Source-linked AI summary
Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle
Youtian Lin, Zuozhuo Dai, Siyu Zhu, Yao Yao
TL;DR
Dynamic scene reconstruction and rendering need high fidelity without the slow training and rendering of NeRF-based methods or the storage burden of per-frame 3DGS. Gaussian-Flow uses explicit deformable Gaussian particles with a dual-domain deformation model, achieving state-of-the-art speed and novel-view quality, while thin-structure fidelity remains a limitation.
Problem
NeRF-based dynamic reconstruction is limited by slow training and rendering, while separate per-frame 3DGS is storage-intensive and unsuitable for monocular video.
Method
Gaussian-Flow represents a 4D scene with deformable 3D Gaussian points and models position, rotation, and radiance residuals using joint polynomial and Fourier fitting without neural networks.
Results
Gaussian-Flow sets a new state-of-the-art for 4D reconstruction in training speed, rendering FPS, and novel-view synthesis quality, including 5× faster training than separate per-frame 3DGS.
Takeaways & Limitations
Explicit deformation modeling preserves 3DGS-like speed while supporting dynamic scene reconstruction and rendering across multi-view and monocular datasets.
Takeaways & Limitations
The method has room to improve high-fidelity rendering of thin structures, and its attribute dimensions are assumed to change independently over time.
Abstract
from arXiv · showhide
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
1. Introduction
Gaussian-Flow targets dynamic 4D reconstruction and rendering by replacing slow or storage-intensive approaches with an explicit 3D Gaussian representation. Its deformation model preserves fast rendering while improving reconstruction quality across multi-view and monocular videos.
- NeRFs require costly ray-wise sampling and field evaluation, hindering high-fidelity real-time rendering, especially for dynamic scenes.
- 3DGS renders two orders of magnitude faster than vanilla NeRF, but per-frame optimization is storage-intensive and unsuitable for monocular video.
- Gaussian-Flow models dynamic scenes as deformable 3D Gaussian points with explicit position, rotation, and radiance deformations.
- Its Dual-Domain Deformation Model fits time-dependent residuals with polynomial and Fourier representations while avoiding neural networks.
- 5× faster training than separate per-frame 3DGS modeling was achieved, alongside significantly better novel-view rendering quality than prior leading methods.
- The approach eliminates per-frame 3DGS optimization and implicit-field sampling, retaining running speed comparable to original 3DGS with minimum overhead.
2. Related Works
Related work progresses from dynamic NeRF and hybrid representations toward explicit point-based rendering. However, existing dynamic methods still face compromises in rendering speed, while 3DGS offers a strong static-scene foundation.
- Dynamic NeRF methods extend time as an input dimension or transfer canonical scenes across frames using scene flow or motion fields.
- NeRF acceleration approaches combine streamlined strategies, neural implicit functions, and explicit 3D structures to reduce training and inference time.
- Dynamic neural radiance-field methods use planes, grids, streaming representations, or sampling networks, but some retain slow rendering and none fully resolve real-time rendering.
- 3DGS uses differentiable point-based rendering and has demonstrated real-time novel-view synthesis with state-of-the-art rendering quality.
3. Gaussian-Flow
Gaussian-Flow extends 3D Gaussian Splatting to dynamic 4D scenes by explicitly fitting time-dependent deformations of Gaussian attributes with a Dual-Domain Deformation Model. The pipeline combines polynomial and Fourier representations with temporal and spatial regularization to preserve efficient reconstruction and rendering.
- 3.1. Recap on 3D Gaussian Splatting: 3DGS represents scenes with Gaussian points, projects them to images, and combines overlapping Gaussians through alpha blending for real-time novel-view synthesis.Each Gaussian has learnable position, covariance, opacity, and view-dependent radiance; adaptive density control uses cloning and splitting to improve reconstruction quality.
- 3.2. Dual-Domain Deformation Model: DDDM fits complex motion trajectories more accurately than polynomial or Fourier-series fitting alone in the illustrated examples.Figure 3 compares the three fitting approaches against sampled trajectory points.
- 3.2. Dual-Domain Deformation Model: Gaussian-Flow models each Gaussian point’s position, rotation, and radiance as deformable attributes while keeping scaling and opacity constant over time.Each changing attribute is expressed as a reference value plus a time-dependent residual.
- 3.2. Dual-Domain Deformation Model: DDDM combines time-domain polynomial fitting with frequency-domain Fourier series fitting to represent time-dependent attribute residuals.Different dimensions of an attribute receive independent residual functions, such as separate functions for the three position dimensions.
- 3.3. Adaptive Timestemp Scaling: Adaptive timestamp scaling dilates normalized time inputs to model substantial motion within short intervals and reduce optimization instability.The scaled time is used as the input to D(t), with dilation and base factors initialized to 1 and 0 in the experiments.
- 3.4. Regularizations: Temporal smoothness regularization encourages attributes at t and t + ϵ to remain consistent, while KNN rigid loss promotes local spatial consistency during alternating optimization stages.The perturbation is set adaptively as ϵ = 0.1/frames, and the latter optimization stage keeps the point set fixed while applying the local rigid constraint.
4. Experiments
Experiments evaluate Gaussian-Flow across multi-view and monocular datasets, testing deformation models, regularizations, model orders, efficiency, quantitative quality, and qualitative reconstruction.
- Datasets: Gaussian-Flow is evaluated on both multi-view and monocular video datasets, including Plenoptic Video and HyperNeRF.Plenoptic Video uses multi-camera recordings, while HyperNeRF uses monocular recordings with large motions, complex lighting, and thin structures.
- Deformation Models: The hybrid DDDM produces sharper details with fewer artifacts than polynomial-only or Fourier-only deformation fitting.Fourier fitting preserves sharper details but introduces more artifacts, whereas polynomial fitting is smoother but produces blurrier renderings.
- Regularizations: Adding either KNN rigid or time-smooth regularization improves novel view rendering quality, while combining both achieves the best performance.The regularization ablation evaluates the two proposed components separately and together.
- Deformation Models: Order 16 yields the best novel view rendering results on HyperNeRF, while performance drops after Fourier-series order 32 because of possible over-parameterization.The ablation identifies model order as important for scene complexity and final performance.
- Quantitative Comparisons: On HyperNeRF, Gaussian-Flow achieves the fastest training time, highest rendering FPS, and highest novel-view PSNR among compared methods.The comparison includes prior NeRF-based and 3DGS-based approaches.
- Quantitative Comparisons: Gaussian-Flow requires 7 minutes of training, renders in real time, and achieves better PSNR than previous state-of-the-art methods.Previous NeRF-based methods require at least 30 minutes and do not achieve real-time dynamic-scene rendering.
- Quantitative Comparisons: Extending training to 60K steps improves performance, reaching the highest Plenoptic Video performance, while increasing training time by about 1.5× there and 2× on HyperNeRF.The longer training schedule remains faster than previous methods.
5. Conclusion
Gaussian-Flow models dynamic 3D scenes with a point-based differentiable rendering framework and DDDM across time and frequency domains. Experiments report state-of-the-art 4D reconstruction speed, rendering performance, and novel view synthesis quality.
- Gaussian-Flow is a point-based differentiable rendering framework for dynamic 3D scene reconstruction.
- DDDM models each 3D Gaussian point’s deformations in both the time and frequency domains.
- Qualitative comparisons evaluate Gaussian-Flow against prior methods on HyperNeRF and Plenoptic Video datasets.The comparisons include TiNeuVox, HyperNeRF, Nerfies, NeRFPlayer, K-Planes, DyNeRF, and LLFF.
- Gaussian-Flow establishes state-of-the-art performance in training speed, rendering frames per second, and novel view synthesis quality.
6. Limitations
The method remains limited in preserving high-fidelity thin structures during final rendering, despite its rendering speed and training efficiency.
- Thin structures remain a fidelity limitation in the final rendering.The paper identifies refined deformation models or advanced regularization as possible future directions.
A. Implementation Details
The implementation applies DDDM to three selected 3DGS attributes and uses a staged training procedure with scheduled point optimization and KNN rigid loss.
- DDDM models Gaussian position, quaternion rotation, and the first three spherical-harmonics coefficients.Learning each attribute follows the corresponding 3DGS attribute’s approach.
- Training begins with 2000 iterations without deformation before continuing with DDDM-based deformation training.
- Gaussian splitting, cloning, and pruning stop at 15K iterations.
- KNN rigid loss starts at 5000 iterations after the Gaussian-point count is fixed.Fixing the point count allows the KNN index to be computed once for greater efficiency.
B. More Results
Additional results evaluate novel-view rendering across more viewpoints and scenes, including spatial and temporal variants and depth-map reconstruction.
- Additional visual results cover broader viewpoints and scenes across spatial and temporal dimensions.
- The method also reconstructs depth maps in the additional results.
- Figure 8 reports more view-synthesis results for the americano, chickchicken, and split cookie scenes.
- Figure 9 presents rendering and depth-map results on the Plenoptic Video dataset from additional viewpoints and times.