Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle
Youtian Lin Zuozhuo Dai Siyu Zhu Yao Yao
We introduce Gaussian-Flow, a novel point-based approach for fast dynamic
scene reconstruction and real-time rendering from both multi-view and monocular
videos. In contrast to the prevalent NeRF-based approaches hampered by slow
training and rendering speeds, our approach harnesses recent advancements in
point-based 3D Gaussian Splatting (3DGS). Specifically, a novel Dual-Domain
Deformation Model (DDDM) is proposed to explicitly model attribute deformations
of each Gaussian point, where the time-dependent residual of each attribute is
captured by a polynomial fitting in the time domain, and a Fourier series
fitting in the frequency domain. The proposed DDDM is capable of modeling
complex scene deformations across long video footage, eliminating the need for
training separate 3DGS for each frame or introducing an additional implicit
neural field to model 3D dynamics. Moreover, the explicit deformation modeling
for discretized Gaussian points ensures ultra-fast training and rendering of a
4D scene, which is comparable to the original 3DGS designed for static 3D
reconstruction. Our proposed approach showcases a substantial efficiency
improvement, achieving a $5\times$ faster training speed compared to the
per-frame 3DGS modeling. In addition, quantitative results demonstrate that the
proposed Gaussian-Flow significantly outperforms previous leading methods in
novel view rendering quality. Project page:
https://nju-3dv.github.io/projects/Gaussian-Flow