We propose a method for dynamic scene reconstruction using deformable 3D Gaussians that is tailored for monocular video. Building upon the efficiency of Gaussian splatting, our approach extends the representation to accommodate dynamic elements via a deformable set of Gaussians residing in a canonical space, and a time-dependent deformation field defined by a multi-layer perceptron (MLP). Moreover, under the assumption that most natural scenes have large regions that remain static, we allow the MLP to focus its representational power by additionally including a static Gaussian point cloud. The concatenated dynamic and static point clouds form the input for the Gaussian Splatting rasterizer, enabling real-time rendering. The differentiable pipeline is optimized end-to-end with a self-supervised rendering loss. Our method achieves results that are comparable to state-of-the-art dynamic neural radiance field methods while allowing much faster optimization and rendering. Project website: https://lynl7130.github.io/gaufre/index.html

使用可变形3D高斯方法进行动态场景重建，将高斯飞溅效果扩展以适应通过多层感知器定义的时间相关变形场的可变形高斯集合的表示，同时使用静态高斯点云允许多层感知器集中其表示能力，最终优化得到与状态最前沿的动态神经辐射场方法相媲美且具有更快的优化和渲染速度。

GauFRe：用于实时动态新视角合成的高斯变形场