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

• URL: http://arxiv.org/abs/2312.11458v1
• Date: Mon, 18 Dec 2023 18:59:03 GMT
• Title: GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View Synthesis
• Authors: Yiqing Liang, Numair Khan, Zhengqin Li, Thu Nguyen-Phuoc, Douglas Lanman, James Tompkin, Lei Xiao
• Abstract summary: We propose a method for dynamic scene reconstruction using deformable 3D Gaussians. The differentiable pipeline is optimized end-to-end with a self-supervised rendering. Our method results are comparable to state-of-the-art neural radiance field methods.
• Score: 17.572987038801475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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