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

• URL: http://arxiv.org/abs/2312.11458v3
• Date: Wed, 15 Jan 2025 22:17:24 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: Implicit deformable representations commonly model motion with a canonical space and time-dependent deformation field. GauFRe, uses a forward-warping deformation to explicitly model non-rigid transformations of scene geometry. Experiments show our method achieves competitive results and higher efficiency than previous state-of-the-art NeRF and Gaussian-based methods.
• Score: 16.733855781461802
• License:
• Abstract: We propose a method that achieves state-of-the-art rendering quality and efficiency on monocular dynamic scene reconstruction using deformable 3D Gaussians. Implicit deformable representations commonly model motion with a canonical space and time-dependent backward-warping deformation field. Our method, GauFRe, uses a forward-warping deformation to explicitly model non-rigid transformations of scene geometry. Specifically, we propose a template set of 3D Gaussians residing in a canonical space, and a time-dependent forward-warping deformation field to model dynamic objects. Additionally, we tailor a 3D Gaussian-specific static component supported by an inductive bias-aware initialization approach which allows the deformation field to focus on moving scene regions, improving the rendering of complex real-world motion. The differentiable pipeline is optimized end-to-end with a self-supervised rendering loss. Experiments show our method achieves competitive results and higher efficiency than both previous state-of-the-art NeRF and Gaussian-based methods. For real-world scenes, GauFRe can train in ~20 mins and offer 96 FPS real-time rendering on an RTX 3090 GPU. Project website: https://lynl7130.github.io/gaufre/index.html

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