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Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction
Zhiyang Guo, Wengang Zhou, Li Li, Min Wang, Houqiang Li
TL;DR
Dynamic 3DGS methods often overlook motion information in 2D observations, limiting efficient and consistent reconstruction. The paper introduces an optical-flow-based motion-aware framework with cross-dimensional correspondence, uncertainty-aware flow augmentation, and transient-aware deformation assistance, and reports superior rendering quality and efficiency over baselines in multi-view and monocular scenes.
Problem
Existing dynamic 3DGS methods mainly extend static representations while overlooking rich motion cues in 2D observations, leaving Gaussian motion under-constrained.
Method
The framework aligns 3D Gaussian movements with pixel-level optical flow using uncertainty-aware flow augmentation and transient-aware deformation assistance.
Results
The method outperforms baselines in rendering quality and efficiency across multi-view and monocular scenes.
Takeaways & Limitations
Motion cues from optical flow can enhance both iterative and deformation-based dynamic 3DGS paradigms.
Takeaways & Limitations
Optical-flow dependence leaves motion blur problematic, and motion uncertainty remains difficult to overcome, especially in monocular scenes.
Abstract
from arXiv · showhide
3D Gaussian Splatting (3DGS) has become an emerging tool for dynamic scene reconstruction. However, existing methods focus mainly on extending static 3DGS into a time-variant representation, while overlooking the rich motion information carried by 2D observations, thus suffering from performance degradation and model redundancy. To address the above problem, we propose a novel motion-aware enhancement framework for dynamic scene reconstruction, which mines useful motion cues from optical flow to improve different paradigms of dynamic 3DGS. Specifically, we first establish a correspondence between 3D Gaussian movements and pixel-level flow. Then a novel flow augmentation method is introduced with additional insights into uncertainty and loss collaboration. Moreover, for the prevalent deformation-based paradigm that presents a harder optimization problem, a transient-aware deformation auxiliary module is proposed. We conduct extensive experiments on both multi-view and monocular scenes to verify the merits of our work. Compared with the baselines, our method shows significant superiority in both rendering quality and efficiency.
1 Introduction
Dynamic scene reconstruction remains difficult because motion, topology changes, and sparse observations complicate time-varying 3D modeling. This work enhances dynamic 3DGS with optical-flow motion cues, uncertainty-aware flow augmentation, and transient-aware deformation assistance, outperforming baselines in multi-view and monocular scenes.
- Dynamic scene reconstruction is challenging because scenes change over time while motion complexity, topology changes, and sparse observations hinder reconstruction.
- Existing dynamic 3DGS methods extend static representations but often overlook motion cues in sequential 2D observations, leaving Gaussian motion under-constrained.
- The framework establishes 3D Gaussian–pixel-flow correspondence, adds uncertainty-aware flow augmentation, and introduces transient-aware deformation assistance.
- The method outperforms baselines qualitatively and quantitatively in multi-view and monocular scenes, enabling more accurate and efficient dynamic-content modeling.
2 Related Works
Related dynamic reconstruction methods build on NeRF and 3DGS, with 3DGS offering efficient explicit modeling but distinct trade-offs between iterative and deformation-based paradigms. The paper uses optical-flow guidance to improve motion modeling without choosing between these paradigms.
- NeRF-based dynamic methods provide strong visual quality but incur substantial time overhead, motivating efficient alternatives such as 3DGS.
- Dynamic 3DGS includes iterative methods that update Gaussians frame by frame and deformation-based methods that deform a canonical Gaussian representation over time.
- 3DGS offers fast training and rendering, while iterative modeling can consume excessive memory and deformation-based modeling maintains only one canonical Gaussian set.
- The paper systematically explores flow-guided motion awareness in 3DGS to enhance both dynamic modeling paradigms.
3 Preliminary
3DGS explicitly represents scenes with learnable 3D Gaussians and renders pixels by projecting and alpha-blending depth-ordered contributions. Dynamic extensions update Gaussian parameters iteratively or deform a canonical representation over time.
- 3.1 3D Gaussian Splatting: 3DGS represents a scene with millions of explicit 3D Gaussians whose learnable parameters include centers, rotations, sizes, colors, and opacity.
- 3.1 3D Gaussian Splatting: Each Gaussian softly occupies space through an opacity-weighted Gaussian function determined by its center and covariance.
- 3.1 3D Gaussian Splatting: Differentiable rendering projects Gaussian centers and covariances onto the camera plane, then computes pixel colors by blending depth-ordered Gaussian contributions.
- 3.1 3D Gaussian Splatting: Gaussian parameters are optimized using reconstruction loss between rendered and ground-truth images.
- 3.2 Different Paradigms of Dynamic 3DGS: Dynamic 3DGS methods follow iterative or deformation-based paradigms, and this work develops motion-aware enhancements for both rather than selecting one.
4 Method
The method enhances both iterative and deformation-based dynamic 3DGS with optical-flow motion supervision, dynamic awareness, and a deformation-specific transient auxiliary module. It establishes cross-dimensional correspondence between Gaussian movements and pixel flow, then addresses flow-supervision errors, uncertainty, and optimization ambiguity.
- Framework overview: The framework augments iterative and deformation-based dynamic 3DGS with flow supervision and a dynamic map, adding transient information for deformation-based optimization.The deformation-based branch additionally uses a motion injector to handle motion ambiguities through auxiliary transient information in Gaussian features.
- 4.1 Cross-Dimensional Motion Correspondence: Dynamic scene flow is modeled by establishing dense correspondence between 3D Gaussian-center movements and pixel-level optical flow.Gaussian centers are unprojected and reprojected across timestamps, while all foreground candidates for a pixel receive the same optical-flow supervision.
- 4.1 Cross-Dimensional Motion Correspondence: Render-based flow supervision can produce squeezed optical flow, supervision errors, and incorrect Gaussian drift because 2D flow and Gaussian rendering interact imperfectly under occlusion.The method therefore avoids relying solely on renderer-produced flow for motion supervision.
- 4.2 Flow Augmentation: The flow augmentation aligns projected scene flows with pretrained optical flow and uses uncertainty-aware weighting to reduce the influence of unreliable predictions.Its variance increases for Gaussian contributions that are smaller or more uncertain, while a regularizer prevents trivial solutions.
- 4.2 Flow Augmentation: The dynamic map collaborates with existing losses by emphasizing regions with larger motion and distinguishing dynamic Gaussians from static background during optimization.A balancing coefficient preserves the original color loss when flow predictions are unreliable for tiny motions.
- 4.3 Transient-aware Deformation Auxiliary: For deformation-based 3DGS, a motion-injection auxiliary loss adds projected Gaussian-motion constraints because joint modeling across timestamps creates a harder optimization problem.The auxiliary constraint selects foreground Gaussians from the current view and uses neighboring-time Gaussian features.
5 Experiments
Experiments evaluate the framework across multi-view and monocular datasets, comparing quantitative quality, qualitative motion reconstruction, efficiency, sparse-view robustness, and component ablations. The method improves reconstruction quality and efficiency across the reported settings while reducing motion and Gaussian redundancy.
- Comparison Results: The method produces less background noise, more precise motion, less blur in dynamic regions, and clearer static regions in qualitative comparisons.These observations are reported for multi-view and monocular scenes, respectively.
- Comparison Results: On Neu3DV, the enhanced iterative framework surpasses the latest NeRF-based method by a large margin, reversing the usual disadvantage of prior 3DGS approaches.The comparison is described in the discussion of Table 2.
- Comparison Results: Across PanopticSports, Neu3DV, and HyperNeRF, the proposed frameworks outperform competing methods in the reported quantitative comparisons, including all metrics on monocular HyperNeRF.Tables 1–3 cover iterative and deformation-based variants across multi-view and monocular settings.
- Efficiency of Dynamic Reconstruction: The framework improves efficiency by reducing Gaussian and motion redundancy without extra rendering cost or a separately designed Gaussian-pruning module.Optical-flow acquisition can be performed in real time, while the augmentation affects only optimization.
- Efficiency of Dynamic Reconstruction: Under sparser multi-view inputs, the proposed method remains competitive while baselines suffer significant degradation.The sparse-view study reduces available views for both iterative and deformation-based paradigms.
- Ablation Study: Ablations show that dense motion correspondence, uncertainty handling, and the transient-aware deformation auxiliary each address distinct weaknesses of simpler flow-supervision designs.Render-based supervision degrades performance, uncertainty handling reduces sensitivity to prior errors, and the deformation auxiliary resolves ambiguity in deformation-based optimization.
6 Conclusion and Discussion
The paper presents motion-aware 3DGS with uncertainty-aware flow augmentation and transient-aware deformation auxiliary modules, reporting superiority on prevalent datasets. It identifies motion blur and optical-flow uncertainty, especially in monocular scenes, as remaining limitations.
- Motion-aware 3DGS combines uncertainty-aware flow augmentation and transient-aware deformation auxiliary strategies to enhance both iterative and deformation-based dynamic 3DGS.The framework is designed to leverage optical-flow motion cues across both modeling paradigms.
- Comprehensive experiments on prevalent datasets demonstrate the superiority of the proposed approach.
- Motion blur can cause overfitting that hinders temporal consistency of Gaussians.
- Errors in flow priors remain difficult to overcome, especially for monocular scenes, motivating exploration of additional priors.