GauFRe：用于实时动态新视角合成的高斯变形场

摘要

我们提出了一种用于动态场景重建的方法，使用适用于单目视频的可变形3D高斯模型。在高斯光滑性的基础上，我们的方法通过扩展表示来容纳动态元素，这些元素通过驻留在规范空间中的一组可变形高斯模型和由多层感知器（MLP）定义的时间相关变形场来实现。此外，在假设大多数自然场景具有保持静态的大区域的情况下，我们允许MLP通过另外包括一个静态高斯点云来集中其表示能力。连接的动态和静态点云构成了高斯光滑光栅化器的输入，实现了实时渲染。可微分管道通过自监督渲染损失进行端到端优化。我们的方法实现了与最先进的动态神经辐射场方法可比的结果，同时实现了更快的优化和渲染。项目网站：https://lynl7130.github.io/gaufre/index.html

English

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

GauFRe：用于实时动态新视角合成的高斯变形场

GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View Synthesis

摘要

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