# DBMovi-GS: Dynamic View Synthesis from Blurry Monocular Video via Sparse-Controlled Gaussian Splatting

arXiv: https://arxiv.org/abs/2506.20998 · HTML: https://mlsanity.com/paper/2506.20998/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 23 of 23)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes](https://mlsanity.com/paper/2011.13084/) (2020)
- [Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis](https://mlsanity.com/paper/2308.09713/) (2023)
- [Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting](https://mlsanity.com/paper/2310.10642/) (2023)
- [Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis](https://mlsanity.com/paper/2312.16812/) (2023)
- [Novel View Synthesis with Diffusion Models](https://mlsanity.com/paper/2210.04628/) (2022)
- [Neural Radiance Flow for 4D View Synthesis and Video Processing](https://mlsanity.com/paper/2012.09790/) (2020)
- [NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior](https://mlsanity.com/paper/2212.07388/) (2022)
- [Deblur-NeRF: Neural Radiance Fields from Blurry Images](https://mlsanity.com/paper/2111.14292/) (2021)
- [3D Geometry-aware Deformable Gaussian Splatting for Dynamic View Synthesis](https://mlsanity.com/paper/2404.06270/) (2024)
- [BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields](https://mlsanity.com/paper/2211.12853/) (2022)
- [DP-NeRF: Deblurred Neural Radiance Field with Physical Scene Priors](https://mlsanity.com/paper/2211.12046/) (2022)
- [3D-aware Image Generation using 2D Diffusion Models](https://mlsanity.com/paper/2303.17905/) (2023)
- [MultiDiff: Consistent Novel View Synthesis from a Single Image](https://mlsanity.com/paper/2406.18524/) (2024)
- [4D Visualization of Dynamic Events from Unconstrained Multi-View Videos](https://mlsanity.com/paper/2005.13532/) (2020)
- [Deblurring 3D Gaussian Splatting](https://mlsanity.com/paper/2401.00834/) (2024)
- [MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images](https://mlsanity.com/paper/2008.06534/) (2020)
- [ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images](https://mlsanity.com/paper/2309.08957/) (2023)
- [DynPoint: Dynamic Neural Point For View Synthesis](https://mlsanity.com/paper/2310.18999/) (2023)
- [Gaussian Splatting on the Move: Blur and Rolling Shutter Compensation for Natural Camera Motion](https://mlsanity.com/paper/2403.13327/) (2024)
- [Point-DynRF: Point-based Dynamic Radiance Fields from a Monocular Video](https://mlsanity.com/paper/2310.09647/) (2023)
- [Evaluating Alternatives to SFM Point Cloud Initialization for Gaussian Splatting](https://mlsanity.com/paper/2404.12547/) (2024)

## Built on by (most cited first, 1 of 1)

- [Kinematics-Driven Gaussian Shape Deformation for Blurry Monocular Dynamic Scenes](https://mlsanity.com/paper/2605.08635/) (2026)

## Abstract (arXiv)

Novel view synthesis is a task of generating scenes from unseen perspectives; however, synthesizing dynamic scenes from blurry monocular videos remains an unresolved challenge that has yet to be effectively addressed. Existing novel view synthesis methods are often constrained by their reliance on high-resolution images or strong assumptions about static geometry and rigid scene priors. Consequently, their approaches lack robustness in real-world environments with dynamic object and camera motion, leading to instability and degraded visual fidelity. To address this, we propose Motion-aware Dynamic View Synthesis from Blurry Monocular Video via Sparse-Controlled Gaussian Splatting (DBMovi-GS), a method designed for dynamic view synthesis from blurry monocular videos. Our model generates dense 3D Gaussians, restoring sharpness from blurry videos and reconstructing detailed 3D geometry of the scene affected by dynamic motion variations. Our model achieves robust performance in novel view synthesis under dynamic blurry scenes and sets a new benchmark in realistic novel view synthesis for blurry monocular video inputs.