Dynamic Gaussian Scene Reconstruction from Unsynchronized Videos
Zhixin Xu, Hengyu Zhou, Yuan Liu, Wenhan Xue, Hao Pan, Wenping Wang, Bin Wang
Abstract
Multi-view video reconstruction plays a vital role in computer vision, enabling applications in film production, virtual reality, and motion analysis. While recent advances such as 3D Gaussian Splatting have demonstrated impressive capabilities in dynamic scene reconstruction, they typically rely on the assumption that input video streams are temporally synchronized. However, in real-world scenarios, this assumption often fails due to factors like camera trigger delays, frame rate discrepancies, or independent recording setups, leading to temporal misalignment across views and reduced reconstruction quality. To address this challenge, a novel temporal alignment strategy is proposed for high-quality 4DGS reconstruction from unsynchronized multi-view videos. Our method features a coarse-to-fine alignment module that estimates and compensates for each camera
BibTeX
@inproceedings{aaai2026_dynamicgaussians,
title = {Dynamic Gaussian Scene Reconstruction from Unsynchronized Videos},
author = {Zhixin Xu and Hengyu Zhou and Yuan Liu and Wenhan Xue and Hao Pan and Wenping Wang and Bin Wang},
booktitle = {AAAI 2026},
year = {2026}
}