Splatter a Video: Video Gaussian Representation for Versatile Processing

TODOs

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• Scripts for tracking / consistent feature generation
• Release training code (in December 2024)

Table R1. Comprehensive comparison with existing methods on Tap-Vid benchmark (DAVIS).

Metric	PSNR ↑	SSIM ↑	LPIPS ↓	AJ ↑	$\delta_{avg}^x ↑$	OA ↑	TC ↓	Training Time	GPU Memory	FPS
4DGS	18.12	0.5735	0.5130	5.1	10.2	75.45	8.11	~40 mins	10G	145.8
RoDyF	24.79	0.723	0.394	\	\	\	\	> 24 hours	24G	> 1min
Deformable Sprites	22.83	0.6983	0.3014	20.6	32.9	69.7	2.07	~30 mins	24G	1.6
Omnimotion	24.11	0.7145	0.3713	51.7	67.5	85.3	0.74	> 24 hours	24G	> 1min
CoDeF	26.17	0.8160	0.2905	7.6	13.7	78.0	7.56	~30 mins	10G	8.8
Ours	28.63	0.8373	0.2283	41.9	57.7	79.2	1.82	~30 mins	10G	149

Citation

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If you find our work useful, please consider citing:

@article{sun2024splatter,
  title={Splatter a Video: Video Gaussian Representation for Versatile Processing},
  author={Sun, Yang-Tian and Huang, Yi-Hua and Ma, Lin and Lyu, Xiaoyang and Cao, Yan-Pei and Qi, Xiaojuan},
  journal={arXiv preprint arXiv:2406.13870},
  year={2024}
}