DBMovi-GS：基于稀疏控制高斯泼溅的模糊单目视频动态视图合成
DBMovi-GS: Dynamic View Synthesis from Blurry Monocular Video via Sparse-Controlled Gaussian Splatting
AI总结：
针对模糊单目视频动态视图合成难题，提出DBMovi-GS方法，通过生成密集3D高斯恢复清晰度并重建动态场景3D几何，在动态模糊场景下实现鲁棒的新视图合成性能。
摘要：
新视图合成是一项从未见过的视角生成场景的任务；然而，从模糊的单目视频中合成动态场景是一个尚未得到有效解决的挑战。现有的新视图合成方法受限于对高分辨率图像的依赖，或对静态几何和刚性场景先验的强假设。因此，这些方法在存在动态物体和相机运动的现实环境中缺乏鲁棒性，导致视图保真度下降。为了解决这一问题，我们提出了基于稀疏控制高斯泼溅的模糊单目视频运动感知动态视图合成（DBMovi-GS），这是一种专为从模糊单目视频合成动态视图而设计的方法。我们的模型生成密集的3D高斯，从模糊视频中恢复清晰度，并重建由动态运动变化引起的场景详细3D几何结构。我们的模型在动态模糊场景下的新视图合成中实现了鲁棒的性能，为模糊单目视频输入的现实新视图合成设定了新的基准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
