We present MoBGS, a novel motion deblurring 3D Gaussian Splatting (3DGS) framework capable of reconstructing sharp and high-quality novel spatio-temporal views from blurry monocular videos in an end-to-end manner. Existing dynamic novel view synthesis (NVS) methods are highly sensitive to motion blur in casually captured videos, resulting in significant degradation of rendering quality. While recent approaches address motion-blurred inputs for NVS, they primarily focus on static scene reconstruction and lack dedicated motion modeling for dynamic objects. To overcome these limitations, our MoBGS introduces a novel Blur-adaptive Latent Camera Estimation (BLCE) method using a proposed Blur-adaptive Neural Ordinary Differential Equation (ODE) solver for effective latent camera trajectory estimation, improving global camera motion deblurring. In addition, we propose a Latent Camera-induced Exposure Estimation (LCEE) method to ensure consistent deblurring of both a global camera and local object motions. Extensive experiments on the Stereo Blur dataset and real-world blurry videos show that our MoBGS significantly outperforms the very recent methods, achieving state-of-the-art performance for dynamic NVS under motion blur.
@article{arxiv.2504.15122,
title = {MoBGS: Motion Deblurring Dynamic 3D Gaussian Splatting for Blurry Monocular Video},
author = {Minh-Quan Viet Bui and Jongmin Park and Juan Luis Gonzalez Bello and Jaeho Moon and Jihyong Oh and Munchurl Kim},
journal= {arXiv preprint arXiv:2504.15122},
year = {2025}
}
Comments
This paper has been accepted to AAAI 2026. The first two authors contributed equally to this work (equal contribution). The last two authors are co-corresponding authors. Please visit our project page at https://kaist-viclab.github.io/mobgs-site/