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.

翻译：本文提出了一种统一框架，能够从散焦与运动模糊的单目视频中实现高质量动态高斯泼溅。由于散焦模糊与运动模糊的形成机制存在显著差异，现有方法通常仅针对其中一种进行优化，缺乏同时处理两者的能力。尽管二者可统一建模为基于模糊核的卷积过程，但准确估计模糊核的内在困难极大限制了该方向的研究进展。本工作在此方向上进一步推进：具体而言，我们提出通过模糊预测网络估计像素级可靠模糊核，该网络利用与模糊相关的场景及相机信息，并受模糊感知稀疏性约束。此外，我们引入动态高斯致密化策略以缓解不完整区域的高斯分布缺失问题，并通过融合未观测视角信息约束场景优化，从而提升新视角合成的性能。大量实验表明，本方法在从散焦与运动模糊单目视频生成逼真新视角合成方面优于现有先进技术。代码已开源：https://github.com/hhhddddddd/dydeblur。