从模糊单目视频中重建动态场景，实现高保真视角合成。
DBMovi-GS: Dynamic View Synthesis from Blurry Monocular Video via Sparse-Controlled Gaussian Splatting
- 用稀疏控制的高斯点云，从模糊视频恢复清晰3D结构。
- 在动态模糊场景下实现稳定且高保真的新视角生成。
- 适合真实复杂环境中的动态视频重建任务。
新视角合成旨在从未见视角生成场景；然而，从模糊单目视频中合成动态场景仍是一个未解决的挑战。现有方法通常依赖高分辨率图像或对静态几何与刚性场景先验的强假设，导致在存在动态物体和相机运动的真实环境中缺乏鲁棒性，造成不稳定和视觉质量下降。为此，我们提出基于稀疏控制高斯点云的动态视角合成方法（DBMovi-GS），专为模糊单目视频设计。该模型生成密集3D高斯点，从模糊视频中恢复清晰度，并重建受动态运动影响的详细3D场景结构。该方法在动态模糊场景下的新视角合成中表现出色，为模糊单目视频输入设立了新的基准。
原文摘要 · Abstract (English)
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.
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