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arxiv: 2606.01315 · v1 · pith:LJ6X3D52new · submitted 2026-05-31 · 💻 cs.CV

DeblurNVS: Geometric Latent Diffusion for Novel View Synthesis from Sparse Motion-Blurred Images

Changyue Shi , Wangbo Yu , Chaoran Feng , Li Yuan This is my paper

Pith reviewed 2026-06-28 17:26 UTC · model grok-4.3

classification 💻 cs.CV
keywords novel view synthesismotion blurlatent diffusiongeometric representationssparse viewsdeblurringneural rendering3D reconstruction
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The pith

DeblurNVS restores geometric representations from sparse motion-blurred images via latent diffusion to synthesize sharp novel views without per-scene optimization.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes a framework for novel view synthesis that starts from motion-blurred and sparse input images rather than clean ones. It first uses latent diffusion to recover intermediate geometric representations that preserve structure and cross-view correspondences, then feeds those plus target camera poses into a synthesis stage that outputs a sharp RGB image. This approach is trained once on a large dataset of synthesized blur and runs directly at inference time. A reader would care because real captures frequently contain blur from camera motion or long exposures, breaking the clean-image assumption of most current NVS pipelines and forcing slow per-scene fitting in prior blur-aware work.

Core claim

DeblurNVS restores the intermediate geometric representations needed for multi-view reasoning from blurred inputs, enabling the synthesis of high-fidelity novel views directly from sparse motion-blurred images without requiring per-scene optimization. The restored representations are combined with target camera information to synthesize the target-view representation and reconstruct a sharp RGB novel view.

What carries the argument

Geometric latent diffusion that restores intermediate geometric representations from motion-blurred observations to recover structure and correspondence cues.

If this is right

  • The method produces perceptually sharper and structurally more stable novel views than existing baselines on synthetic motion-blur benchmarks.
  • It generalizes from the synthetic training distribution to real motion-blurred scenes.
  • It enables efficient sparse-view synthesis by removing the need for costly per-scene optimization at test time.
  • A large-scale motion-blurred NVS dataset constructed via interpolation-based finite-exposure blur synthesis supports the training.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If geometric restoration proves robust, the same latent-diffusion step could be adapted to other common degradations such as noise or low-light conditions that also weaken multi-view correspondences.
  • The separation of restoration and synthesis stages suggests that future work could swap in different diffusion backbones or geometry encoders without retraining the entire pipeline.
  • Success on real scenes would imply that large synthetic blur datasets can substitute for hard-to-collect paired real blurred-and-sharp multi-view data in practical capture settings.

Load-bearing premise

Restoring intermediate geometric representations from blurred inputs is sufficient to recover reliable structure and correspondence cues needed for accurate multi-view synthesis.

What would settle it

A side-by-side evaluation on real motion-blurred scenes where novel views from DeblurNVS are compared against both clean-image baselines and ground-truth sharp views of the same scenes, checking whether structural stability and sharpness improve measurably.

Figures

Figures reproduced from arXiv: 2606.01315 by Changyue Shi, Chaoran Feng, Li Yuan, Wangbo Yu.

Figure 1
Figure 1. Figure 1: We propose DeblurNVS, a novel framework that synthesizes sharp [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of DL3DV-10K-Blur. We show examples of preprocessed sharp–blur image pairs from our dataset. pose-conditioned diffusion models for view generation, as in GeNVS [50], Zero-1-to-3 [51], ZeroNVS [52], and Reconfu￾sion [53]. More recent works introduce video diffusion priors to improve view consistency and camera controllability. In par￾ticular, ViewCrafter [3] combines coarse point-based geometr… view at source ↗
Figure 3
Figure 3. Figure 3: Pipeline of DeblurNVS. 1) We construct a large-scale motion-blurred dataset from DL3DV-10K via frame interpolation and temporal averaging. 2) Given motion-blurred inputs, DeblurNVS learns sharp multi-view latents by jointly adapting the DA3 backbone and multi-view diffusion. 3) Camera tokens are further introduced to guide novel-view latent synthesis, and a shared color decoder maps the restored and synthe… view at source ↗
Figure 4
Figure 4. Figure 4: 2D Deblur+GLD vs Ours. Left: blurred inputs, Uformer-restored inputs, and our restored inputs. Right: ground-truth novel view, Uformer+GLD result, and ours. Independent 2D deblurring remains limited and multi-view inconsistent, leading to degraded GLD synthesis, while our method produces more consistent restorations and sharper novel views. B. Main Results Quantitative Comparisons. We present quantitative … view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results on the DeblurNeRF-Blender dataset. Compared with baseline methods, our method reconstructs sharper details and achieves higher visual fidelity under motion-blurred inputs. faster than GLD because we simplify the diffusion framework by reducing the number of denoising steps and removing cascaded operations across DA3 feature levels. However, as a diffusion-based method, DeblurNVS is natu… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative results on the DeblurNeRF-Real dataset. Compared with baseline methods, our method reconstructs sharper details and achieves higher visual fidelity under motion-blurred inputs. Ground Truth Ours w/o DA3LoRA w/o Context Learning [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Ablation study on overall architecture. Removing LoRA adaptation from the DA3 backbone or discarding the first-stage context learning leads to degraded geometric structures. TABLE VI COMPARISON WITH THE RGB-SPACE CASCADED BASELINE. UFORMER+GLD FIRST DEBLURS THE INPUT VIEWS WITH UFORMER AND THEN PERFORMS NVS USING GLD. Method PSNR↑ SSIM↑ LPIPS↓ DISTS↓ FID↓ GLD 12.868 0.329 0.570 0.281 166.513 Uformer+GLD 12… view at source ↗
Figure 8
Figure 8. Figure 8: Ablation on hyperparameters of DL3DV-10K-Blur. Columns correspond to different averaging window sizes, while rows correspond to different frame interpolation rates. multi-view deblurring from novel view synthesis. We further construct DL3DV-10K-Blur to support large-scale end-to-end training. Experiments on synthetic and real-world benchmarks demonstrate that DeblurNVS achieves better perceptual quality an… view at source ↗
read the original abstract

Novel view synthesis (NVS) is a fundamental problem in computer vision and graphics. Recent advances in neural radiance fields (NeRF), 3D Gaussian Splatting (3DGS), and generative view synthesis have substantially improved its quality. Yet most methods still rely on clean observations, where image structures and cross-view geometric cues are well preserved. Motion blur breaks this assumption by corrupting local details and weakening multi-view correspondences. Such blur commonly arises from camera shake, scene motion, or finite exposure in practical capture. Blur-aware NVS methods address this degradation by modeling image formation, but their reliance on costly per-scene optimization limits efficient and generalizable sparse-view synthesis. To address this, we propose DeblurNVS, a novel framework for synthesizing high-fidelity novel views directly from sparse motion-blurred images, without requiring per-scene optimization. DeblurNVS restores the intermediate geometric representations needed for multi-view reasoning, enabling blurred inputs to recover reliable structure and correspondence cues. The restored representations are then combined with target camera information to synthesize the target-view representation and reconstruct a sharp RGB novel view. To enable the large-scale training, we construct a motion-blurred NVS dataset from DL3DV-10K using interpolation-based finite-exposure blur synthesis. Extensive experiments demonstrate that DeblurNVS outperforms existing baselines on synthetic motion-blur benchmarks and generalizes to real motion-blurred scenes, producing perceptually sharper and structurally more stable novel views while avoiding costly per-scene optimization. Project page: https://github.com/PKU-YuanGroup/DeblurNVS.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims to introduce DeblurNVS, a framework for novel view synthesis from sparse motion-blurred images using geometric latent diffusion to restore intermediate geometric representations from the blurred inputs. These restored representations are then combined with target camera information to synthesize sharp RGB novel views. The approach is enabled by constructing a motion-blurred NVS dataset from DL3DV-10K using interpolation-based finite-exposure blur synthesis. The authors report that DeblurNVS outperforms existing baselines on synthetic motion-blur benchmarks, generalizes to real motion-blurred scenes, and produces perceptually sharper and structurally more stable novel views without requiring costly per-scene optimization.

Significance. If the results hold, the work would be significant for the field of novel view synthesis as it tackles the practical challenge of motion blur in input images, which is common in real-world captures. By avoiding per-scene optimization and using a trained model, it could lead to more efficient and scalable NVS methods applicable to degraded inputs.

major comments (2)
  1. [Abstract] The central claim that the geometric latent diffusion step 'restores the intermediate geometric representations needed for multi-view reasoning, enabling blurred inputs to recover reliable structure and correspondence cues' (abstract) is load-bearing for all downstream synthesis. The interpolation-based finite-exposure blur synthesis used to create the training data from DL3DV-10K may not reproduce real blur statistics such as non-linear trajectories, depth variation, and sensor noise; if the restored latents contain geometric errors, the reported gains in structural stability will not hold.
  2. [Experiments (implied)] No quantitative results, error bars, ablation studies, or metrics on geometric accuracy (e.g., correspondence error or ray consistency) are referenced. Without such evidence, the attribution of improved sharpness and stability to the geometric restoration step rather than other factors cannot be verified.
minor comments (1)
  1. The abstract provides a project page link but does not indicate whether code, models, or the constructed dataset will be released to support reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the constructive feedback. We address the two major comments point by point below, clarifying the role of the geometric restoration and the available evidence while noting where revisions are warranted.

read point-by-point responses

  1. Referee: [Abstract] The central claim that the geometric latent diffusion step 'restores the intermediate geometric representations needed for multi-view reasoning, enabling blurred inputs to recover reliable structure and correspondence cues' (abstract) is load-bearing for all downstream synthesis. The interpolation-based finite-exposure blur synthesis used to create the training data from DL3DV-10K may not reproduce real blur statistics such as non-linear trajectories, depth variation, and sensor noise; if the restored latents contain geometric errors, the reported gains in structural stability will not hold.

    Authors: We acknowledge that interpolation-based blur synthesis is an approximation and does not fully capture real-world effects such as non-linear trajectories or sensor noise. This approach follows common practice in motion deblurring literature for scalable dataset creation. The manuscript demonstrates generalization to real motion-blurred scenes with perceptually sharper and more stable outputs, which provides indirect support for the utility of the restored representations. We will add a dedicated limitations paragraph on the synthetic blur model and include further qualitative analysis on real data to substantiate the structural stability claims. revision: partial

  2. Referee: [Experiments (implied)] No quantitative results, error bars, ablation studies, or metrics on geometric accuracy (e.g., correspondence error or ray consistency) are referenced. Without such evidence, the attribution of improved sharpness and stability to the geometric restoration step rather than other factors cannot be verified.

    Authors: The manuscript presents quantitative comparisons against baselines on synthetic motion-blur benchmarks using standard image quality metrics, plus qualitative evidence of improved sharpness and stability on both synthetic and real scenes. We agree that direct geometric accuracy metrics and ablations would strengthen attribution to the latent diffusion component. In the revised manuscript we will add error bars, ablation studies isolating the geometric restoration, and new quantitative metrics including correspondence error and ray consistency. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical training pipeline with independent evaluation

full rationale

The paper describes a supervised generative model (geometric latent diffusion) trained on a synthetically blurred dataset constructed via interpolation-based finite-exposure synthesis from DL3DV-10K. Performance claims rest on empirical comparisons against baselines on held-out synthetic and real scenes, not on any derivation that reduces to fitted parameters renamed as predictions or self-referential definitions. No load-bearing step equates the output to the input by construction; the geometric restoration is learned from data rather than presupposed. This is a standard end-to-end trained CV pipeline whose validity is externally falsifiable via benchmark metrics.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no information on free parameters, axioms, or invented entities; all fields left empty.

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discussion (0)

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Reference graph

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