EPA: Boosting Event-based Video Frame Interpolation with Perceptually Aligned Learning
Authors: Yuhan Liu
LingHui Fu
Zhen Yang
Hao Chen
Youfu Li
Yongjian Deng
Venue: NeurIPS 2025 poster ()
Keywords: Event-Based Vision
Deep Learning
Computer Vision
Video Frame Interpolation
Primary area: applications

Abstract: Event cameras, with their capacity to provide high temporal resolution information between frames, are increasingly utilized for video frame interpolation (VFI) in challenging scenarios characterized by high-speed motion and significant occlusion. However, prevalent issues of blur and distortion within the keyframes and ground truth data used for training and inference in these demanding conditions are frequently overlooked. This oversight impedes the perceptual realism and multi-scene generalization capabilities of existing event-based VFI (E-VFI) methods when generating interpolated frames. Motivated by the observation that semantic-perceptual discrepancies between degraded and pristine images are considerably smaller than their image-level differences, we introduce EPA. This novel E-VFI framework diverges from approaches reliant on direct image-level supervision by constructing multilevel, degradation-insensitive semantic perceptual supervisory signals to enhance the perceptual realism and multi-scene generalization of the model's predictions. Specifically, EPA operates in two phases: it first employs a DINO-based perceptual extractor, a customized style adapter, and a reconstruction generator to derive multi-layered, degradation-insensitive semantic-perceptual features ($\mathcal{S}$). Second, a novel Bidirectional Event-Guided Alignment (BEGA) module utilizes deformable convolutions to align perceptual features from keyframes to ground truth with inter-frame temporal guidance extracted from event signals. By decoupling the learning process from direct image-level supervision, EPA enhances model robustness against degraded keyframes and unreliable ground truth information. Extensive experiments demonstrate that this approach yields interpolated frames more consistent with human perceptual preferences. *The code will be released upon acceptance.*

EPA: Boosting Event-based Video Frame Interpolation with Perceptually Aligned Learning
Yuhan Liu Linghui Fu', Zhen Yang". Hao Chen', Youfu Lit5, Yongjian Deng2
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University. 36 1600 P.R. China
College of Computer Science, Beijing University of Technology
KKey Lab of Computer Network and Information Integration, Southeast University DDeparmment Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR
*CityU Shenzhen Research Institute, Shenzhen, P.R. China
"yuhanliusstu. xmu, wwu..muuuuuuuu edu. {fulinghui Stulinghhu@emaill @emails. yangzhend, yjdeng@)bjut..o edu.
haochen303@seu, edu. en, meyfli@cityu. edu.hk
Abstract
Event cameras, with their capacity to provide high temporal resolution information between frames, increasingly utilized for video frame interpolation (VFT) in challenging scenarios characterized by high-speed motion and significant occlu- sion. However, prevalent issues of blur and distortion within the keyframes and ground truth data used for training and inference in these demanding conditions are frequently overlooked. This oversight impedes the perceptual realism and multi- scene generalization capabilities of existing event-based VFI (E-VFI) methods when generating interpolated frames. Motivated by the observation that semantic- perceptual discrepancies between degraded and pristine images are considerably smaller than their image-level differences, we introduce EPA. This novel E- E-VFI framework diverges from approaches reliant on direct image-level supervision by constructing multilevel, pngrraddtioonnsensttiii semantic perceptual supervisory signals to enhance the perceptual realism and multi-scene generalization of the model's predictions. Specifically, EPA operates in two phases: it first employs a DINO-based perceptual extractor, customized style adapter, and a reconstruction generator to derive multi-layered, intrraaationnnsensitte semantic-perceplee tures (S). Second, novel Bidirectional Event-Guided Alignment (BEGA) module utilizes deformable convolutions to align perceptual features from keyframes to ground truth with fier-ffrmme temporal guidance extracted from event signals. By decoupling the learning process from direct image-level supervision, EPA enhances model robustness against degraded keyframes and unreliable ground truth infor- mation. Extensive experiments demonstrate that this approach yields interpolated frames more consistent with human perceptual preferences. Codes available at https://gthub..cm//yuuaaa///
fea-
Introduction
Recent years, beeettbbasdd Video Frame Interpolation (E-VFI) has attracted significant attention due to its outstanding performance under extreme conditions. Leveraging the high temporal resolution of event cameras [23 48), E- FI demonstrates clear advantages
frame-based VFI methods in challenging scenarios involving fast motion, severe occlusion, and non-rigid object deformation. It enables more accurate inter-frame motion estimation 40 41 21 31]. or even directly provides temporal priors at the interpolation location 28 g. thereby facilitating the generation of higher- quality intermediate frames. However, previous works often overlook critical issue exposed in such challenging environments: the presence of motion blur and image degradation caused by the inherent
* Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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When training on images with pixel-ieeel degradation
Generalization comparison
High noise
(a) High naise in BS ERGB dataset
[b] Blur in GOPRO dataset
Input Overlay
TLXNet
Ours
To
Possible
loT
For
VFM
$ S'
Degradation insensitive
VFM
lor
degradation
Image level
Feature-level
Supervision
Supervision
Guided Align
Model
Model
ST
Generator
4
The training paradigm proposed Figure 1: Visualization of image degradation public datasets, along with a comparative illustration of our training paradigm versus prior methods, where VFM denotes the visual foundation model with frozen weights..
Previous methods
limitations of conventional imaging sensors, as shown in Fig. I (a)&(b). These methods typically assume that input frames are high quality and rely on image-level supervision during training (Fig. (c)). In fact, when images degraded to certain extent, defect messages conveyed by keyframes or Ground Truth data (GT) would hamper the capability of model to fit the distribution of real world, ultimately hindering the perceptual realism and generalization ability of E-VFI models across diverse scenes.
Taking inspiration from [8] that cerrantic-pereeptuee features (S) of images suffering less from image degradation, in this work, we propose a novel learning framework, EPA, to address above issues of E-VFI tasks through Percepual-based feature Alignment. Unlike prior methods that rely on image-level supervision (Fig. [)). our approach adopts feature-level training to mitigate perceptual degradation caused by overfitting to low- www-uuliity imputs, illustrated in Fig. [[d.. There are two stages contained the EPA. The first stage aims to utilize the visual foundation model (My) extract semantic- semantic-pereeeptull features (8GT) from GT and use the degradation inniiiitiisnnniiiveee advantage of SGT for model optimization. Here, one more thing has to be guaranteed that carefully designed decoder can reconstruct the SGT to its original image format. To this end, we introduce a reconstruction generator (G,) equiped with customized style adapter (A,) for image reconstruction, where some low-level cues or non-saliency regions neglected by M, can be supplemented via A,- The second stage is imposed for aligning the distribution gap bettween semantic- pprruetttcppeeppuulll features from keyframes and Ground Truth data. We achieve this by proposing Bidirectional Event-Guided Alignment (BEGA) module, which performs alignment process under the guidance by inter-frame temporal messages of event data in a hierachical manner. Finally, the aligned perceptual feature is fed into the generator G, for interpolating estimation.
Our contributions can be summarized as follows: (1) We propose EPA, a novel E-VFI framework that learns in the semantic- percetttaa feature space using Eeeradation-inseniiinn supervision, enabling more perceptually realistic frame synthesis. (2) We introduce a Style Adapter to enhance low-level details and non-salient regions overlooked by vision foundation models, improving reconstruction quality. (3) We design a Bidirectional Event-Guided Alignment (BEGA) module, which utilizes fine- grained motion cues from events guide hierarchical feature alignment. (4) Extensive experiments on synthetic and real-world datasets demonstrate that our method consistently outperforms prior approaches in perceptual quality and generalization.
Related Work
2.1 Video Frame Interpolation
Motion-based frame interpolation methods dominate traditional VFI, typically relying on optical flow to warp keyframes. Enhancements such bidirectional flow [7], coarse-to-fo to-fee refinement
have improved performance, these methods remain vulnerable to severe occlusions. To address this, synthesis- based approaches 38 20 avoid warping errors but demand more temporal information, increasing complexity. Both paradigms struggle under large motions due to substantial inter-frame gaps.
37.. correlation-basee updates [24 35), and motion-synthesis coupling [66 12

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Tunable
Frezen
Reconstruction Generator
VFM
VFM
Bidirectional Guint-Guided Alignment
Style Adapter
ger
P
lpr
Reconstruction Generator
Po salrr
Guide Alignment
Style Adapter
Eguet
Style Adapter
Training
Training Stage
Figure 2: The pipline of the proposed EPA. Modules with the same name share weights.
have emerged to produce perceptually convincing frames, albeit often sacrificing pixel-level accuracy due to their stochastic nature.
Recently, generative methods [5 45
2.2 Event-based Video Frame Interpolation
Event cameras, with microsecond-level temporal resolution, enable dense inter-frame reconstruction and have driven rapid progress in event-based video frame interpolation (E-VFI), Most methods
exploit events to improve optical flow estimation 40
46
25
49
51
43
I3
29
27 Advances such
as spline-based modeling [J]. unsupervised consistency 10.
recurrent architectures [3)] multi-cost
volumes (21). and piecewise flow fitting 31 have improved motion estimation, yet non-rigid motion
and occlusion remain challenging. To mitigate these issues, synthesis based approaches [8] 22 have
emerged, avoiding flow-related errors. However, the issue of real semantic perception deviation caused by frame degradation remains largely unaddressed. Although some methods [39 jointly optimize denoising and interpolation, this multi-task formulation tends compromise task-specific performance and introduces unnecessary complexity.
3 Method
This section aims to describe the E-VFI framework. EPA, from problem formulation detailed architectural designs. Given two input keyframes To and In along with the events Eg-1 between them, our objective is to generate the intermediate frame I, at a specific timestamp t E [0, 1]. In detail, shown in Fig. P EPA is composed of two stages:
The first stage contains three components, i.e., Vision Foundation Model M,, a Style Adapter A., and reconstruction generator G,,, where M is for extracting semantic-perceptua features S from images and A, & G, are introduced for reconstruct high-fedility images from corresponding S. In the second stage, En-11 is split into two subsets Eu-r and E1-tr, which are then converted into voxel grids Vo-r and V1-+. These event voxel representations, together with the semantic features (Sfeyli (0, } extracted from To and I are fed into the Bidirectional wrent-Guided Alignment (BEGA) module, which performs hierarchical alignment under the temporal guidance of event data to fit the semantics of interpolated frame. Finally, G, synthesizes the interpolated frame I, directly from the fitted semantic perceptual features. In the remainder of this section, first motivate our choice of feature-level supervision in Sec. [3.1 then describe our image reconstruction strategy Sec. Sec. detail the design of feature alignment in Sec. 3.3 and conclude with the architectural specifics of each component Sec. 3.4
3.1 The Motivation of Introducing Feature-Level Supervision
typically estimate optical flow from events and warp keyframes to synthesize intermediate frames. However, optical flow mainly captures pixel-level motion, normally ignores object semantics and scene structure, making these methods heavily reliant on high-quality keyframes and GT. When keyframes suffer degradation, their discrepancy from human perception of the real world grows, and training degraded images propagates these defects, undermining perceptual fidelity and generalization across diverse scenes. Inspired by [18), we find that the perceptual discrepancy between degraded and clean images is reduced in the feature space
E- VFI methods 40
31 21

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Vo-e
Style Adapter
Convites
Encoder
Offict
Diom
ConvRes
DC
Office
Estimator
90
Sn. St, 8g
Convites
Corvites
Euttor
S
Style Adapter
Offict
DCom
DConv
Offive
Convites
Comv
I
SE
Conv
Style Adapter
Reiu
BCoo
ConvRes
DCow
Offort Extimttor
Conv
SE
Conv Tanh
Conv
Pj.P1
Feature adaptation
Bidirectional Event-Guided Aligament Module
Concat
Upsample
X Multiply
ConwRes(3x3)
Convites(Xx1
Figure 3: An overview the proposed modules. Upsample refers up-sampling using bilnear interpolation.
compared to the image space (Fig- 8). This motivates us proposing the feature-level supervision strategy, which reduces reliance the quaility of input frames.
While prior works [40, (28) attempt to achieve this goal by applying image-level perceptual losses [19] on the predictions, their interpolation processes still operate in the pixel domain. As a result, the models lack a true understanding of image semantics and tend to overfit to low-level details, which limits their robustness under degraded conditions. In contrast, our method enforces alignment with the ground truth at the feature level, promoting high-level semantic consistency and enhancing resilience to variations in input quality. In specifie, incorporate vision foundation model My that is designed to comprehensively capture object-level semantics in visual scenes [., which has been shown be segradatiioninsensittie [26). From Mf. extract semantic-perceptual features S, which play a pivotal role in guiding the learning process.
3.2 Image Reconstruction from Semantic-Perceptuue Features
After obtaining the semantic representation S from M,, the network that is able to reconstruct a high- quality image from this high-level abstraction is required. To this end, we employ an off-the- fff-the-sheff reconstruction generator G, that is compatible to multi-level feature input for restoring the image from S.
Reconstruction with Style Adapter. From observation, while feature representations from M has already offer strong semantic expressiveness and generalization, they are typically optimized for high-level tasks such segmentation and detection. Consequently, these features tend to overlook low-level cues and non-salient regions. As illustrated in the feature visualizations in F attention like the sky significantly reduced, which adversely affects reconstruction quality. To compensate for this deficiency, we introduce a customized style adapter A,, which complements the high-level semantie features with essential low-level details. As shown in Fig. we first employ a lightweight image encoder to extract low-level features I., which are subsequently passed to A, enrich the semantic representation. Specifically, the proposed style adapter A, consists of convolutional layers and gating mechanism, which performs weighted fusion of S, and I, to produce the final adapted features P., where s E 0, 2. These adapted features enable the G, to recover high-fidelity images with both semantic consistency and fine- tne-grained visual quality through the adapted feature P.
3.3 Feature-Level Alignment with Event-Based Assistance
In the E-VFI setting, the objective is to synthesize the intermediate frame I, from the keyframes To and I along with the inter-frame events En-+1- Unlike methods that operate the image-level, EPA interpolates in the semantic semantic-perceptuee perceptual feature space by estimating the semantic representation the intermediate frame and generating the final frame using pretrained reconstruction generator G,.
Bidirectional Event-Guided Alignment (BEGA) module. In order to obtain the semantic- perceptual features of the interpolations, we introduce the Bidirectional Exent-Guided Alignment (BEGA) module. This module semantically aligns the two keyframes by leveraging the fine-grained temporal cues embedded in the event stream. Intuitively, BEGA should be directly applied to align

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S" from Skeyy However, to compensate for the fine- tineggrined low-level semantics (e.g,, color and texture) that are missing in S during reconstruction, alignment of the I" also required. Our empirical findings reveal that this process is non-trivial due to the inherent limitations of event data: events are insensitive to smooth regions and lack color information. This leads unstable training and sub-optimal reconstruction quality. Motivated by this observation, we instead apply BEGA to jointy align both semantic and low-level features, i.e., direct alignment P This approach preserves richer low-level cues, resulting in more stable training and improved synthesis performance.
As illustrated in Fig BEGA takes the event voxel grids Vo-r and Vi-+ as inputs and processes them through a shared wwight event encoder &g to extract hierarchical features E, and E To retain fine motion cues inherent in the event streams, &g is deliberately kept lightweight, comprising only three convolutional layers to emphasize low-level feature representation. Subsequently, at multiple spatial scales {0. 1, 1,2, we synthesize the intermediate frame features via one-way warping using deformable convolutions [2], followed by occlusion-awes aware refinement with a lightweight SE layer [1] This design enables robust bidirectional alignment and effective fusion of intermediate semantic features, formulated in [
5 Fnc(Foo(C(P2 (0,1)
6, = C(e,, =, FsE(c))
where FDC denotes the deformable convolution, Fog represents the offset estimator, which computes the offset and mask required by FDC, following structure similar to that of 42) FsE refers the squeeze-and-eeciiation operation.
3.4 Network Structure
Specifically, we use a DINO-pretrained ResNet backbone as the vision foundation model (My) y) to extract perceptual features, and adopt normalized flow-based generator [45] as the reconstruction network (G,). During the process, keyframes are fed into M, to extract multi-scale semantic features Skey, which are enhanced by three independent style adapters A, using low-level features Ikey from an image encoder, producing adapted features Pa for each scale {0, 1, These are then passed to Gp to synthesize the intermediate frame Ly.
3.5 Loss
As shown in Fig. 2 in the first stage, we utilize Lpe to ensure the reconstruction consistency, as depicted in Eq
Cpe Lipipo (1. + (I + Call.
where the i denoted the the reconstructed image. The Lipips is perceptual loss introduced 49 which excels at measuring the structural similarity between images. The Lap is variant of the 7.1 loss proposed (14), where the L1 loss computed on the Laplacian pyramid representations of two images. Cuu is the negative likeliild loss, used in the optimization of normalized flow-based generators by [45]
In the second stage, we utilize hybrid loss to emphasize feature consistency, as depicted in Eq. 3
2 Cos = B Celo., + de * (Cele) PGT) + (c( ==
(3)
Leg Li ICT) Atap (I:
where Atap and d2 are set as 0.2, 0.1 respectively. The function La denotes the 1.2 loss that used to align two features.
5

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Table 1: Performance comparison on synthetic datasets. The best results are marked in Bold while the second ones are marked with underlines, We reconstructed all skipped frames for GOPRO.
Vimeo90k
GOPRO
Methods
skip
7 skip
15 skip
IPIPS
FloLPIPS!
DISTS,
LPIPS
F
DISTS
LPIPS
FloLIPIS
DISTS
RIFE
0,021
0.062
0.048
0.029
0.100
0.060
0.051
0.168
0.082
UPR-Net
0,015
0.039
0.037
0.024
0.077
0.052
0.042
0.140
0.067
Timelens
0.022
0.040
0.052
0.009
0.033
0.031
0.012
0.047
0.036
CBMNet
0.012
0.021
0.039
0.012
0.050
0.046
0.013
0.058
0.050
TLXNet
0.089
0.142
0.116
0.028
0.052
0.049
0.031
0.063
0.053
EPA (ours)
0.007
0.012
0.036
0.006
0.021
0.019
0.008
0.031
0.023
2
1678
2
Input Overlay
Timelens
CBMNet
TLXNet
(ours)
Figure 4: Visual comparison among different methods synthesis datasets.
4 Experiments
4.1 Setup
Training Settings. For the proposed EPA, we first optimize the style adapter and reconstruction generator modules, after which their weights are frozen to train the bidirectional feature alginement module. In the first training stage, method is optimized using AdamW (30) for 100 epochs within the PyTorch [36]. The initial learning rate is set to Ix10 and is gradually decreased to 1x10-6 via cosine annealing. The batch size is set 40 for each training step. In the second stage, the bidirectional adaptation module is trained for 40 epochs under the same configuration. The entire model is trained on GOPRO 33 following
[31], where synthetic event data is generated using the v2e simulator [9] Note that the training of the generator is not restricted to any specific dataset. To ensure reliable generation quality, this work applies NIQE 32 filter out degraded images, retaining only high-quality samples. For data augmentation, both input frames and their corresponding event voxel grids cropped 256 256 and randomly augmented with rotation and flipping. Our normalized flow generation module follows the setup used in [45].
State-of-the-Art Methods. We compare approach against several state-of-the-ar VFI&E-VFI
methods, including RIFE (ECCV'2022) 14, UPR-Net CWVPR'0023 [16), TimeLens (CVPR'200 2021)
[40. CBMNet (CVPR* 2023) 22) and TLXNet (E2CY 3II, using their publicly available implementations. Additionally, to ensure a fair comparison, we re- re-train all competing methods under the same configuration. As TLXNet does not support 6-skip training, train this method using only GOPRO. For works that have not released official code but may demonstrate promising performance attempted reproduce them. However, due unsatisfactory results, do not include them in our comparisons.
46 28
Datasets. We evaluate method both synthetic and real-world event datasets. The synthetic benchmarks include Vimeo90k -Tripet [47] and GOPRO 33] For world evaluation, we use HS- ERGB [4), comprising 15 scenes and various motion types; BS-ERGB [II, characterized by noise and complex non-rigid deformations; and EventAid-F 77. containing various motion scenarios. Note that due space constraints, full evaluation results EventAid-F provided in the supplementary material.
Our goal is to generate images that align with human perceptual preferences, which are not fully captured by traditional image quality metrics as evidenced in Fig. 6 Thus, We primarily adopt the following metries for performance evaluation: LPIPS [50), DISTS (6,, and FloLPIPS (3), as they
Metries.

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Table Performance comparison on real datasets. The best results are marked in Bold while the second ones are marked with underlines.
HS-ERGB
Method
5 skip
7 skip
PSNRT
SSIM
LPIPS.,
FloLPIPS., DISTSİ
PSNR1
SSIMI
LPIPS.,
FloLPIPS,,
DISTS.
RIFE
32.624
0.857
0.032
0.192
0.083
31.150
0.836
0.037
0.212
0.092
UPR-Net
32.235
0.857
0.075
0.170
0.075
30.689
0.834
0.085
0.188
0.081
Timelens
32.760
0.861
0.046
0.112
0.059
31.871
0.851
0.053
0.126
0.065
CBMNet
32.206
0.842
0.098
0.212
0.108
31.876
0.837
0.101
0.218
0.110
TLXXet
31.578
0.827
0.046
0.105
0.054
EPA (ours)
33.842
0.872
0.014
0.057
0.045
33.402
0.867
0.015
0.062
0.048
BS-ERGB
skip
skip
RIFE
25.616
0.765
0.098
0.310
0.067
23.435
0.728
0.114
0.357
0.073
UPR-Net
25.621
0.779
0.104
0.308
0.083
23.081
0.736
0.108
0.335
0.082
Timelens
27.164
0.783
0.052
0.153
0.065
25.855
0.765
0.064
0.202
0.075
CBMNet
29.257
0.814
0.060
0.203
0.087
28.446
0.807
0.063
0.221
0.090
TLXNet
29.298
0.813
0.047
0,088
0.052
28.720
0.807
0.046
0.090
0.058
EPA (ours)
27.943
0.791
0.024
0.068
0.051
27.221
0.782
0.028
0.082
0.057
Input Overlay
Timelens
CBMNet
TLXNet
EPA (ours)
Figure 5: Visual comparison among different methods on real datasets.
better reflect human perceptual judgments of interpolation quality. For completeness, we also include results using conventional metrics such as PSNR and SSIM [4].
Evaluations on Synthesis Datasets
Quantitative Comparison Tab. T presents comparative analysis between EPA and existing methods. Thanks our training strategy and the precise guidance from events, our method consistently achieves the highest perceptual quality on both the Vimeo90k and GOPRO datasets. Notably, on the GOPRO dataset, EPA demonstrates superior alignment between scene content and human perception. This advantage stems from EPA's semantic feature-based fitting, which enables more effective capture of scene semantics and leads to significantly better performance in terms of the DISTS metric compared to other approaches.
Qualitative Comparison Fig. compares E-VFI methods on synthetic dataset. Our method outperforms TimeLens, CBMNet, and TeX under blurred keyframes, producing clearer fingers and roofs. This superiority is attributed to model's enhanced capability to capture scene semantics, enabling the synthesis of images that are better aligned with human perception. These results validate the effectiveness of our framework designs and perceptual feature fitting approach.
7

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CaM
00
CBMNEL
Ours
PSNR: 25.21 LPIPS: 0.317
PSNR: 23.93 LPIPS: 0.141
PSNR: 21.71 LPIPS: 0.191
PSNR: 19.22 LPIPS: 0.182
TUXNet
PSNR: 25.10 LPIPS: 0.156
GT
PSNR: 20.98 LPIPS: 0.184
GT
Figure 6: Visualization of effects and scores of inserted frames for each method in extreme motion
scenes.
Table Performance Comparison on EventAid-F.
Figure Visual comparison on EventAid-F.
Building
Sculpture
Method
PSNR
LPIPS
PSNR
LPIPS
Timelens
31.78
0.037
36.52
0.020
CBMNet
31,42
0.054
34.79
0.052
TLXNet
29.07
0.035
33.85
0.026
EPA (ours)
31.43
0.015
35.15
0.011
Timelens
CBMNet
TLXNet
EPA (ours)
4.3
Evaluations on Real Datasets
Quantitative Comparison Tab. and Tab. presents comparisons real-world datasets, high- lighting the practical effectiveness of our method. Our approach consistently leads perceptual metrics across three datasets. Notably, on the HS-ERGB dataset, EPA also surpasses other methods in traditional image quality metrics such as PSNR and SSIM. We attribute this to the limitations of compared methods in handling the widespread irregular object deformations present in the data, while our method, leveraging semantic-level features, addresses these challenges more effectively.
Qualitative Comparison Fig. 5 and Fig. 7oo--pPeeee presents visual comparison of different E-VFI methods on real-world event dataset. A direct comparison with the outputs of TimeLens, CBMNet, and TLXNet reveals the advantages of our method. Specifically, in Fig. 5 approach ensures better visual quality in various extreme motion scenarios, as demonstrated by more complete candies, fingers, and better-preserved water flow details. From the images produced by CBMNet, observe that when meet noisy keyframes or occlusion situations, the generated interpolated frames exhibit sever blur (e-g.. the candy) and poor-quality (e.g., the water flow). Instead, our method handle such scenerios more effectively on dynamic objects. Similarly, Fig. shows that our method generates sharper and more perceptually aligned results for structures such building and seulptures. We attribute to that benefiting from the perceptual features alignment with visual foundation models, our approach is able to synthesize more realistic images by comprehending the semantics of interpolated scenes to certain extent. Interestingly, as illustrated in Fig. 6 although CBMNet and TLXNet achieve higher PSNR scores, our method still produces visually superior results in extreme scenarios, demonstrating the efficacy of using perceptual metrics (c.g., Ipips/dists) to measure the VFI task.
Table 4: Comparison results of different fea- Table 5: Comparison results of different S synthesis ture extraction settings in training stage one. method settings in training stage two. & denotes events.
Variants Norm. A, PSNR SSIM LPIPS DISTS
Vimeo90k
Variants & SCA BEGA PSNR SSIM I.PIPS MIPS DISTS
Vimeo90k
A B
161 0.983 0.0038 0.0218
28. 834 0.857 0.0215
0.0481
0.0755
40.793 0.991 0.0016 0.0086
E
36.900 0.961 0.0078
0.0129
0.0375
C (ours)
47.732 0.996 0.0002 0.0012
F (ours) V
37.011 0.964 0.0074
0.0124
0.0361
Model Analysis
In this section, we conduct experiments on the Vimeo90k dataset to analyze the effectiveness of the two core components our proposed method: the Style Adapter and the Bidirectional -eiie- Alignment module.

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12: 47.70
27.01
12.16
12: 7.74
sky was ignored
More errors
layer_
Layer_
layer_?
Degraded image
Feature From DINO
(b)
Figure 8: Visualization of comparison results. (a) compares L2 distances between image-level and feature-level supervision, alongside DINO feature visualizations. (b) shows difference maps between the generated results from settings B and C in Tab. and the ground truth.
Rationality and effectiveness of feature-level supervision. In Fig. 8 a). we present a comparison between the differences induced by image-level supervision and those induced by feature-level supervision when an image undergoes image-level degradation, exemplified here by blurring. We the L2 distance an evaluation metric. It can be observed that the feature-level supervision signals extracted from DINO exhibit higher robustness to blur degradation. Furthermore, as the network depth increases, the robustness of the feature supervision becomes pronounced. This improvement is attributed to the VFM's capability to capture image representations from a perceptural perspective, which helps mitigate the impact of image-level distortions, suggesting the reasonableness of our design philosophy to certain extent.
Effectiveness of the Style Adapter (Ag). As presented in Tab. we introduce baseline model A, which utilizes the first three neural blocks of features from DINO, without incorporating shallow image details. Given that DINO is pretrained ImageNet [3] with associated normalization, we introduce model B. which applies both normalization and denormaliza- tion processes. Model normalizes input images using ImageNet statisties and denormalizes the outputs accordingly. Model C is built upon Model and trained in combination with the proposed Ag- The results of settings A and B in Tab. highlight the importance of the adaptation process. Furthermore, the comparison between Settings B and C demonstrates that our choice substantially improves the model's ability reconstructing images from perceptual features, thereby enhancing the quality of the synthesized interpolated frames, as illustrated in Fig, (b).
evaluate the proposed Ag,
Effectiveness of the Bidirectional Event-Guided Alignment (BEGA) Module. As shown in Tab. to verify the importance of event guidance, we construct a baseline model D by removing all event data and performing feature fitting purely based on frames. Additionally, to assess the effectiveness of our specific design, we introduce a comparison model E, which employs fusion module-referred to SCA- comprising self-attention layer followed by cross-attention layer, similar the structure proposed in (21). The comparison between models D and E highlights the significance of event guidance. The precise inter-framm motion priors provided by events significantly enhance the model's ability to fit features of the interpolated frame. Furthermore, the performance gap between models E and F demonstrates the superiority of our proposed module.
Conclusion
In this work, we present EPA, a novel E-VFI framework that addresses the limitations of image- level degradation in modeling real-world data distributions. By leveraging degradation-insensiie insensitive semantic supervision, EPA enhances both the perceptual fidelity and generalization capability of frame synthesis under challenging conditions. EPA's two-stage training paradigm first extracts robust semantic- perceptual features and ensures reconstruction quality via dedicated style adapter. In the second stage, the proposed Bidirectional Event-Guided Alignment module effectively aligns semantic features from keyframes to the ground truth, guided by fine-grained event cues, and synthesizes the interpolation via a pretrained reconstruction generator. Extensive experiments demonstrate that our method outperforms existing approaches in both perceptual quality and generalization, offering more reliable solution for real-world scenarios affected by camera noise and motion blur.
semantic-perceptual

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7 Acknowledgments
This work is jointly supported by Beijing Natural Science Foundation (4252026), National Natural Science Foundation of China (62203024, 62573369), Research and Development Program of Beijing Municipal Education Commission (KM2022100005027)
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Reviews:
[{"review_id": "vSofpLGsyP", "reviewer": "Reviewer_tAbV", "summary": "This paper addresses the problem of event-based video frame interpolation (E-VFI), particularly in challenging scenarios where input keyframes may suffer from degradation like motion blur and noise. The authors argue that conventional methods, which rely on direct image-level supervision, are hampered by these artifacts, limiting their perceptual realism and generalization. To overcome this, they propose EPA, a novel framework that shifts the learning objective from the image domain to a semantic-perceptual feature space. The method operates in two stages: first, it trains a generator to reconstruct high-fidelity images from \"degradation-insensitive\" semantic features extracted by a frozen DINO-based model. Second, it introduces a Bidirectional Event-Guided Alignment (BEGA) module to align the features of the input keyframes to predict the features of the intermediate frame, guided by event data. The final interpolated frame is then synthesized from these aligned features. Experiments on several synthetic and real-world datasets show that EPA produces results with higher perceptual quality compared to state-of-the-art methods.", "questions": "- Could you please elaborate on the rationale for re-injecting low-level features from the input keyframes via the Style Adapter? Given that the paper's primary motivation is to build resilience to defects in these keyframes, this appears counter-intuitive. Is there a mechanism that prevents the adapter from simply passing on the input noise or blur, and if so, how was it validated?\n\n- How do you interpret the significant drop in PSNR/SSIM on the noisy BS-ERGB dataset? Does this suggest that in the presence of heavy degradation, the feature-level supervision prioritizes perceptual \"plausibility\" to such an extent that it sacrifices structural fidelity to the ground truth? How can a user be confident that the generated output is faithful to the scene's motion and not a well-textured hallucination?\n\n- The paper claims to move away from \"direct image-level supervision\" , yet the final training stage employs the L_cg loss, which includes LPIPS and Laplacian loss on the output image. Could you clarify why this is not considered direct image-level supervision?\n\n- Beyond the L2 distance comparison in Figure 8, have you performed any other analysis to more rigorously quantify the \"degradation-insensitivity\" of DINO features? For example, how does the signal-to-noise ratio of the features compare to that of the input images under varying levels of degradation?", "limitations": "Yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths \n- The paper identifies a significant and practical challenge in video frame interpolation.\n- The authors conduct a comprehensive evaluation on a variety of synthetic (Vimeo90k, GOPRO) and real-world (HS-ERGB, BS-ERGB, EventAid-F) datasets.\n- The visual comparisons provided in Figures 4, 5, and 7 demonstrate the proposed method's strength.\n\n\nWeaknesses\n-  The central thesis of the paper is to move away from degraded image-level details by operating in a \"degradation-insensitive\" semantic feature space. However, the proposed Style Adapter (A_s) directly contradicts this motivation. The adapter is designed to \"compensate for this deficiency [of the VFM]... with essential low-level details\" by taking features from a lightweight image encoder that processes the (potentially degraded) input images and fusing them with the semantic features. This re-introduces the very low-level, potentially corrupted information that the framework ostensibly seeks to avoid. This creates a conceptual inconsistency at the heart of the proposed method. The paper fails to justify how this component does not simply propagate the input degradation.\n- The entire framework is predicated on the assumption that semantic features from a VFM like DINO are largely insensitive to image degradation. The evidence provided for this critical assumption is thin. Figure 8(a) shows that the L2 distance between features of a clean and a blurred image is smaller than the L2 distance between the images themselves. This is expected, as feature extraction is a dimensionality-reduction process that inherently discards some pixel-level variance. However, \"lower distance\" does not automatically equate to \"insensitivity\" or a more reliable supervisory signal. The features are still visibly different, and the paper provides no analysis to prove that these feature differences are less harmful to the learning process than the original image-level differences. This assumption requires much stronger empirical and theoretical support.\n- The core ideas underpinning EPA are not new. The use of feature-space supervision via pre-trained networks is the principle behind perceptual losses (e.g., LPIPS ), which have been a staple in image synthesis for years. The paper attempts to distinguish its work by performing explicit feature alignment and generation in this space, rather than just using it as a loss function. However, this is more of an architectural choice than a paradigm shift. The components themselves—a DINO feature extractor, deformable convolutions for alignment , and a flow-based generator —are all existing technologies. The contribution amounts to a novel combination of these parts, which is an engineering contribution.\n- While EPA excels in perceptual metrics, its performance on fidelity metrics (PSNR/SSIM) on the noisy BS-ERGB dataset is alarmingly poor compared to baselines. In Table 2, for 1-skip interpolation, EPA scores 27.943 in PSNR, whereas CBMNet and TLXNet achieve 29.257 and 29.298, respectively. This is a very significant drop. The authors argue that PSNR is not the goal, but a method truly robust to noise should not suffer such a precipitous decline in reconstruction fidelity. This result suggests that the method may be learning to generate perceptually plausible textures that deviate substantially from the ground truth structure, which calls into question its overall robustness.", "was_revised": true, "final_justification": "Thanks for the rebuttal. Most of my previous concerns have been addressed. However, they do not change my assessment of the novelty. The explanation for the Style Adapter is not fully convincing. The argument that a simple gating module can effectively separate desirable low-level details (such as color information for backgrounds) from undesirable degradation artifacts (such as noise and blur) is a strong claim that requires more rigorous validation. The justification provided remains somewhat anecdotal.", "rebuttal": "We thank Reviewer tAbV for devoting time to this review and providing valuable comments.\n\n> ***Q1:**\"The core ideas underpinning EPA are not new. The use of feature-space supervision via pre-trained networks is the principle behind perceptual losses (e.g., LPIPS ), which have been a staple in image synthesis for years. The paper attempts to distinguish its work by performing explicit feature alignment and generation in this space, rather than just using it as a loss function. However, this is more of an architectural choice than a paradigm shift. The components themselves—a DINO feature extractor, deformable convolutions for alignment , and a flow-based generator —are all existing technologies. The contribution amounts to a novel combination of these parts, which is an engineering contribution.\"*\n\nThank you for your insightful analysis of our architecture. \n- Our core contribution lies in proposing a novel framework that fundamentally shifts how event-based video frame interpolation (E-VFI) is approached, especially in the presence of real-world image degradation. Our novelty is threefold:\n\n  1. Addressing a Critical, Under-explored Problem: We attempt to address a common but often overlooked issue in E-VFI—degraded keyframes—in a single-stage manner, moving beyond the idealistic assumption of clean inputs prevalent in prior work.\n\n  2. A True Feature-Space Paradigm: Unlike methods that simply use LPIPS as a final loss function, our EPA framework performs the core operations of motion alignment and interpolation directly within the semantic-perceptual feature space. The primary learning objective (L_bf in Eq. 3) is driven by feature alignment, not pixel-level errors. This is a fundamental architectural and conceptual shift.\n  3. A Novel, Purpose-Built Module: The Bidirectional Event-Guided Alignment (BEGA) module is specifically designed for this new paradigm. It leverages event data to guide the alignment of  semantic features, which is fundamentally different from traditional optical flow estimation on pixels.\n\n- The significant perception performance gains, particularly on challenging real-world datasets (Tables 2, 3), validate that our contribution is more than a novel combination of parts; it is a new, effective paradigm for robust E-VFI.\n\n\n\n> ***Q2:**\"Could you please elaborate on the rationale for re-injecting low-level features from the input keyframes via the Style Adapter? Given that the paper's primary motivation is to build resilience to defects in these keyframes, this appears counter-intuitive. Is there a mechanism that prevents the adapter from simply passing on the input noise or blur, and if so, how was it validated?\"*\n\n\n\n- Our design takes into account the issue you mentioned. Introducing a style adapter to reintroduce low-level features from key frames was a carefully considered decision. \n- Initially, our method was designed without an adapter. However, during experimentation, we observed that the VFM model’s disregard for motion backgrounds led to color shift issues. As you pointed out, we needed an adapter to filter and transfer low-level information from the key frames. In this regard, we explored several off-the-shelf strategies and found that a simple gating module was sufficient to achieve our goal. We believe this could be due to the semantic-detail separation learning approach, which allows for faster convergence even with fewer data. \n- Specifically, after applying the gating module, our PSNR on Vimeo90k improved by 4.32 and LPIPS improved by 0.02, effectively preventing the propagation of degraded information. Since this is not the main focus of our paper, we did not include related experiments in the main text, but we will add them in the revised version.\n\n\n\n> ***Q3:**\"How do you interpret the significant drop in PSNR/SSIM on the noisy BS-ERGB dataset? Does this suggest that in the presence of heavy degradation, the feature-level supervision prioritizes perceptual \"plausibility\" to such an extent that it sacrifices structural fidelity to the ground truth? How can a user be confident that the generated output is faithful to the scene's motion and not a well-textured hallucination?\"*\n\n- This is an excellent point highlighting the classical trade-off between fidelity and perceptual quality. Our goal is to generate results faithful to the underlying authentic scene, rather than the degraded ground truth.\n\n- The issue you raised is, in fact, a reflection of the success of our method rather than a flaw. The BS-ERGB dataset is well-known for its substantial noise and non-rigid deformations, and both its input frames and ground-truth references are inherently low in quality. In such conditions, a robust model should aim to produce results that are visually clean, perceptually aligned with human judgment, rather than replicating the noise and artifacts present in the original data. Traditional pixel-level metrics like PSNR tend to penalize such ‘reasonable deviations’ since they measure absolute pixel differences without considering perceptual quality.\n\n- Our method achieves significant improvements on perceptual metrics such as LPIPS and DISTS, and the qualitative results (Fig.5, Fig.6 and Fig.7) in the paper clearly show that our interpolated frames exhibit much better visual realism than those generated by prior methods. This strongly supports the claim that our model better captures ‘perceptual authenticity’ and is more suited for real-world deployment.\n\n- Moreover, our approach does not suffer from a noticeable disadvantage on traditional metrics like PSNR and SSIM, and in fact achieves competitive results. This further demonstrates the comprehensive effectiveness of our framework.\n\n\n\n> ***Q4:**\"The paper claims to move away from \"direct image-level supervision\" , yet the final training stage employs the L_cg loss, which includes LPIPS and Laplacian loss on the output image. Could you clarify why this is not considered direct image-level supervision?\"*\n\nThank you for the comment. \n\n- The phrasing could indeed lead to some ambiguity, and we have corrected it. By 'direct image-level supervision,' we refer to pixel-level error calculations, such as MSE loss and L1 loss, which can introduce degradation patterns into the model when the ground truth is degraded. Previous works typically combine pixel-level loss (e.g., L1 loss, MSE loss) with perceptual loss (e.g., LPIPS, Laplacian loss), which reduces robustness in the face of various degradation scenarios. Our training approach avoids this issue.\n\n- Specifically, our EPA framework employs a decoupled, dual-supervision mechanism. The primary driver of the interpolation process is the feature-level alignment loss L_bf , through which the model learns to perform interpolation within a degradation-robust semantic space. This process is safeguarded from the influence of flawed ground truth pixels, thereby shifting the core learning task away from the fragile image domain, which validates our claim.\n\n\n> ***Q5:**\"Beyond the L2 distance comparison in Figure 8, have you performed any other analysis to more rigorously quantify the \"degradation-insensitivity\" of DINO features? For example, how does the signal-to-noise ratio of the features compare to that of the input images under varying levels of degradation?\"*\n\nThank you for the suggestion. \n\n- To provide a more rigorous analysis beyond the L2 distance comparison in Figure 8, we have calculated the signal-to-noise ratio (SNR) for both the image level and feature levels across various degradation types. As the table below clearly demonstrates, the SNR is significantly more stable in the DINO feature space, especially in deeper layers, quantitatively validating their superior robustness to degradation.\n\n|Type          | Gaussian blur | Noise | Jpeg artifact | Low resolution | Motion blur |\n| ------------ | :------------ | :---- | :------------ | :------------- | :---------- |\n| Image level    | *58.07*       | 32.61 | 85.30         | 31.07          | 29.29       |\n| Feature level 0 | 9.20          | 1.22  | 14.75         | 4.67           | 4.44        |\n| Feature level 1 | 3.33          | 1.91  | 2.20          | 2.12           | 2.13        |\n| Feature level 2 | 2.41          | 1.22  | 1.26          | 1.53           | 1.85        |"}, {"review_id": "ZrZgkbxmzN", "reviewer": "Reviewer_pM6N", "summary": "This paper proposes a novel event-based video frame interpolation framework, designed to address image distortions in complex scenarios, where existing methods often fail. By introducing multi-level, degradation-insensitive, semantic-aware supervision signals, the proposed method enhances the perceptual realism and cross-scene generalization of the interpolated frames. Experimental results demonstrate that the proposed method excels in both perceptual quality and generalization capability.", "questions": "See Weakness for details.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n(1) The paper proposes an effective feature-level supervision strategy that addresses the limitations of existing methods when dealing with degraded images.\n\n(2) The experimental results not only show excellent performance on traditional metrics (e.g., PSNR, SSIM) but also significantly outperform prior works on perceptual quality metrics (e.g., LPIPS, DISTS).\n\nWeaknesses:\n\n(1) The proposed method employs a generator-based architecture to produce the final image, a technique inherently known to boost the perceptual quality of generated outputs. Consequently, it is unclear whether the final improvement in perceptual quality stems more from the generator architecture itself or from the proposed feature-level supervision. The authors should provide more extensive ablation studies or analysis to disentangle these two effects.\n\n(2) The paper initially discusses \"motion blur and image degradation\" in broad terms, but the subsequent experiments primarily demonstrate effectiveness on blur and camera noise. Image degradation encompasses a much wider range of phenomena, such as lens distortion, rolling shutter artifacts, compression artifacts, low resolution, rain, and fog. It is questionable whether the proposed method maintains its robustness against these other types of degradation.\n\n(3) The proposed method requires a two-stage training process, but its training efficiency is not discussed. Furthermore, the computational efficiency of the method during inference remains unclear, and it is not specified whether it can meet real-time requirements.\n\n(4) The robustness of DINO features is not guaranteed in specialized domains, such as medical microscopy videos or materials analysis. Since the proposed method relies heavily on DINO features for semantic alignment, its broad applicability and performance in such out-of-distribution scenarios are uncertain.", "was_revised": true, "final_justification": "Thank you for your feedback, which has resolved many of my concerns. The computational efficiency and broad applicability of the proposed method need improvement in future work. My final recommendation score is Borderline accept.", "rebuttal": "We thank Reviewer pM6N for devoting time to this review and providing valuable comments.\n\n\n> ***Q1:**\"The proposed method employs a generator-based architecture to produce the final image, a technique inherently known to boost the perceptual quality of generated outputs. Consequently, it is unclear whether the final improvement in perceptual quality stems more from the generator architecture itself or from the proposed feature-level supervision. The authors should provide more extensive ablation studies or analysis to disentangle these two effects.\"*\n\nThank you for your comment.\n\n- All components of the training framework we propose are interchangeable. This component primarily serves to synthesize interpolated frames, which is not our main focus. During experiments, we also evaluated U-Net-based [1] and Transformer-based [2] decoders. The U-Net structure increased the number of parameters, while the Transformer-based decoder introduced longer inference latency. Both alternatives yielded similar interpolation quality ($\\pm$3%), with larger models offering marginal gains at the cost of efficiency. To strike a balance between performance and runtime, we ultimately adopted a simple yet effective generator.\n\n\n[1] Chan K C K, Zhou S, Xu X, et al. Basicvsr++: Improving video super-resolution with enhanced propagation and alignment[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 5972-5981.\n\n[2] Kim T, Chae Y, Jang H K, et al. Event-based video frame interpolation with cross-modal asymmetric bidirectional motion fields[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 18032-18042.\n\n\n\n> ***Q2:**\"The paper initially discusses \"motion blur and image degradation\" in broad terms, but the subsequent experiments primarily demonstrate effectiveness on blur and camera noise. Image degradation encompasses a much wider range of phenomena, such as lens distortion, rolling shutter artifacts, compression artifacts, low resolution, rain, and fog. It is questionable whether the proposed method maintains its robustness against these other types of degradation.\"*\n\nThank you for the comment. \n\n- We have added comparative results for different methods under JPEG compression artifacts (Table 1) and low-resolution degradation (Table 2) in the BS-ERGB pen_03 scene. As shown, our method ensures the best perceptual quality and highly competitive traditional metrics. Although CBMNet is slightly superior in traditional metrics, it suffers a collapse in perceptual quality, with significant color errors in moving objects. TLXNet, being purely based on optical flow, preserves the degradation from key frames to the fullest, resulting in large discrepancies with the non-degraded ground truth and severely affecting its performance. This further supports our viewpoint and validates the effectiveness of our method. Due to conference requirements, the detailed comparison images will be included in the supplementary material.\n\n\n\n[Table 1]  JPEG compression artifacts:\n\n| Method   | PSNR$\\uparrow$ | LPIPS$\\downarrow$ | FloLPIPS$\\downarrow$ | DISTS$\\downarrow$ |\n| -------- | -------------- | ----------------- | -------------------- | ----------------- |\n| Timelens | 21.373         | 0.216             | 0.313                | 0.235             |\n| CBMNet   | **22.082**     | 0.575             | 0.818                | 0.277             |\n| TLXNet   | 20.126         | 0.371             | 0.599                | 0.268             |\n| Ours     | 21.423         | **0.141**         | **0.246**            | **0.177**         |\n\n\n\n[Table 2] Low resolution:\n\n| Method   | PSNR$\\uparrow$ | LPIPS$\\downarrow$ | FloLPIPS$\\downarrow$ | DISTS$\\downarrow$ |\n| -------- | -------------- | ----------------- | -------------------- | ----------------- |\n| Timelens | 21.285         | 0.191             | 0.253                | 0.247             |\n| CBMNet   | **22.022**     | 0.551             | 0.743                | 0.274             |\n| TLXNet   | 20.175         | 0.226             | 0.382                | 0.247             |\n| Ours     | 21.584         | **0.135**         | **0.212**            | **0.180**         |\n\n\n\n> ***Q3:**\"The proposed method requires a two-stage training process, but its training efficiency is not discussed. Furthermore, the computational efficiency of the method during inference remains unclear, and it is not specified whether it can meet real-time requirements.\"*\n\nThank you for the question. This part will be included in the supplementary material.\n\n- Training stage: The training process is composed of two distinct stages, conducted on a single NVIDIA RTX 3090: \n\n  1. Stage 1: The model was trained for 100 epochs using a single RGB image with a batch size of 28 and image size of 256x256, requiring approximately 24 hours. \n\n  2. Stage 2: The weights of the other parts were frozen, and only the BEGA module was trained for 40 epochs using two RGB images and the corresponding event streams, with a batch size of 32. This stage took about 48 hours to complete.\n\n- Inference Cost and Comparison: The table below details the resource consumption compared to other methods. Our approach achieves a highly competitive runtime and model size, demonstrating an excellent balance between performance and efficiency.\n\n\n| Method         | Parameters$\\dagger$ | Flops  | Runtime | PSNR$\\uparrow$ | LPIPS$\\downarrow$ |\n| -------------- | ------------------ | ------ | ------- | -------------- | ----------------- |\n| Timelens       | 79.2M              | 18.7B  | 0.033s  | 31.871         | 0.053             |\n| CBMNet         | 22.2M              | 46.7B  | 0.231s  | 31.876         | 0.101             |\n| TLXNet         | 7.87M              | 16.5B | 0.027s  | 31.578         | 0.046             |\n| **EPA (ours)** | 13.9M              | 20.2B  | 0.086s  | 33.402         | 0.015             |\n\n$\\dagger$ represents trainable parameters. PSNR and LPIPS scores come from the real dataset HS-ERGB.\n\n> ***Q4:**\"The robustness of DINO features is not guaranteed in specialized domains, such as medical microscopy videos or materials analysis. Since the proposed method relies heavily on DINO features for semantic alignment, its broad applicability and performance in such out-of-distribution scenarios are uncertain.\"*\n\nThank you for the comment.\n\n- Our work, as you mentioned, is a framework-based approach. The areas you highlighted are indeed critical application scenarios. We believe that by replacing DINO with domain-specific vision models, a competitive application performance can be achieved. However, due to limitations in available hardware and the scarcity of event-based datasets, we regret that we are currently unable to validate our method in specific domains, such as medical microscopy videos or material analysis. This is, however, a direction for future work."}, {"review_id": "jWhMDCvTxU", "reviewer": "Reviewer_GPzr", "summary": "This paper proposes an event-based method for arbitrary-time video frame interpolation between two degraded images. Compared with existing flow-based or multi-task (restoration + interpolation) methods, the proposed framework claims to avoid such trade-offs without introducing additional complexity.", "questions": "How is the off-the-shelf reconstruction generator [41] aligned with features extracted by M_f (DINO), especially to support the claim of compatible to multi-level feature input for restoring the image?\n\nInconsistently with HS-ERGB, the proposed method achieves better perceptual metrics (e.g., LPIPS) but worse traditional ones (e.g., PSNR) on BS-ERGB and EventAid datasets. An in-depth analysis for this phenomenon is needed, including more significant visual comparison analyses.\n\nThe paper should clarify differences between training and inference stages and report the inference cost.\n\nDoes the claim of L82 apply to all existing methods? For example, does EVDI [46] have this problem?\n\nminor: \n\n111 losse\n\nsupp 46 Limition", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n\nThe paper presents a new pipeline to learn robust features from vision foundation models and guide reconstruction under image degradation.\n\nThe proposed method is extensively evaluated on both synthetic and real datasets, demonstrating superior performance over existing approaches.\n\nWeaknesses\n\nThe proposed framework lacks a natural connection between motion-induced image degradation (e.g., blur) and event generation, and the two core modules are relatively loosely coupled.\n\nThe DINO-based feature extraction module (Sec 3.1) is primarily used to learn degradation-robust features from images and is unrelated to events. Similar strategies have been adopted in prior work. The authors should clarify the unique contribution of this component in their pipeline.\n\nUnlike prior event-based optical flow methods where events contribute to both motion estimation and image synthesis, the proposed approach uses events only for alignment, without directly enhancing semantic features. The rationale for this design should be discussed.\n\nThe ablation study is limited to toggling modules on or off. A deeper analysis is needed, such as replacing DINO with other backbone models or combining other backbones with the proposed supervision to verify the generality of the approach.", "was_revised": true, "final_justification": "I find most of my concerns addressed, except that I still believe the uniqueness of events in the framework design is unconvincing. Iwill raise my rating to borderline accept.", "rebuttal": "We thank Reviewer GPzr for devoting time to this review and providing valuable comments.\n\n> ***Q1:**\"The proposed framework lacks a natural connection between motion-induced image degradation (e.g., blur) and event generation, and the two core modules are relatively loosely coupled.\"*\n\nThank you for this question. \n\n- We believe the two core modules are, in fact, tightly and logically coupled to solve the problem. Our approach decomposes the complex task of \"interpolating between degraded frames\" into two distinct, manageable sub-problems: (1) Extracting robust semantic information from the (potentially degraded) keyframes.  (2) Accurately aligning this semantic information through reliable spatial-temporal information to generate the intermediate frame.\n\n- The key point is that only with events assistance, the model can obtain the precise inter-frame motion information required to perform the alignment in the second sub-problem. As we argue in the paper, event data's sensitivity to moving objects aligns perfectly with the changing-sensitive nature of semantic features from models like DINO. This enables a robust, temporally coherent alignment in the feature space, which is far more reliable than aligning degraded pixels. Therefore, the DINO features and the event-guided alignment are inseparable components for generating the final, high-quality interpolated frame.\n\n\n\n> ***Q2:**\"The DINO-based feature extraction module (Sec 3.1) is primarily used to learn degradation-robust features from images and is unrelated to events. Similar strategies have been adopted in prior work. The authors should clarify the unique contribution of this component in their pipeline.\"*\n\n- The unique contribution is not VFM (e.g., DINO) itself, but how we leverage it within a new E-VFI paradigm. Previous works optimize their model predominantly rely on pixel-level supervision from the ground truth at the output image-level stage. When keyframes and the GT are degraded, this approach forces the model to learn to reproduce these degradations, harming its generalization.\n\n- Our paradigm addresses this by using VFM to lift the problem into a more robust feature space. The role of VFM (such as DINO) is to act as a stable, degradation-insensitive \"perceptual anchor\". The main contribution of our work is to design a novel framework that operates effectively for dealing with such inspiration, especially with event data assistance. We intentionally keep the VFM with minimal modifications, ensuring that our framework can be easily extended to other domains or tasks by simply swapping in a different, more suitable feature extractor.\n\n\n\n> ***Q3:**\"Unlike prior event-based optical flow methods where events contribute to both motion estimation and image synthesis, the proposed approach uses events only for alignment, without directly enhancing semantic features. The rationale for this design should be discussed.\"*\n\n- This is a deliberate design choice. When keyframe degradation is the primary problem, traditional optical flow methods tend to propagate the degradation from the keyframe to the warped, interpolated frame. This results in an output that, while perhaps spatially aligned, is still perceptually poor in semantic expression. By moving to a feature-level alignment operation, we bypass this issue. We use events for what they excel at—providing precise motion cues for alignment— for facilitating the final synthesis, resulting in high-quality, degradation-insensitive outputs.\n\n\n\n> ***Q4:**\"The ablation study is limited to toggling modules on or off. A deeper analysis is needed, such as replacing DINO with other backbone models or combining other backbones with the proposed supervision to verify the generality of the approach.\"*\n\n- To address this and further verify the generality of our approach, we have conducted a new experiment where we replace the DINO backbone with MoCo v3, another popular self-supervised vision model. After training, the model with MoCo v3 achieved a PSNR of 35.11 and an LPIPS of 0.0079 on the Vimeo90k dataset. While slightly lower than our DINO-based model, these strong results demonstrate that our proposed framework is not tied to a specific backbone and can successfully operate with different feature extractors, confirming the generality of our approach.\n\n\n\n> ***Q5:**\"How is the off-the-shelf reconstruction generator [41] aligned with features extracted by M_f (DINO), especially to support the claim of compatible to multi-level feature input for restoring the image?\"*\n\n- This compatibility is achieved through our two-stage training process and the data-driven nature of the network. In Stage 1, we specifically train the reconstruction generator (G_r) and the Style Adapter (A_s) to do one thing: reconstruct a high-quality image from the multi-level features provided by DINO. An MLP is used to unify the feature dimensions. This pre-training ensures that the generator learns the exact mapping from the DINO feature distribution to the image distribution. It becomes an \"expert\" at translating DINO's language into a visual output, ensuring the two are perfectly aligned for the main task in Stage 2.\n\n\n\n> ***Q6:**\"Inconsistently with HS-ERGB, the proposed method achieves better perceptual metrics (e.g., LPIPS) but worse traditional ones (e.g., PSNR) on BS-ERGB and EventAid datasets. An in-depth analysis for this phenomenon is needed, including more significant visual comparison analyses.\"*\n\nThank you for the comment.\n\n- Our goal is to generate results faithful to the underlying authentic scene, rather than the degraded ground truth.\n\n- The issue you raised is, in fact, a reflection of the success of our method rather than a flaw. The BS-ERGB dataset is well-known for its substantial noise and non-rigid deformations, and both its input frames and ground-truth references are inherently low in quality. The EventAid dataset also contains a significant amount of blur. In such conditions, a robust model should aim to produce results that are visually clean, perceptually aligned with human judgment, rather than replicating the noise and artifacts present in the original data. Traditional pixel-level metrics like PSNR tend to penalize such ‘reasonable deviations’ since they measure absolute pixel differences without considering perceptual quality.\n\n- Our method achieves significant improvements on perceptual metrics such as LPIPS and DISTS, and the qualitative results (Fig.5, Fig.6 and Fig.7) in the paper clearly show that our interpolated frames exhibit much better visual realism than those generated by prior methods. This strongly supports the claim that our model better captures ‘perceptual authenticity’ and is more suited for real-world deployment.\n\n- Moreover, our approach does not suffer from a noticeable disadvantage on traditional metrics like PSNR and SSIM, and in fact achieves competitive results. This further demonstrates the comprehensive effectiveness of our framework. Due to conference requirements, more significant visual comparison analyses will be included in the supplementary material.\n\n> ***Q7:**\"The paper should clarify differences between training and inference stages and report the inference cost.\"*\n\nThank you for the comment. This part will be included in the supplementary material.\n\n- Training stage: The training process is composed of two distinct stages, conducted on a single NVIDIA RTX 3090: \n\n  1. Stage 1: The model was trained for 100 epochs using a single RGB image with a batch size of 28 and image size of 256x256, requiring approximately 24 hours. \n\n  2. Stage 2: The weights of the other parts were frozen, and only the BEGA module was trained for 40 epochs using two RGB images and the corresponding event streams, with a batch size of 32. This stage took about 48 hours to complete.\n\n- Inference Cost and Comparison: In the inference stage, the entire network is used to generate the final frame. The table below details the resource consumption compared to other methods. Our approach achieves a highly competitive runtime and model size, demonstrating an excellent balance between performance and efficiency.\n\n| Method         | Parameters$\\dagger$ | Flops  | Runtime | PSNR$\\uparrow$ | LPIPS$\\downarrow$ |\n| -------------- | ------------------ | ------ | ------- | -------------- | ----------------- |\n| Timelens       | 79.2M              | 18.7B  | 0.033s  | 31.871         | 0.053             |\n| CBMNet         | 22.2M              | 46.7B  | 0.231s  | 31.876         | 0.101             |\n| TLXNet         | 7.87M              | 16.5B | 0.027s  | 31.578         | 0.046             |\n| **EPA (ours)** | 13.9M              | 20.2B  | 0.086s  | 33.402         | 0.015             |\n\n$\\dagger$ represents trainable parameters. PSNR and LPIPS scores come from the real dataset HS-ERGB.\n\n> ***Q8:**\"Does the claim of L82 apply to all existing methods? For example, does EVDI [46] have this problem?\"*\n\nThank you for the comment. \n\n- The denoising and deblurring methods mentioned in [35][46], which are currently leading in the field, do exhibit similar issues. They share a similar objective with ours, aiming to alleviate degradation in key frames for E-VFI. However, they take a different approach, opting for a 'denoise-then-interpolate' strategy. While this approach can yield high-quality interpolated frames and clear key frames, the multi-stage process may introduce error accumulation and additional computational complexity, which contributes to their relatively lower performance in E-VFI tasks. \n- In contrast, our EPA framework is a single, end-to-end solution for the interpolation task. By operating entirely in a degradation-robust feature space, we circumvent the need for an explicit, separate denoising step. This avoids the potential pitfalls of error accumulation inherent in multi-stage pipelines, offering a more direct solution to the problem."}, {"review_id": "mAXSmKEHvZ", "reviewer": "Reviewer_QbtM", "summary": "This paper addresses video frame interpolation (VFI) using event signals, highlighting that existing event-based methods often overlook blur and distortion in keyframes and ground-truth data, leading to suboptimal performance. To mitigate this, the authors propose EPA, a novel framework that leverages multilevel, degradation-insensitive semantic-perceptual supervision using a DINO-pretrained vision backbone. The approach aims to enhance perceptual realism and experiments show that EPA achieves state-of-the-art results in perceptual metrics such as LPIPS.", "questions": "Why is there no computational cost analysis?\n\nHow does the model perform in terms of video-level consistency?\n\nHow does it compare with recent large-motion-capable VFI methods [1,2]?\n\n(Refer to the “Weaknesses” section)", "limitations": "A clear analysis of computational costs and more comprehensive video-based comparisons would strengthen the paper’s claims.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- The paper is well-organized and clearly written.\n\n- The model design is technically sound.\n\n- Using pretrained vision features for perceptual-level supervision is a reasonable.\n\nWeaknesses:\n\n- No analysis of computational cost is provided (e.g., model size, speed, training overhead).\n\n- No video results are shown, which limits the evaluation of temporal consistency—a key factor in VFI tasks.\n\n- Recent methods capable of handling large motion and ambiguous supervision, such as classic models with novel indexing [1] and diffusion-based approaches [2], are not compared. The examples shown seem to involve relatively moderate motion; a qualitative comparison on more challenging sequences would help justify the use of event signals.\n\n- The related work section lacks coverage of prior efforts on unsupervised event-based VFI [3] for degraded inputs, and those further exploiting the hidden temporal information in degraded inputs [4–6].\n\nMinor Issues:\n\nThere are typos such as line 111: “losse” → “losses”\n\nReferences:\n\n[1] Clearer frames, anytime: Resolving velocity ambiguity in video frame interpolation, ECCV 2024\n\n[2] Framer: Interactive frame interpolation, ICLR 2025\n\n[3] Unsupervised Event-Based Video Reconstruction, WACV 2024\n\n[4] Event-Based Frame Interpolation with Ad-hoc Deblurring, CVPR 2023\n\n[5] Unifying Motion Deblurring and Frame Interpolation with Events, CVPR 2022\n\n[6] Event-Guided Frame Interpolation and Dynamic Range Expansion of Single Rolling Shutter Image, ACM-MM 2023", "was_revised": true, "final_justification": "Given the promise of discussion and comparisons in the main paper, as well as the computational costs and currently unavailable qualitative comparisons, this reviewer will withhold the rating.", "rebuttal": "We thank Reviewer QbtM for devoting time to this review and providing valuable comments.\n\n> ***Q1:**\"Why is there no computational cost analysis?\"*\n\nThank you for the reminder. This part will be included in the supplementary material.\n\n- Training stage: The training process is composed of two distinct stages, conducted on a single NVIDIA RTX 3090: \n\n  1. Stage 1: The model was trained for 100 epochs using a single RGB image with a batch size of 28 and image size of 256x256, requiring approximately 24 hours. \n\n  2. Stage 2: The weights of the other parts were frozen, and only the BEGA module was trained for 40 epochs using two RGB images and the corresponding event streams, with a batch size of 32. This stage took about 48 hours to complete.\n\n- Inference Cost and Comparison: The table below details the resource consumption compared to other methods. Our approach achieves a highly competitive runtime and model size, demonstrating an excellent balance between performance and efficiency. \n\n\n| Method         | Parameters$\\dagger$ | Flops  | Runtime | PSNR$\\uparrow$ | LPIPS$\\downarrow$ |\n| -------------- | ------------------ | ------ | ------- | -------------- | ----------------- |\n| Timelens       | 79.2M              | 18.7B  | 0.033s  | 31.871         | 0.053             |\n| CBMNet         | 22.2M              | 46.7B  | 0.231s  | 31.876         | 0.101             |\n| TLXNet         | 7.87M              | 16.5B | 0.027s  | 31.578         | 0.046             |\n| **EPA (ours)** | 13.9M              | 20.2B  | 0.086s  | 33.402         | 0.015             |\n\n$\\dagger$ represents trainable parameters. PSNR and LPIPS scores come from the real dataset HS-ERGB.\n\n> ***Q2:**\"How does the model perform in terms of video-level consistency?\"*\n\nThank you for the question. \n\n- Due to conference policy, we cannot share external links at this stage, but they will be included in the supplementary material. For a quantitative measure, we rely on the FloLPIPS metric, which is specifically designed to measure perceptual coherence across frames. As shown in Tab 1 & 2 of our paper, our method achieves state-of-the-art FloLPIPS scores on all datasets, quantitatively confirming its excellent temporal consistency.\n\n\n\n> ***Q3:**\"How does it compare with recent large-motion-capable VFI methods [1,2]?\"*\n\nThank you for bringing these highly relevant, recent works to our attention. We have conducted experiments on the BS-ERGB dataset, with results shown in the table below. \n\n| Method | PSNR$\\uparrow$ | LPIPS$\\downarrow$ | FloLPIPS$\\downarrow$ | DISTS$\\downarrow$ |\n| ------ | -------------- | ----------------- | -------------------- | ----------------- |\n| [1]    | 23.240         | 0.102             | 0.312                | 0.066             |\n| [2]    | 19.262         | 0.331             | 0.457                | 0.182             |\n| Ours   | **27.221**     | **0.028**         | **0.082**            | **0.057**         |\n\n\n\nThe results demonstrate our method's superior performance on this challenging real-world dataset with degradation. It is noteworthy that [1] also achieves competitive LPIPS and DISTS scores, reinforcing the importance of evaluating perceptual quality—a direction we also champion in our work. We believe this new comparison provides valuable context, and we will incorporate these important results and this analysis into the main paper.\n\n\n\n> ***Q4:**\"The related work section lacks coverage of prior efforts on unsupervised event-based VFI [3] for degraded inputs, and those further exploiting the hidden temporal information in degraded inputs [4–6].\"*\n\nWe appreciate the reminder and agree that these citations are important for a complete literature review. We will revise the related work section in the final manuscript to include and properly discuss these papers. Thank you for helping us strengthen our paper."}]

Author rebuttal:

Meta-review: # Summary
 
This paper approaches the problem of event-based video frame interpolation in challenging scenarios where input keyframes may suffer from degradation like motion blur and noise. The main idea is to employ feature-level supervision strategy to addresses the limitations of current methods when dealing with degraded, blurred images.

# Strength
* The experimental results are strong on both traditional metrics (e.g., PSNR, SSIM) and perceptual quality metrics (e.g., LPIPS, DISTS).

# Weakness
* The novelty is limited: using feature-level supervision and addressing event-based VFI.
* The proposed framework lacks a natural connection between motion-induced image degradation (e.g., blur) and event generation (the connection to event data is not particularly strong in terms of novelty and less convincing).

# Discussion
During discussion, most of concerns from reviewers are addressed, except for the following two: (i) limited novelty and (ii) the connection between motion-induced image degradation and event generation. AC reads all review and discussion and agrees that the strong performance can outweigh the concerns. AC recommends to accept the paper. AC encourages the author(s) to update / incorporate discussion / additional results during rebuttal into their final version of the paper if it is accepted.
Decision: Accept (poster)