@inproceedings{
sun2024splatter,
title={Splatter a Video: Video Gaussian Representation  for Versatile Processing},
author={Yang-Tian Sun and Yi-Hua Huang and Lin Ma and Xiaoyang Lyu and Yan-Pei Cao and XIAOJUAN QI},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=bzuQtVDxv0}
}
Video representation is a long-standing problem that is crucial for various downstream tasks, such as tracking, depth prediction, segmentation, view synthesis, and editing. However, current methods either struggle to model complex motions due to the absence of 3D structure or rely on implicit 3D representations that are ill-suited for manipulation tasks. To address these challenges, we introduce a novel explicit 3D representation—video Gaussian representation—that embeds a video into 3D Gaussians. 
Our proposed representation models video appearance in a 3D canonical space using explicit Gaussians as proxies and associates each Gaussian with 3D motions for video motion. This approach offers a more intrinsic and explicit representation than layered atlas or volumetric pixel matrices. To obtain such a representation, we distill 2D priors, such as optical flow and depth, from foundation models to regularize learning in this ill-posed setting.
Extensive applications demonstrate the versatility of our new video representation. It has been proven effective in numerous video processing tasks, including tracking, consistent video depth and feature refinement, motion and appearance editing, and stereoscopic video generation.
Splatter a Video: Video Gaussian Representation  for Versatile Processing
Yang-Tian Sun Yi-Hua Huang Lin Ma Xiaoyang Lyu Yan-Pei Cao XIAOJUAN QI