |       6.72       | 4.69  |\n| HS-FEFD | F1-all |  58.11  |      57.24       |   72.35    |   45.84   |      49.51       | 28.77 |\n| LL-FEFD |  EPE   |  11.07  |       9.41       |   15.79    |   7.53    |       7.45       | 5.23  |\n| LL-FEFD | F1-all |  65.82  |      59.36       |   75.89    |   55.73   |      52.04       | 31.49 |\n\n### Q5: Comparison of inference time between the modules in our proposed method and other optical flow methods.\n\n**A:** In order to comprehensively test the computational efficiency of the proposed method, we conducted some additional experiments on images from KITTI with a resolution of 1242×375 and DSEC with a resolution of 640×480 to test the number of parameters, memory consumption, and inference time of the proposed method and the comparison methods, using a single RTX 3090 GPU as the inference platform.\n\nAs shown in the table below, we can observe the following:\n\n- The computational efficiency of each module in our proposed method is within a reasonable range.\n- Resulting from the additional introduction of event modality and the nature of iterative denoising of diffusion models, although the overall metrics of our method are slightly higher than those of existing methods, it achieves a significant improvement in performance.\n\n|     Methods      |    Modules    | Parameters (M) | Memory Consumption on KITTI/DSEC (GB) | Inference Time on KITTI/DSEC (ms) | EPE on KITTI/DSEC |\n| :--------------: | :-----------: | :------------: | :-----------------------------------: | :-------------------------------: | :---------------: |\n| FlowFormer++ [4] |    Overall    |      17.6      |               17.3/13.2               |            179.5/141.2            |     0.73/1.67     |\n|    BFlow [6]     |    Overall    |      5.9       |               15.5/10.9               |            187.6/148.7            |     0.67/1.46     |\n| FlowDiffuser [7] |    Overall    |      16.3      |               19.4/15.4               |            231.3/186.9            |     0.65/1.44     |\n|       Ours       | Attention-ABF |      4.5       |                5.2/3.9                |             68.1/52.3             |         -         |\n|       Ours       |    TVM-MCA    |      3.8       |                4.8/3.5                |             53.2/46.8             |         -         |\n|       Ours       |     MGDD      |      8.9       |               10.9/8.4                |            126.9/99.4             |         -         |\n|       Ours       |    Overall    |      17.2      |               20.9/15.8               |            248.2/198.5            |     0.49/1.23     |\n\n### Q6: Explanation of the meaning of $t$ in the TVM-MCA module.\n\n**A:** $t$ denotes the denoising time step, which acts as a controller, enabling the denoising module to perform adaptive denoising behavior at different denoising time steps. This is achieved by encoding the denoising time step $t$ into the time embedding $e_t$ and fusing it with motion/visual features $x_{cv}/x_{fusion}$ via a cross-attention mechanism, which is subsequently served as a conditioning signal for the denoising process.\n\n### Reference\n\n[1] Zachary Teed, et al. Raft: Recurrent all-pairs field transforms for optical flow. *ECCV* 2020.\n\n[2] Mathias Gehrig, et al. Dsec: A stereo event camera dataset for driving scenarios. *RAL* 2021.\n\n[3] Shihao Jiang, et al. Learning to estimate hidden motions with global motion aggregation. *ICCV* 2021.\n\n[4] Xiaoyu Shi, et al. Flowformer++: Masked cost volume autoencoding for pretraining optical flow estimation. *CVPR* 2023.\n\n[5] Mathias Gehrig, et al. E-raft: Dense optical flow from event cameras. *3DV* 2021.\n\n[6] Mathias Gehrig, et al. Dense continuous-time optical flow from event cameras. *TPAMI* 2024.\n\n[7] Ao Luo, et al. Flowdiffuser: Advancing optical flow estimation with diffusion models. *CVPR* 2024."}]

Author rebuttal:

Meta-review: This paper introduces Diff-ABFlow, a novel framework for optical flow in challenging scenes that effectively fuses frame and event data within a diffusion model. The approach is technically sound, well-motivated by the complementary nature of the two modalities, and demonstrates strong performance. The authors' engagement during the rebuttal period was exemplary, providing a remarkable number of substantial new experiments to address every major reviewer concern.

While the initial submission faced valid questions regarding its novelty compared to recent work and the generalization of its results, the rebuttal effectively transformed the paper. The authors conducted new, direct comparisons against key baselines and comprehensively tested their method on a wider variety of adverse conditions, thoroughly resolving all initial points of contention.

This exceptional effort was reflected in the final reviewer stances, creating a unanimous positive consensus and causing all reviewers to raise their scores in support of the paper. Therefore, because the paper now stands as a solid and comprehensively validated contribution, my recommendation is Accept.
Decision: Accept (spotlight)