单模型实现任意分辨率视频渲染与任意比例渐进编码
Progressively Deformable 2D Gaussian Splatting for Video Representation at Arbitrary Resolutions
- 用可变形2D高斯点表示视频，通过神经微分方程建模时间演化
- 训练后按D-最优准则剪枝，支持任意比特率渐进传输
- 每秒渲染超250帧，兼容多尺度连续码率-画质调节，适合视频压缩应用
隐式神经表示（INRs）虽能实现快速视频压缩和高效处理，但单一模型难以跨码率与分辨率实现可扩展解码。现有方法通常依赖重训练或双分支设计，结构化剪枝也无法提供排列无关的渐进传输顺序。受高斯点积显式结构与高效性启发，我们提出D2GV-AR，一种可变形2D高斯视频表示，可在单个模型中实现任意尺度渲染与任意比例渐进编码。将视频划分为固定长度的图像组，每组用一组基准2D高斯基元表示，其时序演化由神经常微分方程建模。训练与渲染时，依据奈奎斯特采样定理进行尺度感知分组，构建跨分辨率的嵌套层次。模型训练完成后，通过D-最优子集目标对基元进行剪枝，实现任意比特率渐进编码。大量实验表明，D2GV-AR渲染速度超过250 FPS，性能达到或超越近期INR基线，支持多尺度连续码率-质量自适应。
原文摘要 · Abstract (English)
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.
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