MoBGS: Motion Deblurring Dynamic 3D Gaussian Splatting for Blurry Monocular Video

• URL: http://arxiv.org/abs/2504.15122v2
• Date: Wed, 30 Apr 2025 12:22:45 GMT
• Title: MoBGS: Motion Deblurring Dynamic 3D Gaussian Splatting for Blurry Monocular Video
• Authors: Minh-Quan Viet Bui, Jongmin Park, Juan Luis Gonzalez Bello, Jaeho Moon, Jihyong Oh, Munchurl Kim,
• Abstract summary: MoBGS reconstructs sharp and high-quality views from blurry monocular videos in an end-to-end manner.<n>We introduce a Blur-adaptive Latent Camera Estimation (BLCE) method for effective latent camera trajectory estimation.<n>We also propose a physically-inspired Latent Camera-inspired Exposure Estimation (LCEE) method to ensure consistent deblurring of both global camera and local object motion.
• Score: 26.468480933928458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present MoBGS, a novel deblurring dynamic 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 for effective latent camera trajectory estimation, improving global camera motion deblurring. In addition, we propose a physically-inspired Latent Camera-induced Exposure Estimation (LCEE) method to ensure consistent deblurring of both global camera and local object motion. Our MoBGS framework ensures the temporal consistency of unseen latent timestamps and robust motion decomposition of static and dynamic regions. Extensive experiments on the Stereo Blur dataset and real-world blurry videos show that our MoBGS significantly outperforms the very recent advanced methods (DyBluRF and Deblur4DGS), achieving state-of-the-art performance for dynamic NVS under motion blur.

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