Dynamic Gaussian Splatting from Defocused  and Motion-blurred Monocular Videos
Authors: Xuankai Zhang
Junjin Xiao
Qing Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Novel View Synthesis
Dynamic Gaussian Splatting
Deblur
Primary area: applications
TL;DR: A Unified Framework for Dynamic Gaussian Splatting from Defocused and Motion-blurred  Monocular Videos

Abstract: This paper presents a unified framework that allows high-quality dynamic Gaussian Splatting from both defocused and motion-blurred monocular videos. Due to the significant difference between the formation processes of defocus blur and motion blur, existing methods are tailored for either one of them, lacking the ability to simultaneously deal with both of them. Although the two can be jointly modeled as blur kernel-based convolution, the inherent difficulty in estimating accurate blur kernels greatly limits the progress in this direction. In this work, we go a step further towards this direction. Particularly, we propose to estimate per-pixel reliable blur kernels using a blur prediction network that exploits blur-related scene and camera information and is subject to a blur-aware sparsity constraint. Besides, we introduce a dynamic Gaussian densification strategy to mitigate the lack of Gaussians for incomplete regions, and boost the performance of novel view synthesis by incorporating unseen view information to constrain scene optimization. Extensive experiments show that our method outperforms the state-of-the-art methods in generating photorealistic novel view synthesis from defocused and motion-blurred monocular videos. Our code is available at https://github.com/hhhddddddd/dydeblur.

Dynamic Gaussian Splatting from Defocused and Motion-blurred Monocular Videos
Xuankai Zhang', Junjin Xiao', Qing Zhang"
SSun Yat-sen University
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httpp::/d@ydebluu.gttttuu
SoM
De4DCS 38.845
42.359
Defocus Blur: PSNR (d.)
D2RF
DyBLuRF =1
MAD
D3DGS
22
Sharp GT
SoM
D2RF
Do4DGS
Ours
Motion Blur: PSNR (dB)
Figure 1: Performance of our method. Our method allows synthesize high-quality sharp novel
views for videos with defocus blur (top) and motion blur (bottom). As shown on the right, our method not only obtains significantly better results than existing methods, e.g., D3DGS (15), SoM (52), D2RF 32), DyBluRF [47), and De4DGS [58], but also achieves performance of 65.141 FPS at resolution of 512 288 NVIDIA RTX 3090 GPU.
Abstract
This paper presents unified framework that allows high-quality dynamic Gaussian Splatting from both defocused and motion-blurred monocular videos. Due to the significant difference between the formation processes of defocus blur and motion blur, existing methods are tailored for either one of them, lacking the ability to simultaneously deal with both of them. Although the two as blur kernel-based convolution, the inherent difficulty in estimating accurate blur kernels greatly limits the progress in this direction. In this work, go step further towards this direction. Particularly, we propose to estimate per-pixel reliable blur kernels using blur prediction network that exploits blur-related scene and camera information and is subject to a blur-aware sparsity constraint. Besides, we introduce dynamic Gaussian densification strategy to mitigate the lack of Gaussians for incomplete regions, and boost the performance of novel view synthesis by incorpo- rating unseen view information constrain scene optimization. Extensive experi- ments show that our method outperforms the state-of- tfate-of-the-art methods in generating photorealistic novel view synthesis from defocused and cootion-blurred monocular videos. Our code is available at http:/hwwww.ooooohhdhhhheececeee httpa///b..ooooccddddddd///eeeld
be jointly modeled
"Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Novel view synthesis of dynamic scenes from monocular videos is a very important problem, with applications in various scenarios such as augmented reality, virtual reality, and 3D content creation. Recent progress in this field mostly aims to learn renderable 3D Gaussian representations from monocular videos. Although some of them have demonstrated impressive results of novel view synthesis [24 55 63 [53, their performance typically deteriorates significantly on blurry videos, making methods applicable to blurry monocular videos particularly necessary.
32 of 3D Gaussian Splatting (3DGS) 15] and Neural Radiance have attempted to reconstruct dynamic scenes from blurry monocular videos, 47 30 tackles motioned-blurree videos by computing a weighted sum of multiple rendered images within the exposure period, while [32] focuses on dealing with defocus blur using layered Depth-of- of-Feld (DoF) volume rendering. Although these methods demonstrate promising results, due to the significant difference between the formation process of motion blur and defocus blur, their effectiveness is limited to either notion-blurred or defocused videos. Currently, there does not exist a method that can effectively handle both types of blurry videos while enabling high-quality novel view synthesis.
Some recent variants 588
30
Field (NeRF)
35
where [58]
In this work, we present a framework that allows high-quality dynamic Gaussian Splatting from both defocused and motion-blurred monocular videos. To this end, we employ blur kernel based convolution to jointly model the two blur types. To obtain reliable blur kernels from dynamic scenes, we develop blur prediction network (BP-Net) that is subject a blur-aware sparsity constraint to simultaneously predict the blur kernel and pixel-level intensity. Moreover, we introduce a dynamic Gaussian densification strategy to mitigate the lack of Gaussians for incomplete regions, and propose to boost the performance of novel view synthesis by incorporating unseen view information to constrain scene optimization. In summary, our main contributions are as follows:
We introduce unified framework for dynamic Gaussian Splatting from both defocused and motion-blurred monocular videos, which to our knowledge, makes the first attempt in this field.
We develop a blur prediction network equipped with a blur-aware sparsity constraint, and introduce dynamic Gaussian densification strategy well unseen view combined scene optimization scheme.
We show that our method outperforms previous methods on both defocused and motion-blurred monocular videos.
2 Related Works
4D Reconstruction. Recent works have extended 3DGS to 4D domain to model dynamic scenes
11
29
63
26]
One line of works define motion as a time-conditioned deformation network
that warps Gaussians from canonical space to observation space (77 63 12
16 [3] 27]. Among
these, DeformableGS [63] employs MLPs predict Gaussian deformations, while 4DGaussians
(57 replaces MLPs with multi-resolution Plane [5) Another line of works model motion as
trajectories of 3D Gaussians
[31 26 40 14
E-D3DGS 2 introduces per-Gaussian
and temporal embeddings to encode time-awweeee aware information, whereas Shape-of Mo-Mono [52] models motion as a linear combination of SE( motion bases. However, existing dynamic 3DGS methods
rely on sharp input images and often struggle to synthesize photorealistic novel views when motion
or defocus blur is present in the inputs.
primarily rely on CNNs (46) and RNNs [69]. Later approaches enhance the deblurring process by incorporating 44. and 3D geometry 37 38 55
Image deblurring. Early works on image and video deblurring 19
20
49 36 65
additional cues such as depth maps 39 25 50 M light fields +3
59 61. Leveraging multi-view and 3D information, NeRF- and 3DGS based deblurring methods Among these, B O model blur by predicting deformable sparse kernels with per-pixel bases. Others simulate the physical formation of blur by integrating multiple sharp images over the exposure time [68 51 38 or use camera model that learns aperture and focal length parameters incorporate 3D geometry into monocular video deblurring. However, existing methods are typically designed for either defocus blur or motion blur.
can generate sharp images with improved scene consistency [4] 21
34 22
D
some approaches [34] 22
33
[55 (5). Additionally, some works 47

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Learning Densified Dynamic Gaussians
Unified Blur Synthesis
Canonical Space
Blur aware Sparsity Constrain: Lppa
Depch Reprojection
Mobion Hoses
Camuri Index
Pixel Position
Tracking
Dynamic
Peanure Extructrr
DP-Net
Kempls
Inteusitt
Foreground
DA
Remipping
Depth
Convolution
Blending
Imput
Blurry
Depths
Oeservation Space
SAM
Depch Reprojection
Dynamic
Stati
Impgge
Blumy Imsuge
Input Hlurry Image
Outour Blury Image
Masks
CS
GS
Figure 2: Overview of our method. We initialize static Gaussians via depth reprojection and dynamic Gaussians from tracking points, modeling their transformations with learnable motion bases. After stable training, we densify dynamic Gaussians using foreground remapping. For blur modeling, a network predicts per-pixel blur kernels and intensity, enabling blur synthesis through convolution and blending operation. The reconstruction loss between synthesized and input blurry images optimizes the Gaussians for sharper results.
In contrast, our approach effectively removes both motion blur and defocus blur from monocular videos, producing high-quality novel-view images of dynamic scenes.
3 Method
Our method aims to achieve dynamic Gaussian Splatting that enables high-fidelity yet sharp novel view synthesis from any given monocular video with defocus blur motion blur. Figure 2 presents the overview of our method.
3.1 Learning Densified Dynamic Gaussians
Representing scene dynamic 3D Gaussians. We adopt Shape-of-Motion 52 to initialize 3D Gaussians and model scene motion. It separately models dynamic static Gaussians, representing motion with compact set of SE(3) motion bases. Specifically, dynamic Gaussians are initialized by reprojecting the depth of 2D tracking points in the canonical frame to, obtained via Depth-Anything (62) and TAPIR [10]. Static Gaussians are initialized by reprojecting background depth across frames and share the same parameter composition as the original 3D Gaussians. Dynamic Gaussians include an additional motion coefficient motion. The affine transformation from the canonical frame ty to the observation frame is computed follows:
IR No, which combines SE( motion bases to model their
No (b)
b=0
Where N2, denotes the number of (3) motion bases, and (T()) of motion base b. The affine transformation Tio-r consists of two components: rotation matrix R.o
represents the affine transformation
and translation vector Lt The transformation of the pose parameters (μ, R) for the dynamic Gaussian from the canonical frame to the observation frame is defined follows:
H = R o + ttg
R₂ = Reg-
The transformed dynamic Gaussians in observation space, along with the static Gaussians, are then processed through a differentiable rasterization pipeline to generate the final rendered image 1, depth map D. and mask M.
Dynamic Gaussian densification.
Shape-of-Motion 52 initializes dynamic Gaussians using point clouds by reprojecting 2D tracking points from the canonical frame into 3D space, Although the canonical frame selected as the one containing the most visible 2D tracking points across all frames, tracking points that are invisible in

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the canonical frame can still lead to partial dynamic Gaussians with inaccurate depth. This occurs because the depth values at the corresponding locations of invisible 2D tracking points do not reflect the true depth after reprojection. To address this, we initialize dynamic Gaussians using only visible 2D tracking points in the canonical frame. While this improves initialization accuracy, it may lead to incomplete dynamic regions. To compensate, we supplement dynamic Gaussians by reprojecting dynamic regions with depth maps from all observation frames. Then, we transform the dynamic Gaussians in the observation frames to the canonical frame using foreground remapping. Since this operation requires relatively stable and accurate motion bases, we perform dynamic Gaussian densification only once after the first Na training iterations.
To this end, we first identify dynamic pixels the training images using motion masks M, obtained via the off-the- off-the-shelf method SAM [8]. A random subset of pixels is selected in each observation frame. For dynamic pixel g in frame L, the mean 1 of the corresponding Gaussian G' in observation space is defined as:
(3) where D(g) is the depth of pixel g. is the projection function for frame I, and P. represents the camera extrinsics for frame t. We use foreground remapping to compute the corresponding position Ml of Gaussian G in the canonical frame. Specifically, we first determine all affine transformations for all existing dynamic Gaussians from to to L. We then select the affine transformation Th = [RC- [ corresponding to the dynamic Gaussian G' that is closest to j after transformation. which gives:
1/= P D(g))),
1. 10 G (
3.2 Unified Blur Synthesis
During training, we explicitly model the blurring process and jointly optimize sharp 3DGS represen- tation along with the blur parameters, ensuring that the synthesized blurry images match the input blur image. Although the formation processes of defocus blur and motion blur are fundamentally different, both types of blur can be approximated as a weighted combination of a pixel and its neighboring pixels. Thus, the generation processes of motion blur and defocus blur can be unified under simple yet powerful mathematical model:
B(x)=
Σ ((x:) s.t
Σ (x:) 1,
(5)
(EE)(2
where [(x;) is the clear pixel value at pixel coordinates xy around the neighborhood of æ, and hi denotes the blur kernel for pixel æ.
Using the per- per-pixel blur kernel he from Eq. 5). we can generate blurry image B from the sharp image I. The main challenge is estimating reasonable blur kernel kg for each pixel r. An intuitive solution would be to use a CNN to estimate ka for each pixel in the sharp image I obtained from rasterization. However, jointly optimizing the 3DGS and the CNN blur kernel prediction network under the constraint of blurry images B can lead to non-rigid distortions in the reconstructed 3DGS. This is in line with expectations because it is possible that the reconstructed 3DGS and the CNN blur kernel prediction network deform together without affecting the reconstructed blurty result. To address this, we design the Blur Prediction Network (BP-Net) Fe, which simultaneously predicts the per-pixel blur kernel hi and the corresponding blur intensity my- Using the blur intensity miz, we compute the output blurry image pixel value B(x) by blending the sharp image pixel value [xx) and the blurry image pixel value B(x):
B(x) = (1-mm)) f(xx) + m. B(x).
Computing the reconstruction loss between the output blurry image i and the input blurry image B directly constrains the rendered image i using the sharp regions in B, progressively guiding toward sharper 3DGS-based scene representation, while ensuring that most of the blur is explicitly modeled by the CNN blur kernel prediction network.
Blur prediction network. The Blur Prediction Network (BP-Net) is a four-layer CNN model that takes camera and scene information inputs, both of which significantly contribute to blur

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Sharp GT
D3DGS
SoM
D2RF
DyBluRF De4DGS
Ours
GT
Figure 3: Visual comparison of novel view synthesis on the D2RF defocus blur dataset
32
in monocular videos. Camera motion and suboptimal camera settings can introduce blur, while dynamic objects and scene depth strongly correlate with blur magnitude. The camera information is represented by learnable embedding vector e(i) obtained from the camera view i. The scene information is encoded into Seeene through three-layer CNN Scene Feature Extractor, which uses the rendered image I, depth D, and motion mask M as inputs. Given the variation in blur across different pixels in real blurry images, we also include the pixel coordinate positional encoding p(x) as input the BP-Net:
lgg m. Fe(e(i), facene (x),p(z)),
(7)
where k2 and m, denote the blur kernel and blur intensity for pixel x, respectively. To improve the scene information representation, add skip connections from fscene after the first two layers of the BP-Net.
Blur-aware sparsity constraint. The blur kernel weights for mildly blurred pixels should be more concentrated around the center, while those for severely blurred pixels should be more uniformly distributed. To prevent unrealistic blur kernels from excessively blurring mildly blurred regions, we use the blur intensity my constrain the weight distribution of the corresponding blur kernel ky- Specifically, we use the blur kernel center weight (c) to quantify the sparsity of the blur kernel kn. A smaller kg (c) suggests more dispersed kernel, implying that the corresponding pixel is more severely blurred. Based on this, we design a blur-aware center weight e₂ follows:
for the blur kernel k2 as
Cs sigmoid -1 (1- sg(m)))),
(8)
where scale a scale factor (set to 5), and ag denotes the ttop-gradient operation. Clearly, the pixel-wise blur intensity m, is negatively correlated with the corresponding blur-aware center weight Cxj as m. increases, ea decreases. We then compute the L, loss between the blur-aware center weight Cx and the blur kernel center weight kz. (c):
Cspa (i(o,,k(()))
During training, we adopt the blur-aware sparsity constraint Expa for training only when the blur intensity m. has been trained for number of Napo iterations to be stable.
3.3 Training Strategy
Monocular reconstruction of complex dynamic scenes is ill- ill-posed and prone to local minima due to the limited information provided by monocular videos, where each timestamp captures only single training view. This lack of sufficient views makes it difficult to accurately learn the 3D structure of dynamic scenes, often leading to overfitting to the training views. To mitigate overfitting and extract more cues from monocular videos, we obtain potential appearance information for unseen views surrounding the training views. During training, we use this appearance information to supervise the optimization process every N,, iterations. Since the appearance information from unseen views is
5

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Sharp GT
D3DGS
SoM
52
Ours
Figure 4: Visual comparison of novel view synthesis on the D2RF defocus blur dataset [32].
Here, we also compare with methods fed with deblurred images produced by a state-of-the-art video deblurring method [66] to manifest the effectiveness of our method.
less accurate than that from training views, we apply slightly different loss functions for them, as detailed in Section 3.4
We leverage the geometry and appearance information from the training views to obtain the appear- ance of unseen views. For instance, consider a pixel ps in the training view Vg. The corresponding pixel pt in the unseen view Vi can be expressed as:
Ps KP. 1 P.D.(p.) P..
(10)
Where K is the camera intrinsic matrix, D2 is the depth map for the training view V., Pa and P. are the camera extrinsics for the training and unseen views, respectively. We then use the color (p.))) of pixel P. derive the color (p:) of the corresponding pixel pt in the unseen view via reversed bilinear sampling [60, 54]. Similarly, the motion mask (p:) in the unseen view can be derived from M.(P.).
To ensure the accuracy appearance information in unseen views, we restrict the selection unseen views close to the training views. In most monocular videos, the camera poses of training views typically form a sequence of consecutive poses directed towards the scene. Therefore, we select unseen views on both sides of the training view sequence and insert unseen views between adjacent training views. We implement this by generating two types of unseen views: (i) parallel-unseen views: generated by interpolating between adjacent training views along the camera trajectory, (ii) perpendicular perpendicular-unseen unseen views: generated by first computing a local perpendicular direction to the camera trajectory and then perturbing the training view's camera center along this perpendicular direction by distance of [0.5, 1] (normalized units). Importantly, the timestamp an unseen view matches the timestamp of the training view from which it is generated.
3.4 Loss Function
Reconstruction loss. We supervise the training process with a reconstruction loss to align per-frame pixel-wise color input B. We compute the blurry image B according to Eq. and Eq. (6). The blurry image B is supervised by the following reconstruction loss:
Cree (1 L) (B, B) BL.sim B,
(11)
where L1 and Lesim are the loss and SSIM (56) loss, respectively, and 3 is set to 0.2. We also constrain the scene geometry using the depth and mask M:
Eyeo Adepth Ci (D,D) Amast (M,M) (M,
(12)

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Sat
Salie
Sharp GT
D3DGS
SoM
D2RF
DyBluRF De4DGS
Ours
GT
Figure 5: Visual comparison of novel view synthesis on the DyBluRF motion blur dataset
47
where Adepth and Amask are set to 0.075. During iterations with unseen views, we only use the mask, excluding depth, to prevent the inaccurate geometry of unseen views from distorting the scene geometry.
Table I: Quantitative comparison of novel view synthesis the D2RF defocus blur dataset 32 and the DyBluRF motion blur dataset (7))
Defocus Blur SSIM
Motion Blur
Method
Param.
Training Time
PSNR
LPIPS
PSNR
SSIM
LPIPS
D3DGS 63
22.54
0.715
0.215
21.54
0.675
0.287
42.6M
10 mins
SoM
28.32
0.784
0.164
26.21
0.823
0.109
164.2M
mins
D2RF
32
27.04
0.808
0.128
23.67
0.745
0.120
2.7M
48 hrs
DyBluRF [4
26.24
0.788
0.159
24.53
0,864
0.079
1.3M
48 hrs
De4DGS
58
28.49
0.791
0.154
26.62
0.871
0.059
754.6M
20 hrs
Ours
29.39
0.859
0.078
27.01
0.876
0.056
192.2M
hr
Smoothing loss. To improve scene motion representation, we introduce a smoothing constraint
Lumo for the dynamic Gaussians' deformation. Similar to Shape-of- [52), Lsmo includes
two components: smoothness constraint on the SE((3 motion bases across adjacent frames and smoothness constraint the mean of dynamic Gaussians.
The overall training objective for the network is:
L = Lree + Legen + Lsmo + Leppa-
(13)
Experiments
Evaluation datasets. We evaluate method two datasets, including the from D2RF 32 of defocus blur and the other one from DyBluRF (47) of motion blur. The first dataset from D2RF consists of 8 dynamic scenes, where each scene contains sharp stereo image sequences and their corresponding blurry images. The other one from DyBluRF contains 6 motion-blurred scenes composed of blurry stereo images and the corresponding sharp images. We train and evaluate our method on all sequences from the two datasets, using their left-view blurred sequences for training and the corresponding right-view sharp sequences for evaluation. Note that, similar to previous methods, the downsampled images in the two datasets are utilized for training and evaluation.
we quantitatively evaluate our performance novel view synthesis using PSNR, SSIM 56, and LPIPS [67.
Metrics. Following previous work [52] 58 32 471,
Implementation Details. We set the number of motion bases N₂, to 20, and the blur kernel size K 9. We employ the Adam optimizer [17] to jointly optimize Gaussians and SE(33 motion bases and the Be-Net. The learning rates are set to 1.6 x 10-4 for motion bases, and x 10 for

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Table 2: Quantitative comparison of novel view synthesis the D2RF defocus blur dataset 32 and the DyBluRF motion blur dataset [47]. Note, BSSTNet [66] a state-of oate--of-thearr video
deblurring method, and 6[66] D3DGS 63 means feeding the dynamic scene reconstruction 63
with deblurred images produced by 166
Defocus Blur
Motion Blur
Method
PSNR
SSIM
LPIPS 4
PSNR f
SSIM f
LPIPS 4
D3DGS
22.54
0.715
0.215
21.54
0.675
0.287
66
D3DGS 63
24.42
0.723
0.179
21.72
0.653
0.279
SoM
52
28.32
0.784
0.164
26.21
0.823
0.109
66
SoM 52
28.56
0.786
0.164
26.33
0.825
0.105
Ours
29.39
0.859
0.078
27.01
0.876
0.056
Table 3: Quantitative ablation study on the D2RF defocus blur dataset [32 and the DyBluRF
motion blur dataset 47]. Note, "w/o Unseen." refers unseen view information utilized.
Defocus Blur
Motion Blur
Method
PSNR f
SSIM f
LPIPS +
PSNR t
SSIM
LPIPS +
w/o Lspo
29.03
0.842
0,086
26.63
0.854
0.072
w/o Shortcut
29.12
0.845
0.086
26.74
0.852
0,078
w/o DGD
29.19
0.843
0.085
26.53
0.847
0.109
w/o Unseen.
29.09
0.836
0.090
26.66
0.853
0.075
Full method 29.39
0.859
0.078
27.01
0.876
0.056
The learning rate for the Gaussians is consistent with that of the original 3DGS [5] We train each scene for 40,000 iterations and introduce unseen view information constrain the scene starting from iteration 3,000. We set the iteration interval N,, for using unseen view information to 5. Dynamic Gaussians densification is performed at N₂ 2,500 iterations. We introduce the unified blur synthesis model at iteration 3,500 and incorporate the blur-aware sparsity constraint at Napa 5,500 iterations. Training on a sequence of 512 288 resolution takes approximately hour on an NVIDIA RTX 3090 GPU, with rendering speed of 65. 15.143 fps for the same resolution.
BP-Net.
4.1 Comparison with State-of-the-Art Methods
Compared methods. We compare our method with various state-of-the-art methods including, DeformableGS [63] Shape-of-Motion (52). D2RF [32]. DyBluRF [47), and Deblur4DGS (58). where the method of D2RF (32 is designed for defocus blur, while the methods of DyBluRF [47] and Deblur4DGS [58] are tailored for motion blur. For fair comparison, we produce their results using
publicly-available implementation or trained models provided by the authors with the recommended
parameter setting.
Comparison defocus blur. Tables [ and report the quantitative results on the defocus blur dataset, where we can see that our method outperforms other methods, even existing methods that are fed with deblurred images produced by state-of- state-o--the-art the-art video deblurring method. The reason is that video deblurring methods cannot effectively ensure 3D scene consistency in the deblurred images, Figures and further present visual comparison of novel-view synthesis. As shown, our method exhibits a clear advantage in producing high-quality novel views in both dynamic and static regions, while the compared methods struggle to maintain structural details and provide sharp results. Please see the supplementary material for additional comparison results, including both image and video results of defocus blur.
Comparison on motion blur. As shown in Tables T and 2 our method quantitatively outperforms the compared methods on all three metrics, manitesting its effectiveness in dealing with motion blurred monocular videos. We also show the visual comparison in Figure 5 As can be seen, our method produces results with more incomplete structure details and sharper appearance. Please see the supplementary material for additional comparison results, including both image and video results of motion blur.

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Sharp GT
w/o DGD
Ours
GT
Sharp GT w/o Unseen.
Ours
GT
Figure 6: Left: Effect of dynamic Gaussian densification (DGD). Right: Effect leveraging unseen view information. Note, "w/o Unseen." refers unseen view information utilized.
Sharp GT w/o Shortcut w/o Epa
Ours
GT
Sharp GT
K=3
K=9
K=
GT
Figure 7: Left: Effect of blur-aware sparsity Constraint Lxpa and the shortcut BP-Net. Right: Effect of varying blur kernel size K.
4.2 More Analysis
Ablation study. We conduct an ablation study to evaluate the contribution of each component in our model. Specifically, we evaluate the effect of (i) removing blur-aware sparsity constraint (w/o f.pa). (ii) removing the shortcut BP-Net (w/o Shortcut), (iii) initializing the dynamic Gaussians using only the visible 2D tracking points in the canonical frame, without utilizing dynamic Gaussian densification (w/o DGD), (iv) removing unseen view information during training (w/o Unseen.). We report the quantitative results in Table 3 where we can see that each of our design has clear contribution. In addition, we Figures and 7 also qualitatively validate the necessity of each component in our model. Besides, we also assess the influence of different blur kernel size in Figure 7 As shown, a larger blur kernel (K) helps produce better results, but this trend becomes less obvious when K
9.
Limitations. Since our method relies on 2D image priors, errors arising from 2D prediction such as depth estimation and segmentation may degrade the overall performance of our method. Moreover, for dynamic scenes with large non-rigid motion blur, our method, as well as other state-of-the-art methods, would fail to produce high-quality novel-view results free visual artifacts, as demonstrated in Figure & Finally, similar the vanilla 3DGS, method has to be optimized for each scene separately.
Sharp GT
D2RF
De4DGS
Ours
GT
Sharp GT
D2RF
De4DGS
Ours
GT
Figure 8: Failure case. Our method may fail to handle a dynamic scene with large non-rigid motion blur. Note, the left column demonstrates results for defocus blur, while the right column presents motion blur outcomes.

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Conclusion
We have presented a unified framework for generating high-quality novel views from defocused and motion-blurred monocular videos. In contrast to previous methods, which are either tailored for defocus blur or motion blur, we propose to model both blur types using blur kernel- convolution. To this end, we develop blur prediction network exploiting blur-related scene and camera information to estimate reliable blur kernels and pixel-wise intensity. Besides, we introduce dynamic Gaussian densification strategy to mitigate the lack of Gaussians for incomplete regions and boost the performance of novel view synthesis by incorporating unseen view information to constrain scene optimization. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods in generating photorealistic novel view synthesis from defocused and motion-bburred monocular vidcos.
Acknowledgement. This work was supported by the National Natural Science Foundation of China (62471499), the Guangdong Basic and Applied Basic Research Foundation 50300002...
10

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Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer L.M policy htap:::///eurips.. www.eeeiiiisseeeoaanaeeeeeeeeeee 2025/ LLM) for what should or should not be described.
20

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A
Implementation Details
Network Architecture. The detailed architecture of Scene Feature Extractor Network and Blu Prediction Network in our framework are illustrated in Figure 9 We enhance the ability to capture high-frequency details by using positional encoding p for pixel coordinates x and a discrete variable embedding module (implemented with PyTorch) for camera indices i.
fscene
1
fscene
mxx
D
64
64
32
fscene
e(i)
64
64
64
M
pxx)
81
1x
Figure Scene Feature Extractor Network (Left). Scene Feature Extractor Network takes rendered image I, rendered depth D and rendered mask M input, and outputs scene feature Sscene- Besides the last layer, each layer outputs 64- 04-dimensional features with ReLU activations. Blur Prediction Network (Right). Blur Prediction Network takes scene feature facene, camera embedding vector e(i) and pixel positional encoding p(x) as input, and outputs blur kernel k2 and blur intensity my- Each layer outputs 64-dimensional features with ReLU activations, except for the last layer. Note, the last layer my is obtained via Sigmoid activations, while k is obtained via Softmax activations.
B Additional Quantitative and Qualitative Results
We compare our method with dynamic scene reconstruction methods 66 52
that use video-deblurred images as input. Figure 10 presents visual comparison of novel view synthesis on the motion blur dataset, where we can see that our method outperforms existing methods that are fed with deblurred images produced by a state-of-the-art video deblurring method. The reason is that video deblurring methods cannot effectively ensure 3D scene consistency in the deblurred images. Please see the video supplementary material for additional novel view synthesis comparison results.
B.1 Novel View Synthesis Comparison
D2RF and DyBluRF Dataset. We compare approach against BAGS 41 and De3DGS 21]. two methods designed to reconstruct sharp static scenes from blurred static images, and evaluate them on the D2RF [32] and DyBluRF [47] datasets. Table 4 and Figure 11 present the comparison results. Clearly, our method demonstrates significant advantages over other methods, producing sharper novel view images while better preserving realistic motion details.
D2RF-v2 and DyBluRF-v2 Dataset. We evaluate our method on two datasets (DyBluRFFv2 and D2RF-v2) with both motion and defocus blur occurring simultaneously. Note that we obtain the DyBluRF-v2 dataset by applying depth-of-ficld (DoF) rendering technique in Bokehme 42 to the original DyBluRF dataset [47 to simulate defocus blur. and create the D2RF-v2 dataset with motion blur by processing the original D2RF dataset [32 using the motion blur generation method in Davanet 770 Table 5 present the comparison results. As shown, our method clearly outperforms all the compared methods on the two datasets, verifying the advantage of our method handling cases with motion and defocus blur occurring jointly.
Deblur-NeRF Dataset. We compare our approach against De3DGS (21. which is designed to reconstruct sharp static scenes from blurred static images, and evaluate them on the Deblur-NeRF (34] dataset. Table and Figure present the comparison results. Clearly, our method demonstrates significant advantages over other methods, producing sharper novel view images.
21

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Sharp GT
D3DGS 63
66
65
SoM S2
66 552
Ours
GT
Figure 10: Visual comparison of novel view synthesis on the DyBluRF motion blur dataset
47
Here, we also compare with methods fed with deblurred images produced by a state-of-the-an video deblurring method [66] manifest the effectiveness of our method.
Sharp GT
BAGS
De3DGS
Ours
GT
Sharp GT
BAGS
De3DGS
Ours
GT
Figure 11: Visual comparison novel view synthesis. Here, we compare our method with methods that are designed to reconstruct sharp static scenes from blurred static scene images. Note, the left column demonstrates results for defocus blur, while the right column presents motion blur outcomes.
Table 4: Quantitative comparison of novel view synthesis on the D2RF defocus blur dataset 32 and the DyBluRF motion blur dataset (47).
Defocus Blur
Motion Blur
Method
PSNR 1
SSIM
LPIPS 1
PSNR
SSIM 1
LPIPS |
BAGS 41
24.41
0.730
0.167
24.27
0.723
0.208
De3DGS 21
23.74
0.716
0.190
22.45
0.689
0.253
Ours
29.39
0.859
0.078
27.01
0.876
0.056
B.2
Deblurring Comparison
We compare the deblur ring capability of our method with broad range of existing methods, including 3DGS- and NeRe-based methods for both dynamic and static scenes 58 41 21,, as well as transformer-bases video deblurring method [66]. Specifically, we compare the sharp images produced training views. For 3DGS- and NeRF-based methods, these images are rendered from the trained sharp scene representations using the same training views. Table 7 and Figure 13 present the comparison results. Our method outperforms 3DGS- and NeRF-based deblurring approaches and achieves performance comparable fate-of-the-ar video deblurring methods.
22

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THE
De3DGS 63
Ours
GT
Figure 12: Visual comparison of novel view synthesis the Deblur-NeRF dataset
34
Table Quantitative comparison of novel view synthesis on the D2RF-v2 defocus blur dataset and the DyBluRF-v2 motion blur dataset. The numerical results of defocus blur are obtained on the Shop and Car scenes of the D2RF-v2 dataset, and the numerical results of motion blur obtained on the Man and Seesaw scenes of the DyBluRF-v2 dataset.
Defocus Blur
Motion Blur SSIM
Method
PSNR
SSIM
LPIPS
PSNR
LPIPS
D3DGS 63
23.66
0.739
0.257
21.75
0.655
0.289
SoM 52
29.04
0.820
0.094
27.28
0.791
0.103
D2RF 32
27.82
0.795
0.132
25.89
0.722
0.133
DyBluRF 47
27.30
0.771
0.150
26.54
0.753
0.112
De4DGS
58
29.74
0.856
0.078
27.97
0.824
0.087
Ours
30.26
0.885
0.062
28.55
0.859
0.064
Table Quantitative comparison of novel view synthesis on the Deblur-NeRF dataset
Defocus Blur SSIM
Motion Blur
Method
PSNR 1
LPIPS 4
PSNR
SSIM 1
LPIPS $
De3DGS 21
23.71 24.22
0.747
0.110 0.095
26.61
0.822
0.108
Ours
0.768
27.14
0.835
0.096
23

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Blur GT
Blur GT BSSTNet
D2RF
De4DGS
BAGS
De3DGS
Ours
Sharp GT
Figure 13: Visual comparison of deblurring, Our method enables the synthesis of high-quality deblurring results for videos with defocus blur (top) and motion blur (bottom).
Table 7: Quantitative comparison of deblurring on the D2RF defocus blur dataset DyBluRF motion blur dataset 447
32 and the
Defocus Blur
Motion Blur SSIM
Method
PSNR 1
SSIM f
LPIPS
PSNR t
LPIPS
BSSTNet 66
33.54
0.961
0.039
33.71
0.965
0.030
D2RF B2
34.33
0.976
0.028
32.14
0.949
0.049
De4DGS 58
32.92
0.953
0,044
33.27
0.958
0.035
BAGS 41
30.69
0.940
0.084
30.55
0.935
0.092
De3DGS 21
30.36
0.941
0.090
29.84
0.924
0.105
Ours
34.85
0.977
0.027
33.45
0.960
0.036
C Additional Ablation Results
C.1 Ablation on BP-Net
We conduct an ablation study to evaluate the contribution of BP-Net. Specifically, we compare three different blur modeling methods: (i) the motion blur and defocus blur modeling method used in De3DGS (2 (w/ blur modeling De3DGS (2T), (ii) the motion blur modeling method in De4DGS (88 (w/ blur modeling in Deblur4DGS (58). and (iii) the defocus blur modeling method in
D2RF 32 (w/ blur modeling D2RF (32). We report the quantitative results Table S where
we can see that our method with the proposed BP-Net produces better results than these alternatives, demonstrating the effectiveness of the BP-Net.
Table 8: Effect of BP-Net.
Defocus Blur SSIM
Motion Blur
Method
PSNR
LPIPS
PSNR
SSIM
LPIPS
w/ blur modeling in De3DGS 21
28.31
0.812
0.098
26.05
0.823
0.118
w/ blur modeling De4DGS [58)
28.63
0.829
0.094
26.74
0.859
0.060
w/ blur modeling in D2RF [32
28.96
0,832
0.094
26.30
0.825
0.109
Ours with BP-Net
29.39
0.859
0.078
27.01
0.876
0.056
C.2 Ablation on Blur Kernel Size
Table 9 further perform quantitative evaluation on how different blur kernel sizes (denoted as K) affect the performance of our method. As shown, larger blur kernel helps to obtain better results. However, this trend becomes less obvious when K larger than 9. To balance the performance and the computational cost, we thus choose K 9 our default choice.

---

Table 9: Effect of varying blur kernel size K. The numerical results of defocus blur obtained on the Gate and Dock scenes of the D2RF dataset, and the numerical results of motion blur are obtained on the Skating and Man scenes of the DyBluRF dataset.
Defocus Blur
Motion Blur
Blur kernel size
PSNRT
SSIM
LPIPS
PSNRT
SSIMT
LPIPS!
K=5
27.84
0.817
0.098
29.53
0.906
0.092
K=7
28.12
0.836
0.074
29.79
0.913
0.067
K=9
28.29
0.842
0.067
30.01
0.921
0.052
K=1l
28.30
0.843
0.066
30.02
0.920
0.050
K=13
28.31
0.842
0.067
30.04
0.922
0.051
Kernels
Blur GT
Blur Intensity
Figure 14: Visualization of the blur kernel and blur intensity predicted by BP-Net. Note that the top row shows image with defocus blur, while the bottom row shows an image with motion blur. In the blur ground truth (GT) image, blue markers indicate pixels with almost no blur, green markers denote pixels with mild blur, and red markers represent pixels with severe blur. A higher blur intensity value corresponds to a more heavily blurred area. Clearly, BP-Net can accurately predict blur regions in images with different types of blur and estimate the corresponding blur kernels at pixel locations with varying blur levels.
D More Analysis on BP-Net
Our method represents image blurring effects using two components: a blur kernel ht and blur intensity m, illustrated in Figure [4 The blur intensity effectively emphasizes the spatial regions affected by blur within each training image. Moreover, the type of blur can be intuitively inferred from the estimated kernels. Specifically, kernels corresponding to motion blur capture structured trajectories that reflect the camera's 's movement, while those associated with defocus blur present Gaussian-shaped patterns that vary with the distance of the pixel from the focal plane.
To evaluate the accuracy of the blur kernel & predicted by BP-Net, we compare the ground truth blur kernel with the blur kernel predicted by BP-Net on a blurry monocular video dataset with ground truth blur kernel. To this end, construet as D2RF-v3 and DyBluRF-v3, by randomly sampling two global Gaussian and linear distribution blur kernels of size 9 and then respectively applying them the ground truth sharp images in D2RF 32 and DyBluRF 47 to obtain the corresponding blurry images. With the two datasets, we quantitatively compare our estimated blur kernels and the ground truth blur kernels using PSNR and KI. divergence as metrics. Table 10 and Figure 1.5 present the comparison results. Clearly, our estimated blur kernels are highly similar the ground truth blur kernels in numerical metries, manifesting the effectiveness of the BP-Net in predicting different types of blur.
datasets with ground truth blur kernels, referred to
25

---

GT
Kemel
Estimated
Kemel
GT
Kernel
Estimated Kernel
Blur GT
Ours
Sharp GT
Figure 15: Visual comparison of estimated kernel the D2RF-v3 defocus blur dataset and the DyBluRF-v3 motion blur dataset. Note that the top row shows an image with defocus blur, while the bottom shows image with motion blur. Clearly, BP-Net can accurately estimate blur kernels from various types of blurry images and thus recover sharp images.
Table 10: Comparison of ground truth kernels and estimated kernels.
D2RF-v3
DyBluRF-vs
PSNR
32.516
29.941
KL Div.
0.214
0.247
E Robustness to Preprocessing and Segmentation Errors
In the Table []] we quantitatively evaluate how errors from external preprocessing steps affect the robustness of our method. To simulate errors from depth estimation, we randomly scale and shift the estimated depth maps within the range of [0.8, 1.2] and [-20, 20]. respectively. To simulate errors from 2D point tracking and SAM, we randomly shift the 2D tracking points within the range of [-30, 30), and randomly add delete five 25 X 25 mask regions towards the mask predicted by SAM. As shown, our results produced with the artificially perturbed depth, tracking points, and motion mask are comparable those produced with the originally estimated depth, tracking points, and motion mask, indicating that our method has some tolerance to errors from external preprocessing steps.
Table 1I: Analysis on the impact of errors from external preprocessing steps.
Defocus Blur SSIM
Motion Blur SSIM
Method
PSNRT
LPIPS
PSNR1
LPIPS
Ours w/ depth perturbation
29.19
0.844
0.088
26.79
0.862
0.074
Ours w/ tracking perturbation
29.21
0.854
0.084
26.86
0.874
0.060
Ours w/ mask perturbation
29.25
0.854
0.085
26.74
0.865
0.067
Ours
29.39
0.859
0.078
27.01
0.876
0.056
26


Reviews:
[{"review_id": "mQnRENwoiL", "reviewer": "Reviewer_XJhL", "summary": "This paper introduces a framework for dynamic Gaussian Splatting capable of synthesizing high-quality, sharp novel views from monocular videos affected by either defocus or motion blur. The approach builds on blur kernel-based convolution to handle both blur types within a unified process. Key contributions include a blur prediction network (BP-Net) that predicts per-pixel blur kernels and blur intensity, a blur-aware sparsity constraint to improve kernel reliability, and a dynamic Gaussian densification strategy to address incomplete scene regions. Additionally, the method renders and constrains adjacent unseen views to prevent geometry from overfitting. Extensive experiments on benchmark datasets demonstrate superior quantitative and qualitative performance compared to recent baselines for both defocus and motion-blurred videos.", "questions": "Please refer to the weaknesses part. I will adjust my ratings according to the authors' response.", "limitations": "yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "## Strengths\n- This work is the first 4DGS reconstruction method that combines motion and defocus blur handling into a single end-to-end pipeline\n- The architecture and framework are clearly described\n- The evaluation is comprehensive, covering both defocus and motion blur, across many SOTA methods\n\n## Weaknesses\n- Minimal Improvement: The numerical increase in the quantitative evaluation seems small, and motion blur still exists in most of the provided videos\n- External Reliance on Preprocessing and Segmentation: The initialization for both static and dynamic Gaussians depends heavily on external methods for depth estimation, 2D point tracking, and SAM. There is no analysis provided on how errors from these preprocessing steps propagate into the final results, nor is the robustness to their failure modes systematically evaluated.\n- Effectiveness of the Unified Blur Model: While the unification of defocus and motion blur via a kernel-based convolution is mathematically plausible, there is relatively little discussion of cases where these two blur types may not be accurately captured by the same convolutional abstraction—especially in the presence of spatially and temporally varying blurs with complex overlap. To this end, the unified model seems to be a naive approach - will it be more effective and easier to train if the motion and defocus blur are estimated separately (e.g. deblurring 3d gaussian splatting, ECCV 2024)? I would suggest the authors can first evaluate on some static blurry datasets.\n- Limited Analysis of Computational Costs: Table 1 reports parameters, training time, and FPS for a single configuration, but there is no detailed analysis of training/inference scalability, memory demands, or sensitivity to scene complexity/sequence length. Furthermore, there is no breakdown of where computational bottlenecks occur (e.g., BP-Net, rasterization, kernel prediction, Gaussian densification).\n- Lacks Comparison with MoBGS: Motion Deblurring Dynamic 3D Gaussian Splatting for Blurry Monocular Video", "was_revised": true, "final_justification": "The authors' thorough rebuttal addressed most of my concerns. In the rebuttal, the authors demonstrated the robustness and performance, validated the unified model, and clarified the limitations and computational complexity.", "rebuttal": "**_(Q1)  The numerical increase in the quantitative evaluation seems small._**\n\nAs shown in Table 1 in the paper, our numerical performance improvement over current SOTA method (Deblur4DGS) is evident on the PSNR$\\uparrow$/SSIM$\\uparrow$/LPIPS$\\downarrow$ metrics (defocus blur: 29.39/0.859/0.078 vs. 28.49/0.791/0.154, motion blur: 27.01/0.876/0.056 vs. 26.62/0.871/0.059), especially considering that our method has significantly fewer parameters than Deblur4DGS (192.2M vs. 754.6M). Moreover, we would like to point out that, our work makes the first attempt towards developing a unified framework for dynamic Gaussian Splatting from both defocused and motion-blurred monocular videos, which we believe is more important than the performance improvement. \n\n**_(Q2) Motion blur still exists in the provided videos._**\n\nDespite the advantage over the compared methods, some of our provided videos do suffer from weak motion blur, because it remains a challenge for our method as well as other SOTA methods to achieve perfect dynamic object reconstruction for complex cases, which is also illustrated in our limitation analysis in Figure 8 in the paper.\n\n**_(Q3) More analysis on how errors from external preprocessing steps (depth estimation, 2D point tracking, and SAM) affect the results._**\n\nThank you for the suggestion. In the table below, we quantitatively evaluate how errors from external preprocessing steps affect the robustness of our method. To simulate errors from depth estimation, we randomly scale and shift the estimated depth maps within the range of [0.8, 1.2] and [-20, 20], respectively. To simulate errors from 2D point tracking and SAM, we randomly shift the 2D tracking points within the range of [-30, 30], and randomly add or delete five 25 X 25 mask regions towards the mask predicted by SAM. As shown, our results produced with the artificially perturbed depth, tracking points, and motion mask are comparable to those produced with the originally estimated depth, tracking points, and motion mask, indicating that our method has some tolerance to errors from external preprocessing steps. \n|Method||Defocus blur (D2RF [1])||||Motion blur (DyBluRF [2])||\n|-----|:-----:|:-----:|:----:|:-----:|:----:|:----:|:----:|\n||PSNR↑|SSIM↑|LPIPS↓||PSNR↑|SSIM↑|LPIPS↓|\n|Ours w/ depth perturbation|29.19|0.844|0.088||26.79|0.862|0.074|\n|Ours w/ tracking perturbation|29.21|0.854|0.084||26.86|0.874|0.060|\n|Ours w/ mask perturbation|29.25|0.854|0.085||26.74|0.865|0.067|\n|Ours|**29.39**|**0.859**|**0.078**||**27.01**|**0.876**|**0.056**|\n\n**_(Q4) More discussion on cases where defocus and motion blur may not be accurately captured by the same convolutional abstraction._**\n\nFor complex cases that involve fast-moving objects in defocused background, the convolutional abstraction may struggle to accurately model defocus and motion blur. We will add some examples of such cases to the supplementary material. \n\n**_(Q5) Will it be more effective and easier to train if the motion and defocus blur are estimated separately (e.g. deblurring 3d gaussian splatting, ECCV 2024)?_**\n\nWe have followed your suggestion to train our method by separately estimating motion and defocus blur as done in the mentioned ECCV 2024 paper [3]. As shown in the table below, adopting the separate modeling strategy results in clear performance drop on all the three metrics, manifesting the effectiveness of our unified modeling of motion and defocus blur. We will include the table in the paper. \n\n|Method||Defocus blur (D2RF [1])||||||Motion blur (DyBluRF [2])||\n|------|:-----:|:-------:|:---:|:-----:|:---------:|:------:|:------:|:------:|:------:|\n||PSNR↑|SSIM↑|LPIPS↓||||PSNR↑|SSIM↑|LPIPS↓|\n|Ours w/ separate modeling|28.31|0.812|0.098||||26.05|0.823|0.118|\n|Ours|**29.39**|**0.859**|**0.078**||||**27.01**|**0.876**|**0.056**|\n\n**_(Q6) More evaluation on some static blurry datasets._**\n\nThank you for the suggestion. In the table below, we compare our method with a recent method (Deblurring 3D Gaussian Splatting [3], ECCV 2024) tailored for static blurry images on the Deblur-NeRF dataset [4]. As shown, our method consistently outperforms the compared method (referred to as Deblurring-GS) on both motion and defocus blur, validating the effectiveness of our method in dealing with static blurry images. We will add the table to the paper. \n\n|Method||Defocus blur (Deblur-NeRF [4])||||Motion blur (Deblur-NeRF [4])||\n|---|---|:---:|:---:|:---:|:---:|:---:|:---:|\n||PSNR↑|SSIM↑|LPIPS↓||PSNR↑|SSIM↑|LPIPS↓|\n|Deblurring-GS|23.71|0.747|0.110||26.61|0.822|0.108|\n|Ours|**24.22**|**0.768**|**0.095**||**27.14**|**0.835**|**0.096**|\n\n**_(Q7) More analysis on computational cost and memory demands._**\n\nOur model requires an average of 6GB GPU memory for both training and inference. During training, the BP-Net dominates the computational cost with a ratio of 40%, while the dynamic Gaussian deformation and rasterization respectively occupy 25% and 15% of the computational cost, and the rest of 10% is for loss computation. During inference, the computational cost only comes from dynamic Gaussian deformation and rasterization, with the former at 70% and the latter at 30%. \n\n**_(Q8) Comparison with MoBGS._**\n\nThank you for the comment. Since the implementation of MoBGS [5] is not yet publicly available, we compare our method with MoBGS by training on the same motion blur dataset as MoBGS in terms of PSNR$\\uparrow$/SSIM$\\uparrow$/LPIPS$\\downarrow$ on the full region. The comparative results between our method and MoBGS are 28.61/0.942/0.059 vs. 28.70/0.945/0.051 (the results of MoBGS are from their paper), showing that our method can produce comparable results to MoBGS on motion blur, even though MoBGS is specially designed for motion blur while our method can effectively handle both motion and defocus blur.\n\n\n**_References_**\n\n[1] Luo, X., Sun, H., Peng, J., & Cao, Z. Dynamic neural radiance field from defocused monocular video. ECCV 2024.\n\n[2] Sun, H., Li, X., Shen, L., Ye, X., Xian, K., & Cao, Z. Dyblurf: Dynamic neural radiance fields from blurry monocular video. CVPR 2024.\n\n[3] Lee, B., Lee, H., Sun, X., Ali, U., & Park, E. Deblurring 3d gaussian splatting. ECCV 2024.\n\n[4] Ma, L., Li, X., Liao, J., Zhang, Q., Wang, X., Wang, J., & Sander, P.V. Deblur-nerf: Neural radiance fields from blurry images. CVPR 2022.\n\n[5] Bui, M. Q. V., Park, J., Bello, J. L. G., Moon, J., Oh, J., & Kim, M. MoBGS: Motion Deblurring Dynamic 3D Gaussian Splatting for Blurry Monocular Video. arXiv 2025."}, {"review_id": "hpiyo02mjr", "reviewer": "Reviewer_ocL4", "summary": "The paper addresses the challenge of building dynamic Gaussian splatting representations from monocular videos degraded by two types of blurs: motion blurs and defocus blurs. While previous methods focus solely on one type of blur, the authors present a framework that handles problems from both types of blurs simultaneously. This is achieved by a blur prediction network (BP-Net) for synthesizing blurs and a densification strategy for dynamic Gaussian splatting, which fills in the missing parts between dynamic Gaussians. In the experiment section, the paper shows outperforming metrics in both benchmarks for separate blur types.", "questions": "Regarding the Efficacy of the Proposal\n1. How well is BP-Net trained for different types of blurs? Comparing generated and estimated kernels can clarify the importance of the suggested BP-Net in this framework.\n2. Why did the authors ablate on the shortcut connections of the BP-Net in Table 3, rather than the BP-Net itself, if the authors wanted to demonstrate the BP-Net itself as their contributions, as in Line 43? Either more clearly stating the exact contributions or restructuring the ablation studies section to better align with the contributions will help.\n3. What if the two types of blurs occur jointly? Can the proposed method also handle this type of degradation?\n\nRegarding Presentation\n1. Please clarify which part is the novel contribution of the authors in section 3. As shown in Figure 2, the overall pipeline involves several pre-built solutions, including SAM, Depth-Anything, and TAPIR. Therefore, it seems crucial to distinguish the paper’s own contributions from those in the manuscript. This version, for example, in lines 76-113, mixes the off-the-shelf and the developed methods in the text. It is challenging to identify which parts constitute the novel contributions.\n2. How frequently does the densification take place (What is N_d)? Showing exact figures for the hyperparameters can boost the clarity of the manuscript. For example, in Line 85 and Line 103, the number of motion bases is only denoted as an abstract symbol N_d. In Line 171, we have N_u, whose value is also not specified. It will not hurt the generality of the method to provide a rough scale of the variables used in the method.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. The paper is the first attempt to jointly solve defocus deblurring and dynamic scene deblurring in the monocular video-based dynamic Gaussian splatting construction.\n2. The effectiveness of the proposal is backed by a sufficient number of experiments, increasing the credibility of the work.\n3. The reported scores in the paper show outstanding performance in both types of blurs as addressed.\n\nWeaknesses\n1. The technical contributions are unclear in the manuscript. In Section 3, for example, the claimed contribution of dynamic Gaussian densification is mixed with other off-the-shelf tools like SAM, Depth-Anything, and TAPIR. It is difficult to discern which parts are the authors' original contributions.\n2. Certain implementation details are omitted in the main manuscript. Hyperparameters like N_d, N_u remain as abstract symbols, making it more challenging to capture the details of how this method works in reality.\n3. Ablation studies in Section 4.2 and the claimed contributions in Section 1 do not align. For example, it is unclear why BP-Net is ablated by only its shortcut connection if the usage of the network itself is claimed as a novelty.\n4. Citations and references to previous methods and datasets are overly abstract. For example, Table 2 presents a crucial previous method simply as a number [62], requiring readers to click the link and search for the method it references. Another example is that all the datasets are presented in a “defocus blur dataset [30]” or “motion blur dataset [55]”.\n\nWeaknesses are mainly about the presentation and lack of clarity in the claimed contributions, which I believe to be significant for a technical report. For suggestions and minor weaknesses, please refer to the Questions section.", "was_revised": true, "final_justification": "The authors' rebuttal addressed some of my essential concerns. I believe that the presentation and clarity will be improved in the final version.", "rebuttal": "**_(Q1) Unclear clarification of technical contributions._**\n\nOur technical contributions are twofold: (i) we present the first unified framework for dynamic Gaussian Splatting from both defocused and motion-blurred monocular videos, (ii) we develop an effective blur prediction network and introduce a dynamic Gaussian densification as well as an unseen view combined scene optimization scheme. Note, we do not consider the off-the-shelf tools like SAM, Depth-Anything, and TAPIR as our contributions. We will clarify this in the paper to avoid possible misunderstandings. \n\n**_(Q2) Implementation details are omitted in the main manuscript._**\n\nThank you for pointing this out. As described in the Paper Checklist, we provide the full implementation details in the supplementary material. We will move the implementation details to the paper to facilitate reading and understanding.\n\n**_(Q3) Ablation study of the BP-Net._**\n\nThank you for the constructive comment. In the table below, we conduct ablation study on the BP-Net, where we compare our method with two alternatives that replace BP-Net with the motion blur modeling approach introduced in Deblur4DGS and the defocus blur modeling method introduced in D2RF, respectively. As can be seen, our method with the proposed BP-Net produces better results than the other two alternatives, demonstrating the effectiveness of the BP-Net. We will include the ablation study in the paper. \n\n|Method||Defocus blur (D2RF [1])||||Motion blur (DyBluRF [2])||\n|-----|:--------:|:-----:|:-----:|:----:|:-----:|:----:|:----:|\n||PSNR↑|SSIM↑|LPIPS↓||PSNR↑|SSIM↑|LPIPS↓|\n|Ours w/ motion blur in Deblur4DGS|28.63|0.829|0.094||26.74|0.859|0.060|\n|Ours w/ defocus blur in D2RF|28.96|0.832|0.094||26.30|0.825|0.109|\n|Ours with BP-Net|**29.39**|**0.859**|**0.078**||**27.01**|**0.876**|**0.056**|\n\n**_(Q4) Citations and references are overly abstract._**\n\nWe will improve the citations and references according to the comment. \n\n**_(Q5) Comparing generated and estimated kernels to examine the effectiveness of BP-Net in predicting different types of blur._**\n\nThanks for the comment. In the table below, we conduct comparison between generated and estimated kernels. To this end, we construct two datasets with ground truth blur kernels, referred to as D2RF-v2 and DyBluRF-v2, by randomly sampling two global Gaussian and linear distribution blur kernels of size 9 x 9 and then respectively applying them to the ground truth sharp images in D2RF [1] and DyBluRF [2] to obtain the corresponding blurry images. With the two datasets, we quantitatively compare our estimated blur kernels and the ground truth blur kernels using PSNR and KL divergence as metrics. As shown below, our estimated blur kernels are highly similar to the ground truth blur kernels in numerical metrics, manifesting the effectiveness of the BP-Net in predicting different types of blur. We will add the table and also visual comparison of blur kernels to the supplementary material. \n\n|       |||  D2RF-v2  || | DyBluRF-v2 |\n| ---- | :-----: | :----: |:-----: |:----: |:-----: |:----: |\n| PSNR↑    ||| 32.516|| | 29.941 |\n| KL Div.↓ ||| 0.214 | ||0.247  |\n\n**_(Q6) What if the two types of blurs occur jointly? Can the proposed method also handle this type of degradation?_**\n\nThank you for the insightful comment. In the table below, we evaluate our method on two datasets (DyBluRF-v3 and D2RF-v3) with both motion and defocus blur occurring simultaneously. Note, we obtain the DyBluRF-v3 dataset by applying depth-of-field (DoF) rendering technique in Bokehme [3] to the original DyBluRF dataset [2] to simulate defocus blur, and create the D2RF-v3 dataset with motion blur by processing the original D2RF dataset [1] using the motion blur generation method in Davanet [4]. As shown, our method clearly outperforms all the compared methods on the two datasets, verifying the advantage of our method in handling cases with motion and defocus blur occurring jointly.\n\n|Method||D2RF-v3||||DyBluRF-v3||\n|-------|:-----:|:-----:|:-----:|:-----:|:------:|:-----:|:-----:|\n||PSNR↑|SSIM↑|LPIPS↓||PSNR↑|SSIM↑|LPIPS↓|\n|D3DGS|23.66|0.739|0.257||21.75|0.655|0.289|\n|SoM|29.04|0.820|0.094||27.28|0.791|0.103|\n|D2RF|27.82|0.795|0.132||25.89|0.722|0.133|\n|DyBluRF|27.30|0.771|0.150||26.54|0.753|0.112|\n|De4DGS|29.74|0.856|0.078||27.97|0.824|0.087|\n|Ours|**30.26**|**0.885**|**0.062**||**28.55**|**0.859**|**0.064**|\n\n**_References_**\n\n[1] Luo, X., Sun, H., Peng, J., & Cao, Z. Dynamic neural radiance field from defocused monocular video. ECCV 2024.\n\n[2] Sun, H., Li, X., Shen, L., Ye, X., Xian, K., & Cao, Z. Dyblurf: Dynamic neural radiance fields from blurry monocular video. CVPR 2024.\n\n[3] Peng, J., Cao, Z., Luo, X., Lu, H., Xian, K., & Zhang, J. Bokehme: When neural rendering meets classical rendering. CVPR 2022.\n\n[4] Zhou, S., Zhang, J., Zuo, W., Xie, H., Pan, J., & Ren, J. S. Davanet: Stereo deblurring with view aggregation. CVPR 2019."}, {"review_id": "Qgugu8ELm1", "reviewer": "Reviewer_qGyt", "summary": "This paper proposes a unified 3D dynamic Gaussian framework for novel view synthesis from monocular blurry videos, capable of simultaneously handling motion blur and defocus blur. The framework employs a blur kernel prediction network to predict per-pixel blur kernels, which are regularized through blur-aware sparsity constraints. Additionally, the framework introduces a dynamic Gaussian densification strategy to optimize unseen views.", "questions": "1. The methodology section presents a disorganized description of the overall framework, lacking clear explanations of the relationships between different modules. For example, Line 82, R20 does not clearly explain what the number 20 specifically represents; Formula 2 does not provide a clear explanation of how the dynamic Gaussian achieves scaling transformation; the specific meaning in Formula 3 is not explained. The description of the two modules in Figure 2 is insufficiently clear, such as how the Image, Depth, and Mask in the right figure are obtained, and how the Blur-aware Sparsity Constrain performs backpropagation.\n\n2. The description of the blur kernel estimation network is unclear: No explicit justification is provided for the chosen blur kernel size. The physical meaning of \"blur intensity mx\" is not defined. The paper fails to explain why blur-aware sparsity constraints can stabilize mx.\n\n3. Regarding the training strategy: How is \"restrict the selection to unseen views close to the training views\" implemented?\n\n4. Experimental setup issues: Key hyperparameter values are not specified. The dataset used was not proposed by Deblur4DGS (incorrect citation).\n\n5. Line 3: \"diference\" is misspelled (should be \"difference\").\n\n6. Figure 2: We recommend using dashed lines to represent the Gradient Flow for better visual distinction.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: This paper presents an extension of blur kernel estimation from static scene novel view synthesis to the more challenging monocular video setting, proposing a unified framework that simultaneously addresses both motion and defocus blur. \n\nWeaknesses: The technical presentation suffers from significant clarity issues - the methodological framework lacks coherent explanation, with ambiguous descriptions and unclear mathematical derivations throughout the paper. Furthermore, the experimental section remains inadequately detailed, particularly regarding crucial implementation specifics like hyperparameter configurations, substantially limiting reproducibility and evaluation rigor. See 'questions' for details.", "was_revised": true, "final_justification": "The authors have addressed my concerns, and I have updated the rating.", "rebuttal": "**_(Q1) Line 82, what the number 20 specifically represents in R20._**\n\nThe number 20 denotes the total number of SE(3) motion bases. We will clarify this in the paper. \n\n**_(Q2) How the dynamic Gaussian achieves scaling transformation in Formula 2._**\n\nNote, we do not perform scaling transformation of dynamic Gaussian in Formula 2, as we aim to achieve dynamic Gaussian deformation in Formula 2. Therefore, we only adjust the pose parameters (position and rotation).\n\n**_(Q3) The meaning in Formula 3 is not explained._**\n\nThe goal of Formula 3 is to obtain the center of the densified dynamic Gaussian by performing depth reprojection of each pixel in a frame to its corresponding 3D point location. \n\n**_(Q4) How the Image, Depth, and Mask in the right part of Figure 2 obtained, and how the Blur-aware Sparsity Constrain performs backpropagation._**\n\nThe mentioned image, depth, and mask are obtained by rasterizing both dynamic and static Gaussians. During training, the gradient of blur-aware sparsity constraint flows to BP-Net through the learnable blur kernel, thereby allowing backpropagation. \n\n**_(Q5) No explicit justification is provided for the chosen blur kernel size._**\n\nIn addition to the visual justification in Figure 7, we in the table below further perform quantitative evaluation on how different blur kernel sizes (denoted as K) affect the performance of our method. As shown, a larger blur kernel helps to obtain better results. However, this trend becomes less obvious when K is larger than 9. To balance the performance and the computational cost, we thus choose K=9 as our default choice.  We will add the table to the supplementary material. \n\n|Blur kernel size||Defocus blur (D2RF [1])||||Motion Blur (DyBluRF [2])||\n|:------------:|:---:|:----------:|:---:|:---:|:---------:|:---:|:---:|\n||PSNR↑|SSIM↑|LPIPS↓||PSNR↑|SSIM↑|LPIPS↓|\n|K=5|27.84|0.817|0.098||29.53|0.906|0.092|\n|K=7|28.12|0.836|0.074||29.79|0.913|0.067|\n|K=9|28.29|0.842|0.067||30.01|0.921|0.052|\n|K=11|28.30|0.843|0.066||30.02|0.920|0.050|\n|K=13|28.31|0.842|0.067||30.04|0.922|0.051|\n\n**_(Q6)  The physical meaning of \"blur intensity mx\" is not defined._**\n\nThe physical meaning of $m_x$ is the degree of blur of a pixel $x$, and a larger $m_x$ indicates a blurrier pixel. \n\n**_(Q7) The paper fails to explain why blur-aware sparsity constraints can stabilize mx._**\n\nThank you for careful review. In fact, what we intend to express in Line 162-163 is that we adopt the blur-aware sparsity constraint $L_{spa}$ for training only when the blur intensity $m_x$ has been trained for a number of $N_{spa}$ iterations to be stable, rather than claiming that the constraint can stabilize $m_x$. We will revise the paper to clarify this. \n\n**_(Q8) How is \"restrict the selection to unseen views close to the training views\" implemented?_**\n\nWe implement this by generating two types of unseen views: (i) parallel-unseen views: generated by interpolating between adjacent training views along the camera trajectory, (ii) perpendicular-unseen views: generated by first computing a local perpendicular direction to the camera trajectory and then perturbing the training view’s camera center along this perpendicular direction by a distance of [0.5, 1] (normalized units). We will clarify this in the paper.\n\n**_(Q9) Key hyperparameter values are not specified._**\n\nIn fact, we give all implementation details in the supplementary material, including the hyperparameter values. We will move the implementation details to the paper. \n\n**_(Q10) Incorrect citation of the dataset._**\n \nThank you for careful checking. We will address the issue in the revised paper. \n\n**_(Q11) Line 3: \"diference\" is misspelled (should be \"difference\")._**\n\nWe will correct this typo. \n\n**_(Q12) Using dashed lines in Figure 2 to represent the Gradient Flow._**\n\nWe will update Figure 2 according to your suggestion.\n\n**_References_**\n\n[1] Luo, X., Sun, H., Peng, J., & Cao, Z. Dynamic neural radiance field from defocused monocular video. ECCV 2024.\n\n[2] Sun, H., Li, X., Shen, L., Ye, X., Xian, K., & Cao, Z. Dyblurf: Dynamic neural radiance fields from blurry monocular video. CVPR 2024."}, {"review_id": "QxUvfN0sb6", "reviewer": "Reviewer_Uuup", "summary": "The paper proposes a method that jointly models defocus and motion blur using a neural network that predicts per-pixel blur kernels and intensities. This unified blur modeling guides the optimization of dynamic and static Gaussians for sharper reconstruction from blurry monocular videos.", "questions": "1. Could the authors clarify how adding an intensity output to the blur kernel network alone prevents it from deforming jointly with the 3DGS representation?\n\n2. Why not consider using a pre-trained large model to simulate blur instead of training a network together with 3DGS, which might avoid the deformation issue altogether?\n\n3. A straightforward idea would be to first apply a network to generate the blurred image, then feed the clear image to 3DGS for reconstruction. Could the authors elaborate on the main advantages of the proposed coupled approach compared to such a decoupled pipeline?\n\nIf the authors can provide clear and convincing answers to these questions, I am willing to consider increasing my evaluation score.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n1. The paper is very well written, with a clear and progressive structure that makes it easy to follow.\n2. The experiments are reasonably comprehensive, including both main results and ablation studies.\n3. The work focuses sharply on the core challenge of jointly handling motion blur and defocus blur, which is timely and relevant.\n\n**Weaknesses:**\n1. The interaction between different modules could be stronger — the current design resembles a standard 3DGS pipeline followed by a separate blur modeling network, rather than a deeply integrated solution.\n2. Some methodological details are under-explained. For instance, while adding an intensity output to the blur kernel network is a sensible idea, it is unclear how this modification alone prevents the network from deforming jointly with 3DGS — since it remains using a network to generate blur.", "was_revised": true, "final_justification": "My initial concerns about novelty and justification have been addressed, and I’ve raised my score.\n\nKey reasons include:\n\n1. The authors convincingly argue that joint optimization is better than a simple two-stage pipeline, as it preserves critical information.\n\n2. Their choice to avoid large models is well-justified", "rebuttal": "**_(Q1) The current design resembles a standard 3DGS pipeline followed by a separate blur modeling network, rather than a deeply integrated solution._**\n\nIndeed, our framework is not highly integrated in architecture. This is mainly because we adopt kernel-based post-processing to achieve unified modeling of both motion and defocus blur. Though our method is not a deeply integrated solution, it still outperforms previous methods, demonstrating its effectiveness. \n\n**_(Q2) Clarify how adding an intensity output to the blur kernel network alone prevents it from deforming jointly with the 3DGS representation._**\n\nBy adopting an intensity output, we are able to identify the blurry and sharp regions in the blurry input image, with which we can enforce the blur kernel network to focus on the blurry regions and also use the sharp regions to constrain the Gaussian rendered image, thereby avoiding deforming jointly with the 3DGS representation. \n\n**_(Q3) Why not consider using a pre-trained large model to simulate blur instead of training a network together with 3DGS, which might avoid the deformation issue altogether?_**\n\nThank you for this inspiring comment. We agree that this is a promising direction to avoid the deformation issue. However, existing pre-trained large models basically recover sharp images from blurry inputs in an end-to-end manner without explicitly modeling blur. Although we have tried our best to fine-tune some pre-trained models (Restormer [1] and DiffBIR [2]) to estimate the blur kernel, we failed to obtain satisfactory results within the rebuttal period. We will keep working on this direction in our future work.  \n\n**_(Q4) A straightforward idea would be to first apply a network to generate the blurred image, then feed the clear image to 3DGS for reconstruction. Could the authors elaborate on the main advantages of the proposed coupled approach compared to such a decoupled pipeline?_**\n\nThe main advantage of our approach is that our method can obtain spatiotemporally consistent scene representation (see Table 2 and Figure 4 in the paper, as well as Figure 2 in the supplementary material for comparison), which is hardly achieved by the decoupled pipeline. The reason is that the decoupled pipeline performs deblurring in 2D image space without considering the 3D information of the scene, and thus there usually exists noticeable spatiotemporal inconsistencies between the deblurred frames, which in turn cause spatiotemporal inconsistency in 3DGS reconstructed scene.\n\n**_References_**\n\n[1] Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., & Yang, M. H. Restormer: Efficient Transformer for High-Resolution Image Restoration. CVPR 2022.\n\n[2] Lin, X., He, J., Chen, Z., Lyu, Z., Dai, B., Yu, F., ... & Dong, C. DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior. ECCV 2024."}]

Author rebuttal:

Meta-review: The paper proposes a deblurring method based on Gaussian splatting that simultaneously addresses both defocus and motion blur. Although the paper initially received three negative ratings, the rebuttal effectively clarified key details and convinced the reviewers with additional qualitative results. All reviewers acknowledge the novelty on the joint formulation framework, comprehensive evaluations, and improved clarity. The AC concurs with the reviewers and recommends clear acceptance. The authors are encouraged to improve the paper presentation according to the suggestions from reviewers ocL4, qGyt, and Uuup when preparing the camera ready version.
Decision: Accept (poster)