Title: Dynamic Gaussian Splatting from Defocused  and Motion-blurred Monocular Videos
Venue: NeurIPS 2025
Accepted: yes
Keywords: Novel View Synthesis

Dynamic Gaussian Splatting

Deblur
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-15"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: 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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\nRegarding 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.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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\n\nQUESTIONS:\nPlease refer to the weaknesses part. I will adjust my ratings according to the authors' response.\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}