Event-Guided Consistent Video Enhancement with Modality-Adaptive Diffusion Pipeline
Authors: Kanghao Chen
Zixin Zhang
Guoqiang Liang
Lutao Jiang
Zeyu Wang
Ying-Cong Chen
Venue: NeurIPS 2025 poster ()
Keywords: Event Camera
Video Enhancement
Diffusion model
Primary area: applications

Abstract: Recent advancements in low-light video enhancement (LLVE) have increasingly leveraged both RGB and event cameras to improve video quality under challenging conditions.  However, existing approaches share two key drawbacks. First, they are tuned for steady low-light scenes, so their performance drops when illumination varies. Second, they assume every sensing modality is always available, while real systems may lose or corrupt one of them. These limitations make the methods brittle in dynamic, real-world settings. In this paper, we propose EVDiffuser, a novel framework for consistent LLVE that integrates RGB and event data through a modality-adaptive diffusion pipeline. By harnessing the powerful priors of video diffusion models, EVDiffuser enables consistent video enhancement and generalization to diverse scenarios under varying illumination, where RGB or events may even be absent.  Specifically, we first design a modality-agnostic conditioning mechanism based on a diffusion pipeline by treating the two modalities as optional conditions, which is fine-tuned using augmented and integrated datasets. Furthermore, we introduce a modality-adaptive guidance rescaling that dynamically adjusts the contribution of each modality according to sensor-specific characteristics. Additionally, we establish a benchmark that accounts for varying illumination and diverse real-world scenarios, facilitating future research on consistent event-guided LLVE. Our experiments demonstrate state-of-the-art performance across challenging scenarios (i.e., varying illumination) and sensor-based settings (e.g., event-only, RGB-only), highlighting the generalization of our framework.

Event-Guided Consistent Video Enhancement with Modality-Adaptive Diffusion Pipeline
Kanghao Chen'
Zixin Zhang AI Thrust, HKUST(GZ))
AI Thrust, HKUST(GZ))
kcheu879@connect. hwwp://cconnnn.....t..oo..e.ee wwust-gz...c... edu. en
zzhang300@connect. hkust-ga -gztt--z... edu. en
Guoqiang Liang Nanyang Technological University
Lutao Jiang
Zeyu Wang CMA Thrust, HKUST(GZZ) CSE, HKUST
AI Thrust, HKUST(GZZ)
Ying-Cong Chen
AI Thrust, HKUST(GZ)) EMIA, HKUST
yingeongchendust.tn
Abstract
Recent advancements in low-light video enhancement (LLVE) have increasingly leveraged both RGB and event cameras to improve video quality under challenging conditions. However, existing approaches share key drawbacks. First, they are tuned for steady low-light scenes, so their performance drops when illumination varies. Second, they assume every sensing modality is always available, while real systems may lose or соrrupt one of them. These limitations make the methods brittle in dynamic, real-world settings. In this paper. we propose EVDiffuser, a novel framework for consistent LLVE that integrates RGB and event data through -ddativv diffusion pipeline. By harnessing the powerful priors of video diffusion models, EVDiffuser enables consistent video enhancement and gener- alization diverse scenarios under varying illumination, where RGB or events may even be absent. Specifically, we first design modality-agnosti conditioning mechanism based on a diffusion pipeline by treating the two modalities as optional conditions, which is fine-tuned using augmented and integrated datasets. Fur- thermore, we introduce a poddality-ddaptiin guidance rescaling that dynamically adjusts the contribution of each modality according sensor-specific characteris- tics. Additionally, we establish a benchmark that accounts for varying illumination and diverse real-world scenarios, facilitating future research consistent event- guided LLVE. Our experiments demonstrate state-of-the. of-the-art performance across challenging scenarios (i.e., varying illumination) and sensor- based settings (e.g., event-only, RGB-only). highlighting the generalization of our framework.
1 Introduction
Video enhancement is pivotal area of research in computer vision, encompassing tasks such as denoising 32 deblurring [18), and low-light enhancement (2). While many methods exhibit impres- sive performance, the inherent limitations of the sensor still constrain the video quality and realism,
"Equal Contribution Corresponning Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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input consitin
Input ewents
(b) Inquet Video Elvents
[ej RCilt-Eieer Video Reeioration
(e) nGB--wood Video Teelondao
20 Modallty Agnastic Consistont Recondtiction (Ours)
modslly apnostie consitioning
ourput video with normel conotant ight
(d) Exeri-based Video Flacrattrction
Ipl
Figure 1: (a) EVDiffuser is a diffusion framework that enables both RGB and event data for consistent LLVE. demonstrates robustness real-world scenarios with varying illumination and generalizes well across multiple settings (e.g.. RGB-only and event-only). Under (b) challenging conditions characterized by extreme illumination variation, our (1) EVDiffuser effectively reconstructs consistent, high-quality video. In contrast, existing methods either (c) produce over-enhanced results or (d,e) fail to preserve structural details.
conditioning on
particularly under extreme conditions such as low light rapid camera motion, necessitating the integration of additional sensors. Recent advancements in event cameras, which provide exceptional dynamic range and microsecond-leve temporal resolution, have introduced new possibilities for video reconstruction (37 60] and restoration [24] 23 (2). These capabilities enable robust performance in extreme conditions. including high-speed motion, low-light environments, and overexposed scenes, thereby addressing many limitations of RGB-based approaches. Among these applications, low-light video enhancement (LLVE) has shown particular promise when leveraging event cameras.
Existing event-guided LLVE methods often assume stable input from both RGB and event modalities under steadily low illumination in well-controlled environment. For example, recent methods [24] 23 rely on both lightight, noisy RGB frames and sufficient event data, typically constrained to a narrow illumination range (e-g., 5-25 lux). However, in real-world applications, illumination can vary significantly, and different sensors are affected to varying degrees. RGB images may be of high quality most of the time, only deteriorating under specific low-light conditions. The normal quality images will still be augmented, often leading to overexposure or inconsistency in existing methods (see Fig. [c.e). In other cases, the RGB sensor may fail completely, capturing information. The RGB+event fusion approaches may also fail to work. One stable method that can operate steadily is purely event-based event-to-video reconstruction (37 41 N 9 but these methods cannot take advantage of high-quality RGB inputs when available, resulting in poor overall quality in varying illumination(sc) see Fig. dd). In summary, while current techniques may perform adequately in specific settings with stable parameters (i.e., illumination and modalities input), they struggle with the variability inherent real-world conditions, where both modality availability and environmental factors frequently change. To the best of our knowledge, no existing method can effectively handle all these scenarios within a unified framework. Given these challenges, it is crucial develop a unified, comprehensive framework that can robustly handle diverse scenarios, accurately model scene dynamics, and maintain consistent video quality despite fluctuating input conditions.
usable
In this paper, we introduce EVDiffuser, a novel framework for consistent LLVE, which integrates both RGB and event data through a modallty-adaativv diffusion pipeline. As shown in Fig. [ the key insight of our framework is to condition the diffusion process on both modalities, producing consistent videos by leveraging the powerful priors from pre-trained video diffusion model [5T]. Specifically, we propose modality- agnostic training pipeline based on a diffusion model, conditioning it on both modalities and fine-tuning it using augmented and integrated datasets. This facilitates the diffusion model to adapt even in the absence of either RGB or event inputs. Additionally, we design novel modality-adaptic guidance rescaling mechanism that facilitates the integration of information from both modalities and adaptively adjusts the guidance to accommodate varying degradation conditions. This ensures the model effectively exploits the complementary characteristics of RGB and event cameras. Finally, existing method addresses consistent LLVE under varying illumination,

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establish a benchmark that accounts for real- illumination variability, laying the foundation for future research across diverse applications. our experiments, the proposed approach demonstrates strong generalization across wide range of scenarios, maintaining consistent LIVE performance under varying illumination and showing robustness in different configurations (i.e. event-only and RGB-only), even when one of the sensors is unavailable.
2 Related Work
2.1 Low-Light Video Enhancement (LLVE)
RGB-based LLVE. Normal-light video reconstruction with the input of low-light RGB frames
10 show impressive performance, their research focuses on LLVE in stable low-light enviromments, assuming the illumination constantly low. Some methods try to reduce temporal jitter through temporal consistency losses [4 53 [6] and multi-frame alignment [31 45 10), but these not enough for the real-world setting, where there are often unstable changes in lighting. The model design and dataset construction of current works are designed for constant low light, making these models unable to generalize to real-world dynamic illumination, leading to temporal discontinuities and severe distortion in the restored video.
has been a long-standing research topic. Although many methods (53 4 36
Multi-modal LLVE. Recent research has explored and shown promise when combining additional sensor (e.g.event camera) with RGB camera to do the LLVE task. For example, Liu et al. [29] synthesize pseudo-events from adjacent low-light frames, enabling artifact-free fusion while pre- serving temporal consistency. EvLowLight [24] enhances coherence by jointly aligning motion and spatial features across events and frames. EvLight++ [7] further refines temporal smoothness through recurrent modules and dedicated loss functions, achieving robust noise suppression. Despite these advancements, these methods still assume a stable low-light environment and the constant avail- ability of both modalities. They fail to consider that different sensors have varying strengths under changing lighting conditions, and they also overlook the possibility that some modalities may become unavailable in real-world scenarios, leading to unsatisfactory performance in real-world settings where illumination may fluctuate. To address this, our work aims to propose modality-adapto and modality-agnostie pipeline that can handle both varying lighting conditions and uncertain modality availability, respectively.
2.2 Video Generation Models
Foundation Models. The field of video generation has evolved rapidly by integrating advances in image synthesis with the challenge of ensuring temporal consistency across frames. This progress has been particularly accelerated by text-to-video models, driven by innovations in the Transformer architecture [43] and diffusion models [2]. Early works explored diverse generative frameworks:
GAN-based methods 22
34 39 pioneered real-time video synthesis but struggled with temporal artifacts, while Transformer- VQVAE hybrids [II [4] 50 improved long-range coherence through autoregressive token prediction, albeit increased computational costs. Recent efforts have shifted toward diffusion-basee models [5] 28 21], which achieve state-of-the-ard quality by leveraging scaled text-video datasets and architectural advancements like Diffusion Transformers (DiT) (35)
Generative models in low-level vision. Generative models possess strong generalization capabilities and encode rich prior information, making them widely applicable in various downstream lowelevel vision tasks. In the domain of multi-modes low-level vision. methods such as Event- Drrent-Diffusion [26]
and Temporal [58) employ diffusion models as the backbone for event-guided image reconstruction
and restoration. Approaches like Repurposing [5] and EGVD (36) leverage video diffusion models combined with event cameras for frame interpolation. E2VIDiff (25 and LaSe-E2V (6) utilize prior knowledge from pretrained image diffusion models to perform event-to-video translation. In contrast to these methods, our method built upon pre-trained video diffusion and introduces a modality-agnostic training pipeline by treating the two input modalities as optional conditions. This design enables flexible handling of different input configurations while leveraging the priors of video diffusion models to ensure consistency under dynamic lighting conditions.
3

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Cullection
Wooere tid Darrse Derrint
DNT Block
Frazen
Conoata
Ty Evant Sequirnc
Trannbbic Dit Copy
doe Oegrasioo Lovigit vagel
First Shome
LoRA
Liniot
Zero
Blook
DIT
T
Dive Dat
1d) Degraded/Low light images
(6) Mosiaiiy Agnostic DIT
Recoestrutedd images
We construct our training dataset including (I) event-RGB paired data, (II) event-to-video data, and (III) RGB-only data. To augment the dataset, random degradation is applied to the ground truth images. The (c) degraded/low-ligtt images and (b) event sequences are processed using a (d) modality-agnostic DiT, with a trainable copy and LoRA.
Figure 2: Architecture of EVDiffuser:
Method
EVDiffuser is a diffusion framework that processes input data through a modality-adaptiv pipeline, enabling seamless integration of heterogeneous modalities (i.e., RGB and event data) for consistent LLVE, as illustrated in Fig. 2 Notably, our model performs robustly in dynamic scenarios with varying illumination and can operate effectively even with a single sensor when the other is unavailable. We begin by formalizing the problem statement for this novel video reconstruction task (see Sec. (.1). followed by a review of the foundational I2V model (see Sec. 3.2). Next, we introduce our framework for injecting modality-agnostic control based on the augmented and integrated datasets (see Sec. (33) Finally, we present the modality-adaptive guidance rescaling technique that adaptively leverages the unique characteristic of each modality under varying conditions (see Sec. 3.4
3.1 Problem Statement
We begin by defining the input data, which consists of both RGB images I E R and the corresponding event data & = {x',, y², p')) N- where x' and y² represent the pixel coordinates of each event, and L and p' denote the timestamp and polarity, respectively. In real-world applications, the environments vary over time, such as when transitioning between indoor and outdoor scenes or between day and night. These changes can result in significant variations in illumination, thereby causing fluctuation and noise in both RGB and event data. In the worst scenario in single-modality systems, one of the modalities may become unavailable, necessitating the modality-agnostic capability of a unified model. Given the input from both RGB and event data under varying illumination and sensor settings, the framework aims to adaptively reconstruct a consistent, high-quality video (i.e., under normal-ligtt light conditions).
3.2 Video Diffusion Foundation Model
LLVE can be viewed as high-quality video generation conditioned on low-quality inputs. This conditional generation nature inherently aligns with video diffusion models' capability generate high-quality video by sampling from high-dimensional video manifolds, and therefore constitutes an optimal foundation for our framework. Our EVDiffuser is fine-tuned based on the Cog VideoX- 12V [51] model, a transforree-bbased video diffusion model (35] that operates in a latent space. Specifically, we adopt the pre-trained model as the base architecture, which takes an image e g kW x3 as input and generates a video V E T xw x3 To prepare the input, the image is first padded with zeros, resulting in conditioned input video of size T' H W x 3, matching the target video's is dimensions. A variational autoencoder (VAE) encoder is then applied to the input frames, producing a latent vector of size T s x & 16, which subsequently concatenated with the noise of the same size. A DiT 351 model eg is iteratively employed to denoise the noisy latent representation over a predefined number of steps. The resulting denoised latent vector is processed by a VAE decoder to generate the video V. In the following sections, we will describe how the RGB images and event data are incorporated as additional conditioning inputs to the base model, yielding moddllit-agnnssie model.

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3.3 aggnotty- DiT for Consistent LLVE
By leveraging the strengths of the diffusion pipeline, we treat the two modalities as optional conditions and fine-tune the DiT in modality-agnostee manner to achieve consistent LLVE. In this approach, when one modality is absent or significantly degraded, the other can serve as an optional conditioning input. Conversely, when both modalities are available, their combined information leads to substantial improvement in video reconstruction quality. Notably, our method maintains temporal consistency despite variations in the input scenarios. The architecture overview is shown in Fig. dd) Below, we detail the process of injecting both modalities as conditions and fine-tuning DiT in a modality-agnostie manner.
Injecting RGB Condition. To incorporate the RGB sequence as a condition in the I2V diffusion model, we first apply the pre- pre-trained VAE to obtain the corresponding latent code, which is then concatenated with the first frame latent along the frame channel. The first frame latent is introduced for auto-regressive long video reconstruction, which will be described in the following. To preserve the original DiT's powerful capabilities while adapting it to our task, we apply LoRA [15] exclusively to the image branch for parameter-efficies tuning, leaving the pre-trained DîT denoiser fully frozen.
Injecting Event Condition. We adopt a design similar to that of ControlNet (55) in EVDiffuser to incorporate event data as an additional conditioning input. To extract the latent features of the event sequence, we first encode the event data into voxel grid with three channels and then apply the image VAE to encode the event data, thereby fully utilizing the pre-trained VAE. Next, we create a trainable copy of the pre-trained denoising DiT to process the latent feature of the event data. We use the first 18 blocks from the original denoising DiT to form the condition DiT for the event branch. In the event branch, we extract the output feature from each DiT block, process it with a rero-initialized linear layer, and add the resulting feature to the corresponding feature of the denoising DìT.
Fine-tuning aggnsicy- DiT. To train the DiT' in its adaptability to varying degradation conditions and settings, we construct a comprehensive training dataset that includes videos paired with event data across multiple scenarios. The training dataset consists of three components, as shown in Fig. 2 a): 1) First, we incorporate the RGB-event paired dataset from SDE 23 to facilitate event-guided low-light enhancement. this setup. both low-light RGB images and event data are provided as conditioning inputs, with the model denoising them to predict the corresponding normal-light RGB sequence. To further enhance the model handle varying illumination conditions, we introduce random degradations to normal-light RGB images, simulating dynamic lighting scenarios. These degraded images are then used as RGB inputs to reconstruct the original normal-light sequence. II) To enable the model to handle more extreme scenarios where the RGB input may fail entirely, we incorporate the E2V dataset from V2E2V [30], where the RGB input is replaced with zeros. III) Additionally, to stabilize the training of the image branch, we include a separate RGB-only low-light enhancement dataset for tinettning the model, where the event input is replaced with zeros accordingly. In this work, we select the SDSD dataset 455 as our RGB-un ooly dataset. Based on these augmented and integrated datasets, the DiT is endowed with mnodality-agnostie capabilities and is robust to varying environments, enabling consistent LLVE. Notably, we do not use any dataset with varying illumination to train our model; nevertheless, can seamlessly adapt challenging, variable scenarios (see Fig. 4 demonstrating the zero-shot generalization of our framework.
manner and ensure
Furthermore, to facilitate the reconstruction of long videos, we introduce random noise into the first frame condition and randomly replace the condition with zeros. During inference, the initial batch of the video is predicted using the zeros-initialized first frame, and the subsequent batch of video frames is predicted an auto-regressive manner, with the final predicted frame from the previous batch serving as the first frame for the next batch.
3.4 Modality-adaptive Guidance Rescaling
Since our framework is based on a diffusion pipeline with consider the unique characteristics of each modality, is feasible to incorporate modality-adaptive guidance rescaling for each condition. To this end, Classifier- Freee Guidance [3] is a promising strategy adapt the sampling pipeline.
conditions. To further
Classifier-Free Guidance (CFG). The model is trained in modality-conditione framework, with both modalities randomly selected from the three datasets previously mentioned. Additionally, during
5

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training, each modality is dropped with a predefined probability (t.e., 0.2), facilitating the co- co-training of both unconditioned and conditioned models for each modality. The unconditioned model tends to be more conservative, striving to generalize across all randomly selected conditions. In contrast, the modality-conditionne conditioned model specializes in specific environment. When applying CFG to our modality- agnosstic denoising framework, we compute the discrepancy between the prediction made by the modality model and the conservative model without conditioning.
Modality- ModdalityAdaptiive Guidance Rescaling, Consider denoising model (i.e., modality-agnostic DiT), denoted cø Xx Cf, Ce. t), that performs pondality-adaptiin generation. The CFG is implemented by adjusting the denoising process at each timestep as follows:
éo(x; (x, Cy, Cas () =x Cr, (1,11
+ wa(eee(x cfs Ces 1) co c,, wy(co((x cy; Ce, cO Ø, c., t)),
(I)
where Cy and C. represent the modality conditions for RGB images and events, respectively, and w. denotes the corresponding guidance scale. For modality-adaptive Guidance Rescaling. w, is adapted to represent the degradation degree (i.e., illumination) in order to exploit valuable information from each modality accommodate the varying environment. Specifically, the illumination prior map is firstly extracted as Lu meancx H (I) E RT, where meancx # represents the operation that computes the mean values across the channel (C) and spatial dimensions (H x W/) for each frame. Then, W, is computed based on the illumination map. with w alar and we = a - La), where a is pre-define coefficient balance the guidance. Intuitively, this formulation ensures that when illumination is low, larger value of we directs the model towards emphasizing event-to-video reconstruction, while higher illumination levels favor the frame condition.
4 Benchmark
We propose a benchmark tailored for practical real-world scenarios, specifically focusing video enhancement performance under varying illumination conditions, which is largely underexplored in previous research.
4.1 Dataset Preparation
To construct the evaluation dataset, we synthesize videos with varying illumi- nation based on the SDE dataset [23 and SDSD dataset [45, referred to as V- SDE and V-SDSD, respectively. Specif- ically, we generate frames with varying lighting conditions V, from normal-light frames I, using linear scaling: (p(p)
Oi x T (p), where aN a scale factor that continuously changes over time, which set as sine function time. In this benchmark, we typically apply two sine wave periods in single sequence. More- over, simulate noise in low-light condi- tions, we introduce random noise to each generated image, which is related to the its lighting level: (p(p) (V),),,, = Events then obtained from the v2e model [16]. The synthesized videos and events are used as input, with normal-light videos serving as the ground truth for comparisons. An example of our test set is shown in Fig.
Input and events varying light
(b) Ground truth frames under constant normal light
Figure Examples our benchmark dataset.
4.2 Evaluation Metric
We evaluate the performance of different methods by comparing the output videos with the ground truth videos using commonly used PSNR, SSIM, and LIPIS metrics. We observe that existing

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Table I: Comparisons on V-SDE benchmark with varying illumination. 1(4) means higher (lower) better. The highest result is highlighted in bold while the second highest result underline.
V-SDE-in
V-SDE-out
Input
Method
PSNK"
PSNR*T
SSIM
LPIPS)
PSNRT
PSNK*T
SSIMI
LPIPS
Event Only
ETNet 47
14.52
16.13
0.5147
0.6035
14.21
16.25
0-4844
0.6217
HyperE2VID
13.77
16.13
0.4914
0.6398
13.80
15.94
0.4566
0.6283
SNR-N
19
10.18
19.81
0.4092
0.4267
8.46
19.82
0.3830
0.5097
Uformer
11.04
19.77
0.5106
0.3672
8.19
18.88
0.4218
0,4460
Image Only
LLFFow-L-SKH
18
11.34
20.04
0.4700
0.4338
10.50
20.16
0.4403
0.4331
Retinexformer
11.10
19.57
0.4330
0.4133
8.36
0.3585
0.5126
ELIE [19]
10.28
19.30
0.3833
0.4036
9.29
19.44
0,3908
0.4661
Image+Event
eSL-Net
20.30
5.32
19.26
0.5203
Liu ct al. 29
9.76
18.25
0.3519
0.4012
7.31
19.01
0.3437
0.4604
EvLight
23
10:46
20.17
9.06
19.80
0.3914
Evl.owlight ght
24]
11.29
20.51
0.4726
0.3698
9.37
20.33
0.4311
0.4527
Vidco+Event
EvLighte+
7
10.57
20.24
0.4578
0.3532
8.91
19.76
0,3995
0.4040
EVDiffuser (Ours)
21.55
27.14
0.8338
0.2249
19.71
26.53
0.8044
0.1999
methods often overfit to low-light inputs, producing overexposed frames with normal-light inputs. To enable comprehensive comparison beyond brightness adaptation, we adopt the PSNR* metric from 23 which computes PSNR after aligning output and ground truth brightness.
Experiment
5.1 Experiment Setting
Implementation Details. Our model is developed by fine-tuning pre-trained 12V diffusion models (i.e., Cog VideoX (5I). which generate RGB videos at resolution of 480x720 with 49 frames at FPS, using 50 sampling steps. The LoRA rank is set to 128 for the image branch. For training, we use learning rate of 1 10-4 and the AdamW optimizer. The model is trained for 30 epochs with gradient accumulation, resulting in an effective batch size of 64. The training process takes 2 days on H100 GPUs. During the inference stage, we employ the DDIM [40 sampler with 50 steps.
Comparison Methods. We compare our method with recent methods in four different settings: (I) the experiment with events input, including ETNNet (47) and HyperF2VII 9. (II) the experiment with RGB image as input, including SNR-Net [49), Uformer [46). L.FFlo--L-SKF [48), and Retinexformer (2) (III) the experiment with RGB image and paired events inputs, including ELIE 19). eSL-Net [44), Liu et al. [29] and EvLight (23). (IV) the experiment with video and events as inputs, including EvLowLight (24) and EvLight++ [7]. Since some of the comparison methods do not release their training code, we have reproduced the training pipeline following the hyperparameters as illustrated in the reports.
5.2 Evaluation Results
To evaluate performance methods (excluding event-only methods) on the SDE training dataset. The quantitative results in Tab. demonstrate the superior performance of method the benchmark, significantly outperforming the comparison methods across all metrics. For the original PSNR, existing methods show a sharp decline due to their tendency to overfit the illumination conditions in the training set, resulting in poor generalization to diverse real-world lighting. To assess image restoration effectiveness excluding the effect of illumination fitting, we compare PSNR*, where our method again significantly outperforms SOTA techniques, achieving improvements of 6.63 dB for V-SDE-in and 6.20 dB for V-SDE-out. For SSIM and LPIPS, our method also achieves superior performance, providing strong evidence for its effectiveness in low-light image enhancement. Notably, the event-only method shows relatively higher PSNR among the baselines, as it relies solely on event data to generate videos. making it more robust to illumination variation. Additionally, we provide an evaluation based on metrics from VBench 17 in the Appendix (see Tab. 7. to further demonstrate the effectiveness of our method on temporal consistency. There is also notable observation that many methods using both event and image modalities perform worse than single-modality approaches on PSNR. For event-only methods, since these methods input only event data, they are not sensitive image
Comparison V-SDE:
V-SDE benchmark, retrain the baseline
7

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Indoor
Outdoor
Timestamp
Tmestamp
GOFFEE
FFFFEE
OFF
FFEE
CFFFEE
FFEE
SOFF
ENDRTasers Evight++
COFFEE
COFT
COFFEE
COFT
FEE
COFFEE
GT
FEE
CO
Figure 4: Qualitative results the V-SDE benchmark under varying illumination.
Shae
Figure 5: Qualitative comparison with state-of-the- art methods on event-to- vi-eeo dataset, including of-the-art methods on RGB-only dataset (i.e., ECD (33) (Column 1&2&3), HQF [41] (Column SDSD (5)).
Figure 6: Qualitative comparison with state-
4) and MVSEC [57) (Column 4).
intensity variations across different scenarios. The intensity of reconstructed frames remains stable within certain range, resulting in relatively higher PSNR compared to image-involved methods, which are severely affected by varying frame intensitics. Additionally, we compare PSNR* R with intensity calibration to evaluate the structural fidelity of reconstructed vidcos, where image-involved methods achieve better performance than event-only methods. When comparing image-only and multi-modality methods, observe that EvLowlight and EvLight++ achieve better performance, while other methods show comparable performance. This is because these methods typically overfit to specific low-light conditions and mostly fail adapt varying illumination scenarios.
Qualitatively, as shown in Fig. for both indoor and outdoor scenes, our method consistently reconstructs sequences under varying illumination, preserving clear edges and producing high-quality results that closely resemble the ground truth. In contrast, competing methods either suffer from overexposure under normal lighting or fail capture details when RGB data is heavily degraded.

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Table 2: Comparisons on our V-SDSD dataset with RGB-only setting. The highest result is highlighted in bold while the second highest result highlighted in underline.
V-SDSD--i
V-SDSD-out
Method
PSNR*1
SSIMT
LPIPS
PSNR*1
SSIMI
LPIPS!
SNR-Net 449
18.96
0,6056
0.4155
19.52
0.4543
0.4656
Uformer 46
18.69
0.6894
0.2997
19.07
0.5153
0.3555
LLFIow-L-SKK [48
20,46
0.6485
0.5070
20,98
0.4780
0,6404
Retinexformer 2
19.83
0.6738
0.2525
19.68
0.4622
0.3509
EVDiffuser (Ours)
29.10
0.9163
0.1033
25.23
0.6365
0.1927
We also evaluate our method on the original SDE test set, shown in the Appendix (see Tab. to further demonstrate its generalization capability.
To assess the effectiveness of our work in real-world scenarios, we collect sequence with varying illumination across indoor and outdoor scenes. While it is impractical to capture high-quality RGB for these scenes, provide only qualitative results in the Appendix (see Fig. 8
Comparison on Event-only Setting: Since the dynamic range of RGB sensors is relatively lower than that of event sensors (e.g.. 60 dB vs. 120 dB), the RGB sensor may fail entirely under extremely low illumination. To demonstrate the generalization capability of our method in extreme cases and its boddality-gnosstee capability, conducted comparisons
event-to-vides reconstruction datasets, including ECD 33 MVSEC [57]. and HQF 41. We did not retrain the model on additional datasets, thus evaluating its zero-shot capability. In these scenarios, existing RGB+event methods fail without RGB input due to their reliance on both modalities. Therefore, we compare our method only with HyperE2VID [9, SoTA E2V method, as shown in Fig. 5 Although our method may introduce additional textures in under-constrained regions lacking sufficient event data, it is able to recover high-quality details and appearance, producing more realistic scene compared to HyperE2VID.
Comparison on RGB-only setting: is worth noting that our modality-agnossi framework also functions without event input, enhancing its practicality for deployment in systems without event sensors. To further evaluate the modality-agnosti capability of our framework, we assess its performance
RGB-only benchmark (i.e., V-SDSD), reflecting scenarios where event camera available on the system. To compare, we retrain the baseline models using the SDSD training dataset. As shown in Tab. and Fig. 6 our method outperforms the baselines in terms of PSNR", SSIM, and LPIPS, achieving gains of over 8.64 dB for V-SDSD-in and 4.25 dB for V-SDSD-out.
Table Impact of each training dataset.
Table 4: Impact of the LoRA adapter.
Even++RGB
RGB only
Event only
PSNRT
SSIM
LPIPS]
PEFT Strategies
PSNR1
PSNR*
SSIM
LPIPS
x
Frozen LoRA
21.17 21.55
25.86
0.7936
0.2533
18.51
0.7839
0.2555
21.55
0.8338
0.2349
27.14
0,8338
0,2249
5.3 Ablation Study
Impact of the Integrated Datasets. To assess the impact of the fusion in our training dataset, we conduct experiments with multiple dataset combinations. As shown in Tab. 3 training exclusively on the Event+RGB dataset yields PSNR of 18.31 dB, whereas incorporating the RGB-only dataset results in an improvement of 0.2 dB. Including all three types of datasets achieves the best results, demonstrating that the diffusion model benefits from diverse and comprehensive dataset to enhance performance for consistent LLVE.
Impact of the LoRA for Image branch. To verify this, we conduct an ablation study in Tab. 4 Compared to the baseline with a frozen image branch, incorporating LoRA leads a 0.38 increase in PSNR, demonstrating the importance of the adapter. This is because the original model is pre- trained on the image-to-video -ovidoo task, while our model focuses on video-to-video. We also experiment with fully fine-tuning all parameters; however, this approach encounters memory limitations.

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Impact of the Modality-aware Guidance Rescaling, In this section, we con- duct an ablation study assess the impact of each individual modality and eval- uate the effectiveness of our proposed Mwdality-aware Guidance Rescaling (MGR).
shown in Fig- 7 the event-only base- line is able to recover the scene's s edges but fails to capture the full visual ap- pearance, often leading to hallucinated details. Similarly, the image-only base- line produces moderate results in normal conditions, although may underper- form in extreme scenarios. Additionally, the model incorporating both modalities without MSR exhibits degraded visual quality, as it overly relies on the degraded images. In contrast, when MSR is ap- plied, the model shows significant im- provement. This comparison highlights the effectiveness of our MGR and empha- sizes the flexibility of the diffusion- based framework in managing event-based ap- plications.
Inpur FOOR lover
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Conclusion
WMMGR
We present novel framework for consistent LLVE that seamlessly inte- grates both RGB and event data through a modality-agnosse diffusion pipeline. Our approach guarantees high-quality construction with robust temporal consis- tency, even in dynamic and challenging environments. Experimental results demonstrate the superiority of our framework, offering solid foundation for future advancements in addressing real-world variability.
Figure 7: Impact of modality inputs and our Modality- adaptive Guidance Rescaling (MGR).
Due to the inherent limitations of the diffusion model, our inference process requires multiple sampling steps, which can be time-consuming. This affects the efficiency of our framework in applications with strict performance requirements. In future work, we aim to explore more efficient diffusion models for this framework, including one-step sampling and quantization techniques, to reduce computational costs. Additionally, while our framework specifically tailored for the LLVE task adapted for other tasks (e.g., interpolation, denoising) by leveraging the strengths of our approach.
Limitations and Future Work:
of the promising applications of event cameras it can potentially be
The proposed EVDiffuser framework has significant potential for real-wooorkd impact. By enabling consistent, high-quality video enhancement under challenging conditions-inclldee low light, dynamic illumination, and partial sensor failure-it it benefit applications in surveil- lance, autonomous driving, and wearable devices. Its modality-agnostic design enhances resilience and flexibility, promoting broader deployment even when only single sensor is available. This adaptability fosters accessibility in resource- ceoource-oonstraieed settings and paves the way for more reliable vision systems across diverse environments, ultimately contributing safety, security, and situational awareness both public and private sectors.
Broader Impact:
Acknovledgements
This work is supported by National Natural Science Foundation of China (No. 62206068) and Guang- dong Provincial Key Lab of Integrated Communication, Sensing and Computation for Ubiquitous Internet of Things NNo.2023B1 212010007) This work was partially supported by the Guangdong Provincial Key Lab of Integrated Communication, Sensing and Computation for Ubiquitous Inter- net of Things 22023B12220000 and the Guangzhou Industrial Information and Intelligent Key Laboratory #2024A03J0628.
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Appendix
A
User Study
We conducted user study with participants evaluating the performance of different methods. We employ preference ranking to assess user satisfaction across different methods. The evaluation focuses on four key aspects: lighting continuity, video quality, detail preservation and similarity to the ground truth (GT similarity). As shown in Tab. 5 across all subjects and in every aspect, our method stood out with a consistently superior preference score, highlighting that it is perceived by humans significantly superior to current baseline methods.
Table 5: User Study Preference Ranking
Method
Lighting Continuity 4
Video Quality 4
Detail Preservation +
GT Similarity +
ET-Net
[47
4.375
4.938
5,000
4.875
Retinexformer
3.813
3.563
3.063
3.688
Evlight++ 7
3.063
2.438
2,688
2.813
EyLowlight 24
2,688
3.063
3.125
2.563
EVDiffuser (Ours)
1.000
1.000
1.125
1.063
B Runtime Profile and Parameter Count
Tab. summarizes the efficiency profile of method. While our unified design entails higher pa- rameter count and computational cost compared to modality-speciffe models, it delivers significantly improved generalization across wide range of challenging scenarios. We prioritize generaliza- tion and performance over efficiency, and our method focuses on offline use as a post-processing technique, rather than for real-time applications.
Moreover, our proposed framework inherently extensible and compatible with emerging accel- eration techniques for diffusion-basee generative models. Below, we highlight several promising directions:
Recent advancements S4 in quantized attention mechanisms have demonstrated a 1.8x speedup for Cog VideoX without degradation in video quality. These techniques are fully compatible with our Cog VideoX-
Quantized Attention:
architecture.
The recent flow matching algorithm 20 in video generation reinterprets the denoising process as multi-stage pyramidal flow, achieving real-time video generation at 24 FPS for 768p resolution. This approach can be scamlessly adapted to our diffusion model with a modality-agnostte training pipeline.
Flow Matching:
Auto-regressive Modeling: Recent studies [3 (27 have explored auto-regressive strategies for video generation, formulating the task next-token prediction and achieving real-time sampling at 24 FPS. Extending these techniques to event camera presents promising direction for fully leveraging the high temporal resolution inherent to event data.
One-step Diffusion: Recent efforts 8,,5 38 have shown progress in reducing the num- ber of denoising steps for generation tasks (achieving 12 FPS), although they remain diteo-generation scenarios. In our preliminary exploration, implemented naive few-step diffusion pipeline within our framework, reducing the sampling process to 20 DDIM steps. However, this reduction introduces a trade-off between computational efficiency and video quality, which we recognize valuable direction for future work.
limited handling complex
In summary, while our unified framework incurs additional computational cost. it opens up meaning- ful research topic and provides a flexible and generalizable solution across modalities and illumination conditions. Furthermore, the aforementioned acceleration techniques are immediately applicable to our proposed EyDiffuser framework and constitute central focus of our future work. We hope our response helps to clarify the core contributions of our work and resolves the concern.
22

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Table 6: Efficiency profile and parameter count of different methods.
Method
Params (M)
Memory (GB)
Inference Time (s/frame)
ILFlow-L-SKT 48
409.50
39.91
0.30
Retinexformer 2
15.57
1.61
0.12
ELIE 19]
440.32
33.36
0.32
eSL-Net 44
560.94
0.56
0.18
EvLight++ 7
225.91
26.21
0.21
EVDiffuser (Ours)
7932.80
30.89
5.63
C
Additional Implementation Details
For the comparison method, we reproduced ELIE 19, Liu et al. [29 and Evlight++ 7 according to the implementation details in the original papers, while the others retrained with the released code. We replace the event synthesis module in (29 by inputting events captured with the event camera or generated from the event simulator [16].
D Qualitative Results on the Self-capture Sequence
To further demonstrate the effectiveness of our method in real-world scenarios, we record sequence under varying illumination conditions, covering both outdoor and indoor environments. As shown in Fig. 8, our method consistently preserves clear structure and appearance across frames, including outdoor scenes with normal lighting and indoor scenes where the RGB input nearly fails. In contrast, the comparison methods perform well only under specific illumination conditions and fail in most frames, underscoring the limitations of existing approaches.
Timestamp
Input
ExLight-
EVDiffuser (Ours)
Figure 8: Qualitative results on self-captured sequence under varying illumination, when moving from outdoor indoor scenes.
E Quantitative Comparison on Temporal Consistency
conduct comprehensive evaluation of video enhancement quality by assessing the temporal consistency metric introduced in VBench [17]. As shown in Tab, 7 our method achieves significant
23

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improvements in capturing temporal coherence, demonstrating the effectiveness of our modality- agnostic EVDiffuser for consistent LLVE.
Table 7: Comparisons on the temporal metric from VBench 17 A higher score indicates relatively better performance for particular dimension.
Subject Consistency
Background Consistency
Motion
Methods
Smoothness
ELIE [9
0.6597
0.8716
0.9841
eSL-Net
0.6382
0.8992
0.9842
Liu et al.
0.6468
0.8784
0,9815
EvLoolight [24
0.6183
0.9040
0.9824
EvLight++ 23
0.6610
0,8793
0,9881
EVDiffuser (Ours)
0.7007
0.8823
0.9942
F
Additional Qualitative Results V-SDE Dataset
Fig. 9 shows a comprehensive qualitative comparison between our method and baseline methods, featuring the proposed V-SDE Dataset. Our method significantly preserves both image consistency and quality, producing results that closely match the ground truth acquired under constant lighting conditions.
G Comparison on the Original SDE and SDSD Datasets
We also test our modality-agnostic model on the original SDE and SDSD datasets, where stable low light condition is maintained across the sequences. As shown in Tab, 8 although our model not specifically designed for stable low light condition, our method still achieves performance comparable to the SoTA under this setting, demonstrating the generality of our approach. Notably, the comparison methods are specifically designed for this stable low-light condition, limited to handling only the illumination conditions consistent with their training sets.
Table 8: Comparisons on the original SDE dataset 23 The highest result highlighted in bold
while the second highest result is highlighted in underline.
SDE-in
SDE-out
Method
PSNR1
PSNR #1
SSIM
PSNR
PSNR #1
SSIM
SNR-Net
49
20.05
21.89
0.6302
22.18
22.93
0.6611
Uformer 46
21.09
22.75
0.7524
22.32
23.57
0.7469
LLFlow-L-SKF [48
20.92
22.22
0.6610
21.68
23.41
0.6467
Retinexformer
21.30
23.78
0,6920
22.92
23.71
0.6834
ELIE 119
19.98
21.44
0.6168
20.69
23.12
0.6533
eSL-Net
21.25
23.19
0.7277
22.42
24.39
0.7187
Liu al. 29
21.79
23.88
0.7051
22.35
23.89
0.6895
EvLight [23
22.44
24.81
0.7697
23.21
25.60
0.7505
EvLowlight
20.57
22.14
0.6217
22.04
23.72
0.6485
EyLight++ 7
22.67
25.83
0.7791
23.34
26.01
0.7676
EVDiffuer (ours)
20.03
22.34
0.6537
22.76
24.32
0.7298
H Impact of Variation Frequencies
We also conduct an analysis study to analyze the impact varying illumination change frequencies in video sequences. Here, we define frequency as the number of sine wave periods occurring within single sequence. As shown in Tab. as the change frequency increases, the performance metrics PSNR and PSNR* decrease accordingly, while other metrics fluctuate within a narrow range. This demonstrates the robustness of our method to changes in illumination frequency, as our training process incorporates more extreme and randomized augmentations.

---

Inpal Fice
leput
ETMM
Eventenny
Imige magge-nnly libme
Imaege Event
Vidro Event
Diughe
Figure 9: Additional qualitative comparison V-SDE Dataset.
25

---

Table 9: Analysis study on the changing frequency of illumination. Frequency is defined as the number of sine wave periods occurring within single sequence.
V-SDE-in
Frequencies
PSNRT
PSNR**
SSIMT
LPIPS
22.89
27.47
0.8158
0.2148
21.55
27.14
0.8338
0.2249
20.76
26.89
0.8278
0.2279
I Impact of the Random degradation
We also perform an ablation study to assess the impact of random augmentation in illumination. As shown in Fig. [0 the absence random augmentation, the illumination varies in parallel with the input RGB, which highlights the effectiveness of the random degradation strategy.
Input ROB Event
wio R.A
WRA
Figure Impact of the random augmentation (RA) in illumination.
26


Reviews:
[{"review_id": "ztSNa4NaAe", "reviewer": "Reviewer_37iR", "summary": "This paper proposes EVDiffuser, a novel diffusion-based framework for consistent low-light video enhancement (LLVE) under varying illumination conditions. It addresses two key limitations of existing methods: (1) sensitivity to illumination fluctuations, and (2) reliance on simultaneous availability of both RGB and event modalities. The core innovations include: 1）A modality-agnostic diffusion pipeline that treats RGB and event data as optional conditions, enabling robust performance even when one modality is missing. 2）Modality-adaptive guidance rescaling to dynamically balance contributions from each sensor based on illumination levels. 3）A new benchmark (V-SDE/V-SDSD) simulating real-world illumination variations. 4）Experiments demonstrate SOTA results on diverse settings (RGB-only, event-only, multimodal).", "questions": "1. V-SDE/V-SDSD rely on synthetic illumination curves (sine waves) and simulated noise (Sec. 4). Real-world corner cases (e.g., sudden flashes) may not be fully covered. Please consider to add more experimental results on these real cases.\n2. Results on original SDE (see Tab. 6 in main paper) show lower PSNR compared with specialized methods (e.g., EvLight++), suggesting a trade-off between generalization and optimality in fixed conditions. Please explain more on this point.\n3. Qualitative results (Fig. 4,8) rely on metrics like LPIPS; human perceptual studies would strengthen claims of \"high-quality\" reconstruction. Please consider to add more human studies on this point.\n4. The auto-regressive inference (Sec. 3.3) stitches video batches using the last predicted frame, risking error propagation in long sequences. Please show more result and analysis on this.", "limitations": "1. Runtime profile and parameter count are not provided. These are necessary to justify how compact and efficient the proposed method is.\n2. Table 1 only includes image-only baselines up to 2023. Why are more recent methods not included? Moreover, comparisons with video-only methods are also suggested to better evaluate video modeling capacity.\n3. Table 2 only evaluates RGB-only baselines on V-SDSD, unlike Table 1 which includes various input modalities (event-only, RGB-only, RGB+event, video+event). This limits the assessment of modality-agnostic performance on diverse datasets.\n4. The ablation study is not sufficiently detailed. Have ablations been conducted on LoRA insertion positions, CFG/dropout ratios?\n5. Since event streams do not carry color information, generating RGB video from event-only input is inherently ambiguous. Please explain how color priors are learned without RGB supervision.\n6. Figure 5 shows that event-only outputs exhibit clear color distortions (e.g., green shifts). Please explain this phenomenon. Suggest evaluating color fidelity using perceptual metrics or conducting user studies.\n7. The proposed model is trained only on synthetic event data. Why not include results on real-world event datasets to demonstrate robustness and generalization?", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "1. The modality-agnostic diffusion framework is the first unified solution for LLVE under illumination variations and partial sensor failure. The design elegantly combines pre-trained video diffusion (CogVideoX) with ControlNet-inspired conditioning.\n2. The modality-adaptive guidance rescaling (Eq. 1) is a clever mechanism to exploit sensor-specific strengths (e.g., prioritizing events in low light). Ablation studies (Tab. 3, Fig. 7) validate key components.\n3. The V-SDE/V-SDSD datasets address a critical gap in evaluating illumination-varying scenarios, facilitating future research.\n4. Zero-shot tests on event-only datasets (Fig. 5) and RGB-only benchmarks (Tab. 2) prove robustness beyond training conditions.", "was_revised": true, "final_justification": "Thanks for the great effort during the rebuttal. The most important issue still not be resolved well that is the too heavy parameters of the proposed model and the too long inference time. So I decided to maintain my rating.", "rebuttal": "We sincerely thank the reviewer for the thorough evaluation and constructive feedback.\n\n**Q1: V-SDE/V-SDSD rely on synthetic illumination curves and may not cover real-world corner cases like sudden flashes.**\n\nWe have conducted a detailed analysis of **illumination frequency variations** in our V-SDE dataset (see **Table 7** in the supplementary material), which demonstrates the robustness of our method under extreme illumination changes. In addition to synthetic data, we included real-world, self-captured sequence with varying illumination for qualitative evaluation (**Fig. 8** in the supplementary material), showcasing our method’s ability to generalize to realistic scenarios.\n\nFor even more challenging cases - such as sudden flashes, where real-world data are difficult to obtain - we performed additional quantitative experiments on V-SDE-in by **randomly masking frame segments** to simulate abrupt illumination changes. The table below shows that our method maintains robustness under such conditions and significantly outperforms existing approaches.\n\n\n| Method     | PSNR $\\uparrow$ | PSNR* $\\uparrow$ | SSIM $\\uparrow$ | LPIPS $\\downarrow$ |\n|------------|------|------|------|-------|\n| EvLight++  | 8.92  | 17.92 | 0.3871 |  0.4102  |\n| EVDiffuser (Ours) | **15.20**  | **23.03** | **0.5492**  | **0.3698** |\n\n**Q2: Performance trade-off between generalization and optimality in fixed conditions.**\n\nWe appreciate the question.\nThis trade-off is well recognized in the deep learning field (e.g., general-purpose models like GPT versus specialized translation models), where generalization is often prioritized over task-specific optimization due to its practical benefits.\nOur method follows this principle by focusing on generalization across diverse lighting conditions rather than optimizing for fixed low-light scenarios. While specialized models such as EvLight++ may achieve slightly higher PSNR in specific cases, they tend to overfit and lack robustness in broader contexts.\n\n**Q3: Please consider to add more human studies to validate \"high-quality\" reconstruction claims.**\n\nThank you for this valuable suggestion. We conducted a user study with 16 participants evaluating the performance of different methods. \nWe employ preference ranking to assess user satisfaction across different methods. The evaluation focuses on four key aspects: lighting continuity, video quality, detail preservation, and similarity to the ground truth (GT similarity).\n\nAcross all subjects and in every aspect, our method stood out with a consistently superior preference score, highlighting that it is perceived by humans as significantly superior to current baseline methods. We will include these results in the revised manuscript.\n\n| Method | lighting continuity $\\downarrow$ | Video Quality $\\downarrow$ | Detail Preservation $\\downarrow$ | GT similarity $\\downarrow$ |\n| --- | --- | --- | --- | --- |\n| ET-Net | 4.375 | 4.9375 | 5 | 4.875 |\n| Retinexformer | 3.8125 | 3.5625 | 3.0625 | 3.6875 |\n| Evlight++ | 3.0625 | 2.4375 | 2.6875 | 2.8125 |\n| EvLowlight | 2.6875 | 3.0625 | 3.125 | 2.5625 |\n| EvLowlight (Ours) | **1** | **1** | **1.125** | **1.0625** |\n\n**Q4: Auto-regressive inference error propagation analysis for long sequences.**\n\nWe have demonstrated long-video results (~300 frames) in our supplementary video demo. Technically, unlike existing image-to-video frameworks that rely solely on the first frame and **lack intermediate control**, our approach is constrained by sequential frames and event data. To further prevent over-reliance on the initial frame, we **randomly add noise and mask to the frames** during training (Line 191), encouraging the model to leverage both event streams and RGB information more effectively. \nOverall, these design choices substantially reduce error propagation compared with conventional first-frame-based auto-regressive methods.\n\n**Q5: Runtime profile and parameter count not provided.**\n\nThank you for the suggestion. The table below summarizes the efficiency profile of our method. Compared to traditional frameworks, our diffusion-based approach requires more parameters but achieves significantly better generalization across diverse scenarios.\n\n| Method        | Params (M)  | Memory (GB) | Inference Time (s/frame) |\n|---------------|------------|-----------|-----------|\n| LLFlow-L-SKF  | 409.50     | 39.91     | 0.30\n| Retinexformer | 15.57      | 1.61      | 0.12\n| ELIE          | 440.32     | 33.36     | 0.32\n| eSL-Net       | 560.94     | 0.56      | 0.18\n| EvLight++     | 225.91     | 26.21     | 0.21\n| EVDiffuser (Ours)   | 7932.80    | 30.89     | 5.63\n\n**Q6: Limited baselines and missing recent methods in Table 1.**\n\nWe have expanded our comparison to include more recent methods: MambaLLIE (NeurIPS 2024 [1]) for image-based enhancement, BRVE (CVPR 2024 [2] )and Zero-TIG (arxiv 2025 [3]) for video-based enhancement. Since these methods being optimized for specific low-light scenarios, our method **maintains a significant performance advantage** across all comparisons, as shown below. This expansion will be included in the revision.\n\n| Method                   | PSNR $\\uparrow$ | PSNR* $\\uparrow$ | SSIM $\\uparrow$ | LPIPS $\\downarrow$  |\n|--------------------------|-------|-------|--------|--------|\n| MambaLLIE (NeurIPS 2024) | 13.12 | 19.38 | 0.7167 | 0.2973 |\n| BRVE (CVPR 2024)         | 13.96  | 20.40 | 0.6359 | 0.3910 |\n| Zero-TIG (arxiv 2025)    | 9.92  | 12.16 | 0.2218 | 0.7775 |\n| EVDiffuser               | **21.55** | **27.14** | **0.8338** | **0.2249** |\n\n[1] Weng J, Yan Z, et al. Mamballie: Implicit retinex-aware low light enhancement with global-then-local state space[J]. NeurIPS, 2024, 37: 27440-27462.\n\n[2] Zhang G, Zhang Y, et al. Binarized low-light raw video enhancement[C]. CVPR. 2024: 25753-25762.\n\n[3] Li Y, Anantrasirichai N. Zero-TIG: Temporal Consistency-Aware Zero-Shot Illumination-Guided Low-light Video Enhancement[J]. arXiv preprint, 2025.\n\n**Q7: Inconsistent evaluation across modalities in Table 2.**\n\nThere may be a misunderstanding regarding Table 2's setting. This table specifically evaluates the **RGB-only** performance on the V-SDSD dataset under varying illumination conditions, demonstrating our framework's generalization capability in single-modality settings. This design choice allows us to isolate and validate the robustness of our modality-agnostic approache.\n\n**Q8: Insufficient ablation study details.**\n\nSorry for the confusion.\nThe ablation study of LoRA on the image branch is presented in Table 4 and Lines 314–318. Regarding the LoRA insertion positions, since we incorporate LoRA only in the image branch, we ablate it by comparing the performance with and without LoRA.\nThe CFG ablation results are shown in Figure 7 and Lines 319–342. For the dropout ratio, we followed the commonly used setting in CogVideoX and set it to 0.2. Due to time constraints, we will include more detailed sensitive analysis on the dropout ratios in the revised version.\n\n**Q9: Color prior learning from event-only input without RGB supervision.**\n\nWe appreciate this question. \nFirst, our model is built on a pre-trained video diffusion model (CogVideoX) trained on large-scale RGB datasets, which provides **rich color priors**. \nSecondly, during fine-tuning, we introduce a modality-agnostic training pipeline by applying random RGB masking, forcing the model to **learn semantic content from event data** and learn to associate it with the appropriate color information. This combination of diffusion priors and the novel training strategy enables the model to generate plausible colors even from event-only inputs.\n\n**Q10: Color distortions in event-only outputs (Figure 5).**\n\nWe respectfully disagree with the assessment regarding color distortions. Our method is able to recover plausible colors despite their absence in the event data, demonstrating the model’s ability to capture semantic information. In contrast, previous methods always lose color information entirely.\nFor quantitative evaluation, event-to-video reconstruction is typically assessed in grayscale, **focusing on structural fidelity rather than color accuracy**. The table below shows quantitative results on the ECD dataset. Our method delivers performance comparable to specialized event-to-video approaches while providing superior generalization across varying illumination conditions and modality availability.\n\n| Method      | MSE $\\downarrow$   | SSIM $\\uparrow$  | LPIPS $\\downarrow$ |\n|-------------|-------|-------|-------|\n| E2VID+      | 0.070 | 0.503 | 0.236 |\n| HyperE2VID  | 0.033 | 0.576 | 0.212 |\n| EVDiffuser (Ours)  | 0.051 | 0.529 | 0.220 |\n\n**Q11: Training only on synthetic event data without real-world datasets.**\n\nThere may be a misunderstanding regarding our training datasets. Our model is trained on a combination of synthetic event data and real-world RGB datasets (Line 175-185). Specifically:\n\n* **Event-RGB paired:** The SDE dataset contains **real-world** RGB-event paired data captured using DVIS346C cameras.\n* **RGB-only:** The SDSD dataset is a **real-world** RGB dataset.\n* **Event-only:** The V2E2V dataset is **Synthetic** event data. Employing synthetic event data for event-to-video reconstruction is standard practice, as event cameras capture high-speed motion and extreme lighting conditions that are difficult for RGB cameras to record simultaneously, making paired real-world data collection extremely challenging.\n\nBased on these training datasets, we conduct extensive **quantitative evaluations** on our synthetic datasets, as collecting ground-truth data in real-world illumination-varying scenarios is highly challenging. In addition, we include real-world self-captured sequence for **qualitative evaluation** in the supplementary material (Fig. 8), which demonstrates our method’s ability to generalize to realistic scenarios."}, {"review_id": "3IIEuuOSwm", "reviewer": "Reviewer_18Kq", "summary": "This paper introduces EVDiffuser for consistent low-light video enhancement (LLVE) that integrates RGB and event camera data via a modality-adaptive diffusion pipeline. It features a modality-agnostic conditioning mechanism and a modality-adaptive guidance rescaling strategy, enabling high-quality, temporally consistent video reconstruction even when RGB or event data are missing or corrupted. Extensive experiments on challenging benchmarks show that EVDiffuser significantly outperforms state-of-the-art methods, demonstrating superior generalization and resilience in real-world dynamic environments.", "questions": "1. Could the authors clarify the computational requirements and complexity of the proposed method, such as inference time and GPU memory usage, both during training and inference?\n\n2. The current evaluation is conducted on synthetic dynamic illumination conditions. Are there any results available on real-captured datasets with varying lighting? I understand that conducting such experiments may be difficult within the rebuttal period, so it will not affect my decision if these results cannot be provided. However, it is important to note that the noise patterns of event cameras can differ substantially under real-world lighting variations, which might not be fully captured by v2e simulation. Since the paper primarily claims advantages in real-world scenarios, providing results on real-captured sequences with dynamic lighting would be crucial and could significantly strengthen the paper by demonstrating its generalization ability.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. Addressing the realistic challenge of varying illumination and potential modality corruption is highly valuable and practical.\n\n2. The experimental results are encouraging and demonstrate strong potential.\n\n3. The implementation of the proposed idea is clear, well-structured, and reasonable.\n\nWeaknesses:\n\n1. The experimental data is purely synthetic, which may limit the persuasiveness of the results.\n\n2. Employing two DiT models could make the overall framework computationally expensive during both training and inference.", "was_revised": true, "final_justification": "I have read the rebuttal and the discussions with other reviewers. It appears there is a shared understanding that, despite the significant computational overhead, this work introduces a meaningful new research perspective. As a result, I maintain my original positive evaluation.", "rebuttal": "We sincerely thank the reviewer for the thorough evaluation and constructive feedback. Below, we provide detailed responses to each point.\n\n**Q1: The experimental data are purely synthetic, which may limit the persuasiveness of the results.**\n\nSorry for the confusion. We have included real-world self-captured sequence for qualitative evaluation in the supplementary material (Fig. 8).\nWhen transitioning from outdoor to dark indoor environments, our method successfully reconstructs videos with consistent lighting, even under extremely low-light conditions, thanks to the modality-agnostic framework.\nIn contrast, existing methods either overexpose outdoor scenes or fail to reconstruct extremely low-light regions, performing well only within a narrow illumination range. This further demonstrates our method’s strong generalization capability in realistic scenarios.\nFollowing your suggestion, we will collect additional real-world data in the future and include it in the revised version.\n\n\n\n\n**Q2: Employing two DiT models could make the overall framework computationally expensive during both training and inference.**\n\nWe acknowledge this concern and would like to clarify our architectural design. Our framework does not utilize two entire DiT models. Instead, building on the pre-trained CogVideoX DiT backbone (42 blocks), we employ only the first 18 blocks as the conditioning branch, as described in Line 169. This approach significantly reduces computational overhead while preserving effectiveness.\n\n\nThe computational requirements are detailed below:\n\n***Training Requirements:***\n- GPU Memory: 76GB for batch size 4\n- Training Time: ~2 days on 8×H100 GPUs\n\n***Inference Requirements:***\n- GPU Memory: 31GB\n- Inference Time: 5.63 seconds per frame on a single H100 GPU\n\nAdditionally, the table below summarizes the efficiency profile of our method. Compared to traditional frameworks, our diffusion-based approach requires more parameters but achieves significantly better generalization across diverse scenarios.\n\n| Method        | Params (M)  | Memory (GB) | Inference Time (s/frame) |\n|---------------|------------|-----------|-----------|\n| LLFlow-L-SKF  | 409.50     | 39.91     | 0.30\n| Retinexformer | 15.57      | 1.61      | 0.12\n| ELIE          | 440.32     | 33.36     | 0.32\n| eSL-Net       | 560.94     | 0.56      | 0.18\n| EvLight++     | 225.91     | 26.21     | 0.21\n| EVDiffuser    | 7932.80    | 30.89     | 5.63"}, {"review_id": "Dqyj3nAqNl", "reviewer": "Reviewer_LDfn", "summary": "This paper introduces EVDiffuser, a novel framework for consistent low-light video enhancement (LLVE) that adaptively integrates RGB and event camera data through a modality-adaptive diffusion pipeline. EVDiffuser can handle varying illumination and operate effectively even when one modality is unavailable. Comprehensive experiments demonstrate that EVDiffuser significantly outperforms state-of-the-art methods across various challenging scenarios, including varying illumination and sensor-based settings.", "questions": "- Why did you choose to encode events into a three-channel voxel grid and use a pre-trained image VAE without fine-tuning? Given the significant modality gap between event data and RGB images, wouldn't fine-tuning the VAE for event data be more appropriate?\n- What motivated the decision to use different approaches for injecting RGB (LoRA) and event data (ControlNet-style)? Did you perform ablation studies comparing alternatives, such as using ControlNet for both modalities or LoRA for both?\n- What is the maximum sequence length your model can handle in the auto-regressive long video reconstruction mode? Do you observe error accumulation over time, and if so, how do you address this issue?\n- Could you clarify what you mean by \"modality-agnostic\"? Have you tested or do you have theoretical reasons to believe your framework would work with other modalities beyond RGB and event data, such as infrared, depth, or point cloud data?\n- In Table 1, many methods using both event and image modalities perform worse than single-modality approaches. What explains this counterintuitive result?\n- Given the high computational requirements of diffusion models, how does your approach compare to baselines in terms of FLOPS, runtime, parameters, and throughput? Are there any strategies you've explored to make the inference more efficient?\n- Since your experiments use synthetically generated low-light data, how confident are you about the model's generalization to real-world low-light scenarios with complex noise patterns and degradations?\n- How does EVDiffuser compare to powerful contemporary diffusion models like Stable Diffusion or Wan (https://huggingface.co/Wan-AI/Wan2.1-T2V-14B) in terms of video quality and generation capabilities?", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The paper addresses a significant gap in LLVE research by introducing a unified framework that handles varying illumination and adapts to different input modality configurations.\n- The evaluation covers a wide range of scenarios (varying illumination, event-only, RGB-only) and compares against numerous state-of-the-art methods.\n\nWeaknesses:\n- In line 166, the authors mention converting event data into a three-channel format, but this approach ignores the significant modality gap between event data and RGB images. Using the pre-trained image VAE directly on this converted event data without fine-tuning is problematic and may not effectively capture the unique characteristics of event data.\n- The paper uses a ControlNet-style approach for event data injection but LoRA for RGB injection without providing adequate justification or ablation experiments for this design choice. This lack of explanation makes the architectural decisions seem arbitrary rather than principled.\n- While the authors mention auto-regressive long video reconstruction, they don't specify the maximum sequence length the model can handle, whether error accumulation occurs over time, or how such issues are addressed.\n- The term \"modality-agnostic\" is somewhat misleading. It's unclear whether it means different modalities aren't important or that the model can accept various modalities as input. The paper only validates RGB and event modalities, making claims about broader applicability to infrared images, depth maps, or point clouds seem overreaching.\n- The paper primarily integrates existing techniques (diffusion models, ControlNet, LoRA) without substantial innovation in the diffusion framework itself. The inclusion of Classifier-Free Guidance (CFG) as a methodological contribution is questionable since it's a well-established diffusion training technique that belongs in the experimental details rather than the method section.\n- In Table 1, many methods using both event and image modalities perform worse than single-modality approaches, which seems counterintuitive and requires explanation.\n- As a diffusion-based approach, the method requires multiple sampling steps during inference, making it computationally expensive. The paper lacks comprehensive comparisons of computational metrics (FLOPS, runtime, parameters, throughput) with baselines. The mention of training on 8 H100 GPUs (line 257) suggests a prohibitively high computational cost compared to baselines.\n- The low-light data used in experiments is synthetically generated, raising questions about generalization to real-world low-light scenarios.\n- The paper lacks comparisons with powerful contemporary diffusion models like Stable Diffusion or Wan (https://huggingface.co/Wan-AI/Wan2.1-T2V-14B), which would provide better context for the method's performance.\n- I am also curious about the results in the case where both event and RGB are missing. The model is still able to generate promissing results?", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the thorough evaluation and constructive feedback. Below, we provide detailed responses to each point.\n\n**Q1: Event data encoding and VAE usage without fine-tuning**\n\nWe appreciate this concern regarding our event data processing. First, the three-channel conversion technique is widely adopted in existing methods. Regarding the potential domain gap and the use of a pre-trained VAE for encoding event frames, we evaluated the reconstruction performance and observed a **sufficiently low reconstruction error** (MSE: 0.0076), demonstrating that the pre-trained VAE can effectively encode event frames.\nTechnically, the VAE is trained on large-scale image data and primarily serves as an encoder to project event frames into latent space. The unique characteristics of event data are captured and **learned within the conditional DiT branch**. Also, fine-tuning the VAE on event data remains a promising direction for future work and could further enhance performance.\n\n\n\n\n\n**Q2: Different injection approaches for RGB (LoRA) and event data (ControlNet-style)**\n\nTable 4 presents the results without LoRA for the image branch. Technically, our architectural design follows modern practices for conditioning diffusion models.\n\n* **RGB conditioning:** We condition the diffusion backbone by concatenating each input RGB frame with the noisy latent. Since the image-to-video CogVideoX is pre-trained with similar conditioning strategy to extract RGB features, it naturally facilitates extending the diffusion prior to our low-light images. Leveraging this property, we repurpose the generic video-generation model for low-light video enhancement via a lightweight LoRA adaptation, thereby retaining the original diffusion priors while introducing only a minimal parameter overhead.\n\n* **Event conditioning:** We employ a ControlNet-style conditioning approach, which is a widely adopted technique in recent conditional diffusion models. The ControlNet branch is essential for handling a completely different domain not learned by the pre-trained CogVideoX, similar to recent ControlNet applications (e.g., sketch-based and depth-based generation).\n\nThese design choices enable us to effectively leverage the prior knowledge of the pre-trained video diffusion model while adapting it to the specific requirements of our modality-agnostic low-light video enhancement. We will clarify this rationale in the revised version.\n\n\n\n\n**Q3: Auto-regressive long video reconstruction limitations**\n\nWe have demonstrated long-video results (~300 frames) in our supplementary video demo. Technically, unlike existing image-to-video frameworks that rely solely on the first frame and **lack intermediate control**, our approach is constrained by sequential frames and event data. To further prevent over-reliance on the initial frame, we **randomly add noise and mask to the frames** during training (Line 191), encouraging the model to leverage both event streams and RGB information more effectively. \nOverall, these design choices substantially reduce error propagation compared with conventional first-frame-based auto-regressive methods.\n\n**Q4: \"Modality-agnostic\" terminology clarification**\n\nWe appreciate this concern. By *“modality-agnostic,”* we mean that our framework can operate effectively and adaptively under varying modality availability, including cases where certain modalities are partially corrupted or entirely absent. This differs from modality-invariant approaches, which assume equal contributions from all modalities. We will make this clearer in the final version. \nIn this work, we focus exclusively on RGB and event data for consistent low-light video enhancement. For modalities beyond RGB and event data, we believe that our modality-agnostic training pipeline is flexible enough to be extended to incorporate additional modalities for diverse downstream tasks, as different modalities can be treated as different conditions to the diffusion backbone. We will discuss this in the final version.\n\n\n**Q5: Limited methodological innovation in diffusion framework**\n\nWe would like to re-iterate the innovation of our framework. While we build upon modern diffusion techniques, our contributions lie in the novel integration and adaptation of existing techniques for the challenging consistent LLVE task. We facilitate a modality-adaptive diffusion framework that enables consistent LLVE across varying illumination and modality availability for the first time. Technically, we propose a modality-adaptive training pipeline that injects both modalities and fine-tunes on integrated and augmented datasets. Additionally, we present a well-designed modality-adaptive guidance rescaling mechanism that dynamically adjusts guidance based on illumination conditions and modality reliability. Overall, **the model design, training pipeline, and dataset are all developed in synergy to address this task**.\n\n**Q6: Counterintuitive multi-modal performance in Table 1**\n\nThank you for this question. This is indeed a surprising observation that many methods using both event and image modalities perform worse than single-modality approaches. \nFor event-only methods, since these methods input only event data, they are not sensitive to image intensity variations across different scenarios. The intensity of reconstructed frames remains stable within a certain range, resulting in relatively higher PSNR compared to image-involved methods, which are severely affected by varying frame intensities. Additionally, we compare PSNR* with intensity calibration to evaluate the structural fidelity of reconstructed videos, where image-involved methods achieve better performance than event-only methods. \nWhen comparing image-only and multi-modality methods, we observe that EvLowlight and EvLight++ achieve better performance, while other methods show comparable performance. This is because these methods typically overfit to specific low-light conditions and mostly fail to adapt to varying illumination scenarios.\n\n**Q7: Computational efficiency concerns**\n\nThe table below summarizes the efficiency profile of our method. Compared to traditional frameworks, our diffusion-based approach requires more parameters but achieves significantly better generalization across diverse scenarios. \nFor efficiency improvements, we have tried one-step diffusion and flow matching, which can reduce sampling to 20 DDIM steps. However, we recognize this represents a **trade-off between computational efficiency and performance**, which may be a promising future direction.\n\n\n| Method        | Params (M)  | Memory (GB) | Inference Time (s/frame) |\n|---------------|------------|-----------|-----------|\n| LLFlow-L-SKF  | 409.50     | 39.91     | 0.30\n| Retinexformer | 15.57      | 1.61      | 0.12\n| ELIE          | 440.32     | 33.36     | 0.32\n| eSL-Net       | 560.94     | 0.56      | 0.18\n| EvLight++     | 225.91     | 26.21     | 0.21\n| EVDiffuser (Ours)    | 7932.80    | 30.89     | 5.63\n\n**Q8: Generalization to real-world low-light scenarios**\n\nSorry for the confusion. We have included real-world self-captured sequence for qualitative evaluation in the supplementary material (Fig. 8).\nWhen transitioning from outdoor to dark indoor environments, our method successfully reconstructs videos with consistent lighting, even under extremely low-light conditions, thanks to the modality-agnostic framework.\nIn contrast, existing methods either overexpose outdoor scenes or fail to reconstruct extremely low-light regions, performing well only within a narrow illumination range. This further demonstrates our method’s strong generalization capability in realistic scenarios.\nDue to the time limit, we will collect additional real-world data in the future and include it in the revised version.\n\n\n**Q9: Comparison with contemporary diffusion models**\n\nDirect comparison is not practical because EVDiffuser is designed for a different task than general video generation models (e.g., Wan). These large-scale models serve as powerful **foundation models** and can provide stronger backbones for EVDiffuser compared to CogVideoX. We attempted to adapt the Wan model for our task, and although it achieved better video quality, it incurred an excessively high computational cost, making it impractical for us.\n\n\n\n**Q10: Performance without both modalities**\n\nThe model can still generate reasonable results when both RGB and event data are missing. This is because our model is based on the pre-trained CogVideoX and fine-tuned by randomly masking both modalities for CFG inference. However, this scenario is impractical for our LLVE task."}, {"review_id": "NrhVL3bbFs", "reviewer": "Reviewer_j9ey", "summary": "This paper proposes an event-guided video enhancement method targeting varying lighting conditions. To achieve modality-agnostic reconstruction from both event and image data, two modality-agnostic DiT models are introduced—one for each modality. Additionally, to account for the distinct characteristics of each modality, a Modality-Adaptive Guidance Rescaling strategy is proposed for classifier-free guidance. The method is validated on both synthetic and real datasets to demonstrate its effectiveness. While it outperforms existing methods under varying lighting conditions, it does not surpass state-of-the-art methods on the low-light enhancement dataset SDE, as shown in Table 6.", "questions": "1. A comparison with existing methods trained on the same dataset should be included to ensure fair evaluation.\n2. Performance comparisons in terms of runtime, FLOPs, and the number of parameters should be provided to assess computational efficiency.\n3. The results for the \"Event only\" and \"Image only\" settings should be included in Table 1 to better understand the contribution of each modality.\n4. The smoothness effects introduced by the proposed method should be further explained and analyzed, particularly regarding their impact on visual quality and detail preservation.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 1, "originality": 2, "strengths_and_weaknesses": "The proposed method explicitly accounts for the differences between events and images by formulating a two-branch pipeline for reconstruction. It significantly outperforms existing methods in both qualitative and quantitative evaluations under varying lighting conditions, demonstrating superior performance in consistency preservation.\nHowever, several concerns remain:\n1. Unfair Training Conditions: The proposed method is trained on a specifically designed dataset aimed at consistency preservation under varying lighting conditions, whereas the compared methods are trained under consistent lighting. This raises the question of whether the observed improvements stem from the dataset or the model itself. A fair comparison—where all methods are trained on the same dataset—is necessary to better isolate and validate the contribution of the proposed model.\n2. Efficiency Concerns: The use of two DiT models raises concerns regarding computational cost. A comparison of runtime, FLOPs, and the number of parameters with existing methods should be included to assess the efficiency of the proposed approach.\n3. Ablation of Input Modalities: The proposed method can operate with \"Event only\" and \"Image only\" inputs, yet the results for these settings are missing in Table 1. Including this comparison would help demonstrate the robustness and flexibility of the model when only one modality is available.\n4. Detail Preservation: There are concerns regarding the model’s ability to preserve fine details. For example, in “Indoor Scene 3” from the supplementary video, sharp details appear overly smoothed by the proposed method. This issue warrants further analysis and discussion.", "was_revised": true, "final_justification": "The authors have addressed parts of my concerns. Although I still have reservations regarding the slow runtime and occasional blurriness artifacts, I acknowledge that this work represents a valuable attempt toward advancing low-light video enhancement.", "rebuttal": "We sincerely thank the reviewer for the thorough evaluation and constructive feedback. Below, we provide detailed responses to each point.\n\n**Q1: Unfair Training Conditions**\n\nSorry for the confusion. There may be a misunderstanding regarding the training pipeline of EvDiffuser and the comparison methods. We would like to clarify the following points:\n\n1) **Overall Framework:** First of all, we clarify that our method addresses a novel and realistic problem: **modality-adaptive** video enhancement under **various lighting conditions**. Both the model architecture and the dataset are designed **synergistically** to tackle this problem.\n\n2) **Dataset Integration:** To enable the model to handle different modality availabilities, our dataset integrates RGB, event, and RGB-event paired data. Based on a **modality-agnostic training pipeline**, our approach can effectively learn from all these data types. In contrast, the comparison methods are modality-specific and can only utilize a subset of the dataset, making **direct training under integrated dataset infeasible**. For example, training EvLowlight (event+RGB) on an RGB-only dataset is meaningless because it relies on valid inputs from both modalities for interaction. Similarly, Retinexformer (RGB) cannot be trained on the RGB-event dataset, as it is incapable of processing event data.\n\n3) **RGB Augmentation:** To further handle diverse illumination conditions, our model applies random degradations to RGB images during training (as illustrated in Line 179). To validate this, we performed an additional comparison by applying the same augmentation strategy to the training sets of RGB-based baseline methods. As shown in the table below, although the baselines show modest improvements, they still struggle to adapt to varying illumination because their modality-interaction mechanisms are not designed for such conditions.\n\n| Method     | PSNR $\\uparrow$ | PSNR* $\\uparrow$ | SSIM $\\uparrow$ | LPIPS $\\downarrow$ |\n|------------|------|------|------|-------|\n| Retinexformer | 16.89 | 24.10 |0.6815 | 0.2540 |\n| EvLight++  | 15.42 | 24.93 |0.6980 |0.3167 |\n| EvLowlight (Ours) |21.55 | 27.14 | 0.8338 |0.2249 |\n\nIn conclusion, our research focuses on modality-adaptive video enhancement under diverse lighting conditions. The model design, training pipeline, and dataset are all developed in synergy to address this task. The additional comparison using similar RGB augmentation for baseline methods further validates the effectiveness of our approach. We hope this clarifies the misunderstanding and addresses your concerns.\n\n\n\n\n**Q2: Computational Efficiency Analysis**\n\nWe clarify that our framework does not utilize two entire DiT models. Instead, building on the pre-trained CogVideoX DiT backbone (42 blocks), we employ only the first **18 blocks** as the conditioning branch, as described in Line 169. This approach significantly reduces computational overhead while preserving effectiveness.\n\nThe table below summarizes the efficiency profile of our method. Compared to traditional frameworks, our diffusion-based approach requires more parameters but achieves significantly better generalization across diverse scenarios.\n\n| Method        | Params (M)  | Memory (GB) | Inference Time (s/frame) |\n|---------------|------------|-----------|-----------|\n| LLFlow-L-SKF  | 409.50     | 39.91     | 0.30\n| Retinexformer | 15.57      | 1.61      | 0.12\n| ELIE          | 440.32     | 33.36     | 0.32\n| eSL-Net       | 560.94     | 0.56      | 0.18\n| EvLight++     | 225.91     | 26.21     | 0.21\n| EvLowlight (Ours)    | 7932.80    | 30.89     | 5.63\n\n\n**Q3: Single-Modality Performance Results**\n\nFig. 7 have shown the qualitative results of EVDiffuser under single-modality configurations. \nQuantitatively, we provide results for EVDiffuser on the V-SDE-out dataset. These results demonstrate comparable performance in both event-only and RGB-only configurations compared to existing methods, validating our framework's robustness and flexibility. We will incorporate these results into Table 1 in the revision.\n\n|Input | Method     | PSNR $\\uparrow$ | PSNR* $\\uparrow$ | SSIM $\\uparrow$ | LPIPS $\\downarrow$ |\n|------|------------|------|-------|------|-------|\n| Event-only | ETNet | 14.21 |16.25 |0.4844 |0.6217 |\n|            | HyperE2VID | 13.80 |15.94 |0.4596 |0.6283 |\n|            | **EVDiffuser (ours)** | 13.69 | 16.11 | 0.4856 | 0.5920 |\n| RGB-only   | LLFlow-L-SKF | 10.50 | 20.16 | 0.4403 | 0.4331 |\n|            | Retinexformer | 8.36 |18.89 |0.3585 |0.5126 |\n|            | **EVDiffuser (ours)** | 17.20 | 24.48 | 0.6997 | 0.2102 |\n| Event+RGB | EvLight | 9.06 | 19.80 | 0.4099 | 0.3914 |\n|           | EvLowlight | 9.37 |20.33 |0.4331 |0.4527 |\n|           | **EVDiffuser (ours)** | 19.71 |26.53| 0.8044 |0.1999 |\n\n\n\n**Q4: The smoothness effects introduced by the proposed method**\n\nWe respectfully disagree with the assessment of smoothness in the *“Indoor Scene 3”* example. Our method successfully reconstructs fine details, such as the **electric wire and its shadow**, whereas existing methods exhibit significant artifacts and loss of detail. Quantitative evaluations in Table 1 further demonstrate that our approach achieves higher PSNR and SSIM scores, indicating superior detail preservation compared to existing methods.\nAdditionally, we conducted a user study with 16 participants to evaluate the performance of different methods. We employed preference ranking to assess user satisfaction based on four key aspects: lighting continuity, video quality, detail preservation, and similarity to the ground truth (GT similarity).\nAcross all participants and evaluation criteria, our method consistently achieved the best preference score, showing that it is perceived by users as significantly superior to current baseline methods. We will include these results in the revised manuscript.\n\n\n| Method | lighting continuity $\\downarrow$ | Video Quality $\\downarrow$ | Detail Preservation $\\downarrow$ | GT similarity $\\downarrow$ |\n| --- | --- | --- | --- | --- |\n| ET-Net | 4.375 | 4.9375 | 5 | 4.875 |\n| Retinexformer | 3.8125 | 3.5625 | 3.0625 | 3.6875 |\n| Evlight++ | 3.0625 | 2.4375 | 2.6875 | 2.8125 |\n| EvLowlight | 2.6875 | 3.0625 | 3.125 | 2.5625 |\n| EvLowlight (Ours) | **1** | **1** | **1.125** | **1.0625** |"}]

Author rebuttal:

Meta-review: The paper proposed EVDiffuser, a diffusion-based framework for consistent low-light video enhancement (LLVE) under varying illumination conditions. It addresses two key limitations of existing methods: (1) sensitivity to illumination fluctuations, and (2) reliance on simultaneous availability of both RGB and event modalities. The core innovations include: 1）A modality-agnostic diffusion pipeline that treats RGB and event data as optional conditions, enabling robust performance even when one modality is missing. 2）Modality-adaptive guidance rescaling to dynamically balance contributions from each sensor based on illumination levels. 3）A new benchmark (V-SDE/V-SDSD) simulating real-world illumination variations. 4）Experiments demonstrate SOTA results on diverse settings (RGB-only, event-only, multimodal).

The paper was reviewed by four reviewers with diverse comments. The authors submitted rebuttal and there had been discussions among the authors and reviewers. The authors addressed most of the concerns. Afterward, the final ratings were 3XBorderline Accept and 1XBorderline Reject. The only negative reviewer's remaining concerns lie in the heavy parameters of the proposed model and the too long inference time (10 out of 11 comments have been addressed). As provided by the authors, the parameters of the proposed model is 20X other method, which may create concerns for the applications. While all methods have their application scenarios, given the offline nature of the method, it is fine.

I would like to recommendate to accept the paper and the authors are requested to revise the paper accordingly.
Decision: Accept (poster)