Implicit neural representations (INRs) enable fast video compression and effective video processing, but a single model rarely offers scalable decoding across rates and resolutions. In practice, multi-resolution typically relies on retraining or multi-branch designs, and structured pruning failed to provide a permutation-invariant progressive transmission order. Motivated by the explicit structure and efficiency of Gaussian splatting, we propose D2GV-AR, a deformable 2D Gaussian video representation that enables \emph{arbitrary-scale} rendering and \emph{any-ratio} progressive coding within a single model. We partition each video into fixed-length Groups of Pictures and represent each group with a canonical set of 2D Gaussian primitives, whose temporal evolution is modeled by a neural ordinary differential equation. During training and rendering, we apply scale-aware grouping according to Nyquist sampling theorem to form a nested hierarchy across resolutions. Once trained, primitives can be pruned via a D-optimal subset objective to enable any-ratio progressive coding. Extensive experiments show that D2GV-AR renders at over 250 FPS while matching or surpassing recent INR baselines, enabling multiscale continuous rate--quality adaptation.
@article{arxiv.2503.05600,
title = {Progressively Deformable 2D Gaussian Splatting for Video Representation at Arbitrary Resolutions},
author = {Mufan Liu and Qi Yang and Miaoran Zhao and He Huang and Le Yang and Zhu Li and Yiling Xu},
journal= {arXiv preprint arXiv:2503.05600},
year = {2026}
}