Monocular Dynamic Gaussian Splatting: Fast, Brittle, and Scene Complexity Rules
Authors: Yiqing Liang
Mikhail Okunev
Mikaela Angelina Uy
Runfeng Li
Leonidas Guibas
James Tompkin
Adam W Harley
Venue: Accepted by TMLR ()

Abstract: Gaussian splatting methods are emerging as a popular approach for converting multi-view image data into scene representations that allow view synthesis. In particular, there is interest in enabling view synthesis for dynamic scenes using only monocular input data---an ill-posed and challenging problem. The fast pace of work in this area has produced multiple simultaneous papers that claim to work best, which cannot all be true. In this work, we organize, benchmark, and analyze many Gaussian-splatting-based methods, providing apples-to-apples comparisons that prior works have lacked. We use multiple existing datasets and a new instructive synthetic dataset designed to isolate factors that affect reconstruction quality.  
We systematically categorize Gaussian splatting methods into specific motion representation types and quantify how their differences impact performance. Empirically, we find that their rank order is well-defined in synthetic data, but the complexity of real-world data currently overwhelms the differences. Furthermore, the fast rendering speed of all Gaussian-based methods comes at the cost of brittleness in optimization. We summarize our experiments into a list of findings that can help to further progress in this lively problem setting.

Published in Transactions on Machine Learning Research (06/2025)
Monocular Dynamic Gaussian Splatting: Fast, Brittle, and Scene Complexity Rules
Yiqing Liang', Mikhail Okunev', Mikaela Angelina Uy Runfeng Li', Leonidas Guibas?, James Tompkin', Adam W. Harley?
'Brown University
Stanford University
*NVIDIA
[yiqing_ liang, aikhail_okuuee, runfeng_li, james_tompking ttt.ttkkmomnbooooonn edu, (mikacuy, guibas, (erlleyy@cs.staforddde
Reviewed on OpenReview: http:://gaarroi.s...aee/////t/tp
Abstract
Gaussian splatting methods are emerging as a popular approach for converting multi-view image data into scene representations that allow view synthesis. In particular, there is interest in enabling view synthesis for dynamic scenes using only monocular input data- ill-posed and challenging problem. The fast pace of work in this area has produced multiple simultaneous papers that claim to work best, which cannot all be true. this work, we organize, benchmark, and analyze many Gaussian-splatting- based methods, providing apples- to-apples comparisons that prior works have lacked. We use multiple existing datasets and new instructive synthetic dataset designed to isolate factors that affect reconstruction quality, We systematically categorize Gaussian splatting methods into specific motion representation types and quantify how their differences impact performance. Empirically, we find that their rank order is well-defined in synthetic data, but the complexity of real-world data currently overwhelms the differences. Furthermore, the fast, rendering speed of all Caussian-based methods comes at the cost of brittleness in optimization. We summarize our experiments into a list findings that can help to further progress in this lively problem setting.
1 Introduction
Let us consider the problem of monocular dynamic view synthesis. Using monocular camera data rather than multi-view data makes the problem highly ill-posed. In the past year, there have been hundreds of research papers on Gaussian splatting, with some applied to monocular dynamic view synthesis. Many papers claim superior performance compared their peers even with only minor methodological differences between them this seems implausible. The lack of shared evaluation benchmark and inconsistent dataset splits make fair comparison between methods difficult, not impossible, based solcly on existing published results.
Our work provides clear snapshot of progress on the problem of dynamic view synthesis for monocular sequences with Gaussian splatting approaches. Inspired in part by DyCheck (Gao et al. 2022), which benchmarked Neural Radiance Fields (NeRFs), our work quantifies the performance of existing work in comparisons and provides analysis on failure modes. To support this analysis, we aggregate datasots used across current works, totaling 50 scenes. We also quantify performance on an instructive synthotic dataset that we created, which contains sequences with controlled camera and scone motion.
Our analysis establishes several findings that help clarify progress in this rapidly-movine field:
1. Gaussian methods struggle compared to hybrid neural fields: While Gaussian methods achieve fast rendering (20 200 FPS vs 0.3 FPS for TiNeuVox (Pang et al. (2022)) )), they are consistently outperformed by ToNeuVox in image quality across datasets and have similar (and more variable) optimization times.
2. Low-dimensional motion representations help: Local, low-dimensional representations of motion perform better than less-constrained systems, with field-based methods (DeformableGS (Yang el. al. (2023) 4DGS (w et al.] (2023))) showing better quality than per-Gaussian motion models.

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3. Dataset variations overwhelm methodological differences: Contrary to claims in individual works, we find no clear rank-ordering of methods across datasets, with performance varying siguificantly by scene.
4. Adaplive density control rol causes significant issues: This mechanism leads to varying efficiency across scenes; increased overfitting risk, and occasional optimization failures.
5. Lack of multi-view cues particularly hurts dynamic Gaussians: On strictly-monocular data like the iPhone (Gao et al. (2022] dataset, all Gaussian methods perform substantially worse than TiNeuVox
6. Narrow baselines and fast objects cause systematic errors: Our synthetic dataset reveals degraded reconstruction quality as camera baselines decrease or object motion increases.
7. Specular objects remain challenging for all methods: Contrary to some claims, all current methods struggle with scenes containing reflective surfaces.
8. Metrics be dominated by static background regions, requiring specific mcasurement dynamic regions through /dntim/ masks to quantitatively reveal progress in methods.
Public release. Code and qualitative results including instructive synthetic dataset can be found here
Data can be downloaded here including segmentation masks and better camera poses for existing datasets.
2 Overview
First, we will establish the specific background relevant to our experiments. We present more comprchensive discussion of related work our supplementary material.
2.1 3D Gaussian Splatting (3DGS)
Gaussian splatting (erbb et al., 2023) represents a
set of 3D anisotropic Gaussians allowing for high-quality and fast view synthesis given only input images for static 3D scene. Concretely, the scene is represented as a collection of 3D Gaussians with underlying attributes, where each Gaussian G, = (z,, Σ, Cts c,) is parameterized with mean Z₁, covariance matrix Σ,, opacity a;, and view-dependent RGB color c; parameterized by 2nd-order spherical harmonic coefficients. The covariance matrix E, is factorized into a rotation matrix R2 derived from a unit quaternion 9: E R1, and scaling vector 8; E R to guarantce to be positive semi-dcfinite, giving Σ; = R,diag(s) (s)
These parameters are optimized directly given set of images with known camera intrinsics K and extrinsics V via gradient descent using differentiable rasterization. To form an image, the color of a pixel (u, v) can be obtained by along the camera ray for pixel (u, u):
AN sorted by depth the contributions the visible Ganssians (G, 1-=1
C(u,v) = Coappp v)
II ( v)) k=1
(1)
JEN
(a) Statie and Dynamic Gaussian Splatting
(b) Adaptive Density Control
Dynamics module
Camera
Time
Projection
Split
Points
Or,
Initialize
Handler
Rasterizer
Image
Adaptivve Density
Temporal
Gaussians
Gaussians
Control
Operation Flow
Gradient Flow
Pruning
Opacity
Random
Figure 1: Gaussian splatting with motion. a) Overview of 3D Gaussian Splatting. Adding motion to static 3DGS typically modifies the pipeline only by adding dynamics module, as marked by the red box. b) Adaptive densification via cloning, splitting, and pruning Gaussians to reflect scene details. This brittle process often causes difficulties during optimization for monocular dynamic sequences.
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Table 1: Overview of existing dynamic GS-based methods. We organize and summarize the type,
motion model and list of monocular datascts tested on by the different methods.
Method
Ref.
Type
Motion model
Monocular; tested datasets
Dynamic 3D Gaussians
Luiten et al.
2023
Itteraive Iterative
3DGStream
Sun et al.
2024
x
RTGS
Yang et al.
2024
4D
/ dnerf
Rotor-Baseer Based 4DGS
Duan et
2024
4D
/ dnerf
Space Timeeesssnss
Li et al.
2023a
3D motion
Poly RBF
x
GauMesh
Xiao et al. 22024
3
HexPlane
x
GauFRe
Liang ct a..
20233
motion
MLP
/ duerf, hypernerf, nerfds
Deformable-GG
Yang al. 2023
3D+motion
MLP
/ dnerf, hypernerf, nerfds
MoDGS
Liu et al. 2024
3D-motion
MLP
4DGS
Wu ct al.
22023
3D+motion
HexPlane
/ duerf, hypernerf
EffGS
Katsumata et al.
2023
3D-motion
Fourier Poly
dnerf, hypernerf
DynMF
Kratimenos et al.
2023
3D+motion
Fourier+Mir MLP
/ dnerf, hypernerf
Shape of Motion
Wang el al. 2024
3Dtmotion
Poly+MLP
/ iphone
Ganssian-Flon
Lin et al. 2023
3
Fourier+ +Poly
/ dnerf, hypernerf
GaGS
Lu et al. 2024
motioo
VoxeI+MLP
/ dnerf, hypernerf
E-D3DGS
Bae et al.
[2024
4Dmootoon
MLP
/ hypernerf
where (u, v) is the contribution of Gaussian Gj to pixel (u,v), which is the probability density of the j-th Gaussian at pixel v). To obtain (,(u, v) given camera intrinsic K and extrinsic V, the 3D Gaussian G-=M(r (r, Σ₁) is approximated by 2D Gaussian via lincarization of the perspective transformation (Zwicker et al. 2001 j. This gives the 2D mean z/= KVI, E R²a and covariance Σ JVE, where J is the Jacobian the perspective projection.
To balance rendering quality and computational efficiency, 3DGS uses adaptive densification via periodically performing cloning, splitting, and pruning of the Gaussians (Figure 1
The optimization requires the static scene to be obscrved from multiple viewpoints such that 3D Gaussians converge to the underlying 3D scene geometry even under the ambiguity from 2D projection. This makes it extremely challenging for both the monocular and dynamic setting. Further difficulty is introduced in non-Tambertian scenes where view-dependent scene elements such as glossy objects or mirrors can cause Gaussians to be mistakenly placed to hallucinate reflections.
2.2 Gaussian Splatting for Dynamic View Synthesis
Extending 3DGS methods to dynamic scenes from monocular input requires deciding how represent time (or spacetime). For instance, what kind of model is used to describe Gaussian motion, whether that model applies to Gaussians individually or collectively, whether motion is offset from a single timestep or from a canonical space representing all time, or even whether to use a motion model at all (3D++motonn') or simply to represent 4D space (Tab. [ Methods must also decide which Gaussian parameters to change over time, e.g., position, rotation, typically via offsets 8G,,- The choice of motion design can significantly impact the expressiveness, efficiency, and robustness of the overall dynamic 3DGS system.
Motion reference frame. Iterative approaches assume that Gaussian motion time can be optimized from some reference timestep (say, t == in which Gaussians are already well-placed. Liuten et al. first showed multi-view dynamic 3DGS (Luiten al.] (023]p by updating Gaussian parameters one frame at a time. We might define discrete function to query at each time to obtain Gaussian offsets:
f(i,0) - G f(i.t)= = fli,t 1] 5G,,-
This approach relies upon the reference frame being well reconstructed, such as from multi-view input, otherwise errors can be propagated across time (Fig, 2 Instead, canonical methods (Yang et al. (2023);
Wu et al. (2023): Kratimenos al. (2023) Liang ct al. [2023] Bac ct al. (2024): Lu et al.) (2024)) assume 3D Gaussians are offsct from an embedding that represents all of time without being any one frame. This helps for monocular cameras where multi-view constraints must be formed over time. Effectively, all monocular methods use some form of canonicalization.
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Motion complexity, Motion may be defined by function ʃ of time 1 with varying complexity. For instance, we could define Gaussian motion to be linear over time, which would only be able to describe basic motions. A piecewise-line lineer model could explain many motions-c.g, Luiten et al's Dynamic 3D Gaussian work is effectively piecewise linear (Litenn et al. 2023
Train GT
Train Render
Test GT
Test Render
Frame 0
as Gaussian positions vary over integer timesteps. But, for monocular input, in practice authors must find low- dimensional 'sweet spot' functions that can constrain or smooth the ill-posed motion optimization.
Frame 15
Curve methods use a polynomial basis of order to define f over time that determines Gaussian offsets (Lin et al.) 2023 ʃ could also use a Fourier basis (Katsumata
Figure 2: Iterative methods fail on monocular input.
al. 2023) or a Gaussian Radial
Basis (Li et al.) 2023aa). Most of these typically use low order, where L = 2 surprisingly even L 1. We could also define a motion function with a multi-layer perceptron (MLP). A ReLU MLP would be a piccewise-lincar model with changes at arbitrary points in continuous time rather than integer times.
Motion complexity is important to reconstruction quality and computational speed. It also affects the success of optimization as it determines motion smoothness. For instance, MLP-based method are robust function fitters with regulaaguaii properties, but come with higher computational burden. In our survey, we found that function complexity like the polynomial order not always reported, making it difficult to consider these trade-offs.
Motion locality. As 3D Gaussian splatting is primitive based, many dynamic approaches define motion models also per-Gaussian property. This extends the parameter set of each Gaussian by the parameters of the motion model, which can be costly in terms of memory, and ignores the spatial implications of motion: that ncarby primitives may move together.
Field-based approaches (Xie et al.) 2022) attempt to encapsulate this idea by using a function to estimate the entire motion field. Gaussians then query this field by their position and time or by an embodding vector. Fields can be advantagcous in that the number of parameters in the motion model is independent of the number of Gaussians, and that the field can enforce smoothness between Gaussians indirectly by the complexity of the motion function.
One popular approach is to use field embeddings represent both locality space and motion over time [Liang et al., 2023: Yang et al. 2023 Wu et al. 2023 Lu et al., 2024). For instance, continuous deformation field encoded by an MLP Jo might take as input each Gaussian G!'s embedding 2; and to produce an offset:
fo(z,, =Gaa Gi, G. + 8Gs,
(3)
As they represent volumes of contimuous space, field-based approaches can exploit ideas from volume-based NeRF literature, such as voxel (Lu et al. 2024) and HexPlane (Wu et al. 2023) structures. These discrete data structures can also help to aggregate information and accelerate indexing.
Finally, intermediate approaches have begun to aggregate motion model parameters over local Gaussian neighborhoods LLei et al. 2024). In general, there are many schemes to model both motion complexity and locality, but it helps to consider how both space and time are represented and smoothed.
Higher dimensionality instcad of motion. Another alternative instead of modelling motion together with 3D Gaussians to define Gaussians directly in 4D space (Yang et al. 2024 Duan et al. 2024): mean x; E R and covariance matrix Σ; R This representation couples the space (IR*) and time (R) dimensions. To optimize this representation through rendering, the 4D Gaussians are first conditioned on or projected to a given time to obtain 3D Gaussians; then, these can be rendered and compared to the 2D images at the given timeframes. 4D methods are flexible and can describe complex dynamic scenes. However, as there are many possible projections of plausible motions within the 4D space, these models may be difficult to fit correctly with the lew constraints provided by monocular input. Moreover, the representation also does not. have inherent. smoothness locality constraints.
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3
Evaluation Setup
3.1 Video Datasets
Sequence variations affect performance siguificantly within and across datasets. Scenes with small scene motion only or large camera motion relative to object motion are casier than sequences with large scene motion and small camera motion. Scenes with highly-textured objects are easier to reconstruct but require more Gaussians. To gain much variation as possible, we collect all common datasets used across papers. We briefly explain key differences here; our supplemental material provides a fuller description and examples.
D-NeRF (Punarola et al. (020)) shows synthetic objects captured by 360-orbit inward-facing cameras against a white background (8 scenes). Nerfies (Park et al. (2021a)) (4 scenes) and HyperNeRF (Park et al. (2021b)) (17 scenes) data contain general real-world scenes of kitchen table top actions, human faces, and outdoor animals. NeRF-DS (Yan el al. (2023) contains many reflective surfaces in motion, such as silver jugs glazed ceramic plates held by human hands in indoor tabletop scenes (7 scenes). Some existing datasets (D-NeRF, Nerfies, HyperNeRF) use a "teleporting" camera setting (Gao et al. (2022)) instead of using natural handheld camera trajectories: views from different cameras are used different timesteps create pseudo-monocular video. This sctup provides good multi-view constraints but is unrcalistic: real handheld videos have more contimuous camera motion and fewer viewpoint changes. NeRF-DS dataset 's cameras do not "teleport", but the scene motion is small by design. The iPhone dataset from DyCheck (Gao et al. (022)) (14 scenes) has dynamic objects undergoing large motions, with real-world camera trajectories.
In total, these comprise 50 scenes. For all datasets, we use the original train/test splits as provided by the authors to ensure fair comparison.
Instructive synthetic dataset. To vary scene motion complexity in a controlled way, we also create a dataset with varying camera and scene motion (Figure 3). Each scene 60 frames long. These scenes contain a simple textured cube and textured background wall. While simple, these scenes have both slow and fast camacra and scene motion, and many methods struggle to reconstruct them. In SlidingCube, the cube accelerates from stationary along a straight line to cover different distances E {0, 5, 10). The camera moves around the cube in an are with total distance of B E (1,3, 5, 10, 20). The camera. rolates to track the cube at all times. In RolatingCube, the cube additionally rotates along its vertical axis by a radians.
Figure 3: Left: Illustration of the instructive dataset. A simple textured cube moves in a straight line (distance D) against a differently-exxtured background. In another condition, the cube additionally performs overall rotation (ó radians). The training-view camera moves in an are (covering distance B) in the same direction as the cube, tracking it. The test camera (not depicted) travels in a sinusoidal trajectory behind the training camera. Middle: A training view. Right: test view at the same time instance.
3.2 Metrics
For view synthesis, we use image quality metrics of peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM) (Wang et al. 2004) and its variant Multi-Scale SSIM (MS-SSIM) (Wang et al. 2003). and Iearned perceptual metric LPIPS (Zhang et al.) 2018) using the AlexNet-trained features. We also report training and rendering time in seconds. Existing rescarch typically reports metric as a mean average over sequences with one optimization attempl; we also report the standard deviation of three independent optimizations. All train time and rendering speeds are computed on NVIDIA GeForce RTX 3090 cards.
Static vs. dynamic. Static scene areas imaged by a moving camera may have enough constraints for recon- struction, and if these areas are large then they will dominate the metric. So, to demonstrate reconstruction quality on dynamic regions only, we compute per-frame binary dynamic masks using SAM-Track (Cheng et al. 2023 and report masked metrics mPSNR, mSSIM, mMS-SSTM and mIPPIPS.
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Mean and Variance For each experiment, we perform 3 runs to capture performance variability. In our visualizations, the height each bar represents the mean (μ², and the error bars exlend one standard deviation (a) above and below the mean.
3.3 Methods
We select representative methods of each motion group (Table to focus on "basic" representations without too much focus on regularization tricks.
1. Low Order Poly+ Fourier:
EffGS Katsumata et al. 2023 uses a 2-order fourier basis and a 1-order polynomial basis per Gaussian to model motion.
2. Low Order Poly+RBF:
(Li et al. 2023a or STG uses 3-order radial basis function and a 1-order polynomial basis per Gaussian to model motion. A small
SpaceTiieGaussian
MLP decodes color; we remove this for fairer comparison (cf. STG-decoder)
3. Field MLP:
DeformableGS (Yang et al. 2023) uses an MLP to estimate Gaussian offsets from a deformation field; the MLP is shared among all Gaussians.
4. Field HexPlane:
4DGS [Wu et al. 2023) discretizes and factorizes the field with HexP HexPlane (Cao
& Johnson 2023 this is shared among all Gaussians.
5. 4D Gaussian:
RTGS Yang et al. 2024 this has no explicit motion model.
To minimize differences in implementation except for the motion model, we integrate these algorithmss into single codebase. For hyperparamelers, we follow the original works closely, e-g., we use separate hyperparameters for synthetic and real-world scenes. Implementation details (e.g., motion-model degree, deformation-field depth, regularization loss weight, and learning rate) can be found in appendix C
Baselines: We also evaluate 3DGS without any motion, as adding motion model might make results worse. We also include TiNeu Vox (Fang et al. 2022) comparison point for what voxel feature grid method can accomplish on dynamic scenes, even if it cannot be rendered quickly.
4 Results
We present our main findings and include full details in the supplemental material. To help frame findings, we note that our implementations approximately match the results of the original works on their reported datasets. We also note that most dynamic Gaussian methods do not report performance across all datasets, and no dynamic Gaussian method has yet reported performance metrics on the iPhone dataset, the comparisons made possible by this full-slate evaluation are of special interest.
4.1 Finding 1: Gaussian Methods Struggle In Comparison to a Hybrid Neural Field
We begin our analysis by evaluating all mothods on all datascts. We present a summary of the findings, averaged across D-NeRF, Nerfies, HyperNeRF, NeRF-DS and iPhone datasets in Table 2 The full results are available in supplementary Section A.
Table 2: Summary of Quantitative Results. Table shows summarized quantitative evaluation of all methods averaged across all five datasots.
Method Metric
PSNR
SSIMT
MS-SSIMI
LPIPS1
FPS1
Timi (s)J
TiNeu Vox
24.54
0.706
0.804
0.349
0.29
2664.89
3DGS
19.48
0.651
0.688
0.358
243.47
2964.81
EfGSS
21.84
0.672
0.725
0.347
177.21
3757.81
STG-decoder
21.81
0.678
0.742
0.352
109.42
5980.64
STG
19.51
0.583
0.643
0.475
181.70
5359.56
DeformableGS
24.07
0.694
0.755
0.283
20.20
6227.43
4DGS
23.55
0.708
0.765
0.277
62.99
8628.89
RTGS
21.61
0.663
0.720
0.350
143.37
7352.52
From this summary, we can make the broad and sobering observation that TiNeuVox, non Gaussian method, regularly outperforms all Gaussian methods image quality. TiNeu Vox also trains quickly and converges reliably, compared to the Gaussian methods. The main drawback of TiNeu is simply its

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0.8
TiNeuVox
EMGGS
STG-decoder
3DGS
RTGS
STG
DeformableGS
4DGS
0.6
LPIPS
0.4
0.2
0.0
iphone
hypernerf
nerlds
Figure 5: Per-datasct Quantitative Results. Test set LPIPS along with error bars for all methods each of the different datasets. Note that lower is better. No method is clearly better across datasets.
1e6
3DGS
RTGS
STG- deccdec
6-
DeformableGS
1DGS
SIG
ErrGS
#Gaussions 1
I perfies
0
iphone
hypernerf
purfds
doorf
Figure 6: # Gaussians after optimization for each method, result of the adaptive densification.
rendering time, operating at 0.3 FPS compared to 20- 20-200 FPS for the Gaussian methods. The speed difference is due the rasterization 3DGS versus volume rendering in NeRF.
4.2 Finding 2: Low-dimensional motion representations help
Comparing motion-basee and 4D representations Table 2 appears that local, low-dimensional represen- (ations of motion perform better than less-constrriined systems.
Motion locality helps quality. Comparing Gaussian methods on the LPIPS metric, we can observe that the field-based methods (DeformableGS 4DGS) perform better than the methods which attach motions to individual Gaussians (EfGGS & STGs) in rendering quality.
Motion representation complexity hurts efficiency. Comparing methods training time and rendering speed, find that basis-based methods (EffGS &
Train GT
Train Render
Test GT
Test Render
STGs) are faster to train and faster to render than MLP-based methods (De- formableGS & 4DGS).
Going to 4D makes things worse. The 4D Gaussian approach, which is the most expressive representation of space- time, performs worst in both quality and efficiency. This consistent with our expectations established Scction 2.1 We qualitatively demonstrate this effect in Figure
Figure 4: RTGS overfits to training views
4.3 Finding 3: Dataset Variations Overwhelm Gaussian Method Variations
Inspecting the results of Gaussian methods across datasets, we find, contrary to the claims in the individual works, that rank-ordering of the methods is unclear. We plot the per-dataset performance metrics in Figure and note that the datasets have different winning methods, We observe that 4DGS and DeformableGS perform relatively well, but their performance across datascts varies greatly.
4.4 Finding 4: Adaptive Density Control Causes Headaches

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Nerfies
HyperNeRP
Vix R70000
3DGS
DeformableGS
BIGS
R--0.24
Rt-0.32
R -0-0442
13000
15000
13000
15000
12000
1rain l'mn (6)
Troin Time (5)
Train Time
0000
Trin Time
9000
9000
1000
3000
3000
3000
3000
2000
4000
8009
1od0
2000
2000
Brog
2000
4000
8oop
foesg
2001
3000
80op
10000
Mean Spectrum Magnitude
Mean Spectrum Magaitado
Mean Spectrum Magnitude
Mean Spectrum Magnltdde
STG-nodecoder
STG-decoder
Re=0.46
R==0.24
R==0.34
10000
15000
13000
15000
12000
12000
12000
120000
Tralo Time (s)
9000
Train Tlate
90000
Train 1hne
5000
8000
Train
0000
6000
3000
3000
3000
3000
2000
Losen
18000
4030
10000
10000
Mean Spectrum Magnitude
Mean Spectrum Magnisude
Mean Spectrum Magnitude
Moon Spectrum Magnitude
Figure 7: Convergence vs. frequency. We see that scenes with higher frequencics take longer to optimize.
iPhone
Nerfies
HyperNeRF
D NeRF
NeRF DS
Linear Fil
3DGS
DeformableGS
EOGS R==0.63
R==0.56
RM=0,42
Moan Spectrum Magnittdde
8x
Maguittade
Mean Spectrum Maquittado
x
6x
8x
xx301
TiNeaVox not-appiicable
4x
2x
x02
2x100'
2x
x
2 100 x 1x Number of Caussians
2x 10 x 1x 10" Number Gaussians
2x 20' 10f 8 10 1x Number of Caussians
4DGS R=-0.53
STG-decoder R=50.50
R=-0.49
Meen Spectrum Maguitade
30
Moan Spoctrum Magnitude
8 8xx
Spectrum Magnitude
8x..0
Mean Spectrum Maguttud
8 x0
8x
6x
8x.10"
xx300
4x30
40
x000
4x
2x200
1x
xx.0
2x10
2 10 5 10 10 3x3 Number of Gaussions
1x 5x100 x 1x 10'
1x 5 30t 8x 10 1N'
10 10" Bx 10" x Number of Gaussians
Number of Gaussians
Number of Gaussians
Figure 8: Frequency vs. # Gaussians. Scenes with higher frequencies end up with more Gaussians.

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iPhone
Nerfies
HyperNeRP
D.NeRF
NeRF-DS
Linear Pit
3DGS R0
DeformableGGS R*-0.58
BIGS
R5-0.5
10
3x
3
2x
10
2
2x
2xx10"
x.30
TiNeuVox not-applicable
7PS
1x10P
1x
1x300
10 10f1
0. 10f6
Caussians
Number
Number
RTGS
4DGS R==0.50
SIG-decoder R==0,38
STC Ro=0.46
R==0.72
2x
2.x
203
207
2 10
10
2
1PS
RPS
TPS
1x300
1x
1x
1
0 2x 10f4 10% x Number of Caussians
10% 10 10"sx 10h1
2xx x 10f6 10th Number Gaussians
10'
2x 10% 10%6 10fn K10 Number of Caussians
Number of Caussiens
Figure 9: Render FPS vs. # Gaussians. More Gaussians leads to slower rendering.
0.0
-0.1
LPIPS-gap
0.2
-0.3
TiNcuVox
EftGS
STG-decoder
-0.4
3DGS
RTGS
STG
-0.5
DeformableGS
4DGS
iphone
ner
hypernerl
Figure 10:
Train-Test Performance Gaps. We show the difference between the average LPIPS on the train and test set, where a larger gap indicates more overfitting to the training sequence. Note that here larger negative values indicate more severe overfitting.
As discussed in Section 2.1 key detail of Gaussian Splat- ting methods is adaptive density control: Gaussians are added and removed during optimization, according to carefully tuned heuristics, allowing the complexity of the representation to adapt to the complexity of the data.
Training and rendering efficiency varies greatly across scenes. The optimization time for Gaussian Splatting methods varies greatly across scenes, with certain seenes consistently opti- mizing quickly and others optimizing slowly, across methods. Further, the methods take significantly more time to reconst real-world datasets (HyperNeRF, iPhone) compared to the synthetic datasets.
Figure 11: Instability. Training frame and renderings from runs of the field MLP method DeformableGS (Yang et al. 2023
This factor is partially explained by the frequency condent of the data. For this analysis, we use a Fast. Fourier Transform (FFT) to transform each frame to the frequency domain, and aggregate the magnitude
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spectrum from all images to calculate the mean frequency. Scenes with a predominance of higher frequencies take longer to optimize (Figure 7 because more Gaussian splats are required for reconstruction (Figure D
The total number of Gaussians after optimization varies greatly across scenes (Figure G). This variability directly impacts efficiency, as rendering time is naturally tied the number of Gaussians optimized for each scene (Figure 9. Methods based neural deformation fields (DeformableGS and 4DGS) have much higher render time per Gaussian due to the inference time of the underlying ML.Ps.
While this adaptive density feature adds expressivity the overall representation, we find that it causes noticeable brittleness through three main effects: efficiency variations, increased overfitting risk, and occasional optimization failures. These factors may explain why some prior works tune the number of Gaussians and the adaptive density settings on a per-scene basis (Kerbl ct al., 2023).
Density adaptation makes overfitting worse. In Figure 10 we show the train-test performance gap for all Dynamic Gaussian mothods, along with 3DGS and TiNeu Vox. For this evaluation, we calculate LPIPS metrics for train and test set of the same sequence, and then substract test set metric results from train set metrics. Larger quantities in this evaluation indicate wider train-test gaps, and serve as an indicator for overfitting. The evaluation reveals that the methods with adaptive density (i.e., all methods except. TiNeuVox) have consistently wider train-test gaps. The only exception in the NeRF-DSS dataset, where we hypothesize that the reflective objects in that data cause issues for all methods, flattening the performance disparities between them.
Density adaptation risks total failure. Density changes can occasionally result in catastrophic failures with emply scene reconstructions (Figure [].. Gaussians may be pruned due to their opacities being under a hand-chosen threshold, and once a substantial mumber are deleted then it is difficult to recover the scene structure by subsequent cloning and splitting. We find that this happens unpredictably; we exclude these runs from our evaluation statistics.
4.5 Finding 5: Lack of Multi-View Cues Hurts Dynamic Gaussians More
Most datasets used for monocular dynamic view synthesis consist of data where there is a slow-moving scene captured by a rapidly-moving camera. This circumstance allows methods to leverage multi-view cues for optimization. DyCheck (Gao et al., 2022) introduced iPhone dataset to evaluate methods on "strictly-mooocclaar"" scenarios, meaning that the camera is moving more naturally. Figure 12 quantifies all methods' performance on this dataset. DyCheck showed that NeRF-based methods' performance dogrades in the face of strict monocular setting, and we find that GS-based methods suffer here too. In fact, excepting for the uarceptual metric LPIPS, all dynamic Gaussian hods perform worse than NeRF-like TiNeu Vox method by large margin. This is consistent with our finding on overfitting: since the Gaussian methods are more susceptible to overfitting, they are also more likely to do poorly with data lacking Multi-Vion Clues.
4.6 Finding 6: Narrow Baselines and Fast Objects Cause Error
Camera and object motion can both influence reconstruction performance considerably (Gao et al. 2022 This is related to the amount of multi-view information available to constrain optimization, but can be studied in detail with the help of our instructive synthetic dataset. Figure 14 (left) shows a high-level overview of the performance of all methods, across different camera baselines and different motion ranges. As the camera's baseline decreases, and/or as the object motion increases, reconstructions degrade. This result also holds for each method individually (see supplemental).
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Figure 14 (right) summarizes LPIPS and masked LPIPS across methods and scenes in our instructive synthetic dataset. Unlike in the complex real-world datasets (Table 2) we see a clear ranking emerge between methods. DeformableGSS is the most reliable and RTGS with EfGS are the least reliable. RIGS and STG occasionally crash during optimization, due to an out-of-memory error triggered as a side effect of the setting. As such, we could not compute consistent metrics for STG in a narrow baseline setting (camera baseline S 5), and we show two numbers in the figure: the metric computed over all baselines, where crashed runs are assigned minimum possible value for that metric (solid bar), and the metric computed over wide baselines only (textured bar). ToNeuVox competitive accordance with Section 4.1
Curiously, 3DGS completely static peethod-acheevee a high rank when using LPIPS over the whole image. This happens for several reasons. First, large portion of the image is covered by static background, which the method is well-suited to reconstruct. Second, 3DGS can overfit the dynamic object to some extent by creating incorrect geometry on its motion path and making it appear when needed. The artifacts on the dynamic object are still pronouneed, especially when the cube is rotating. In the masked LPIPS ranking 3DGS falls to the low end of the ranking as expected. In sum, we find that all methods are sensitive to camera baseline and the magnitude of the underlying motion, although the levels of robustness vary.
4.7 Finding 7: Specular Objects are Challenge for All Methods
Yang et al. (0023) highlighted that their DeformableGS method outpeforms previous statc-of-art methods As NeRF-DS focuses on specular objects, this might imply that other Gaussian methods are able to handle specular objects well. However, we found that this is not the case (Figure 13). Generally, it is difficult for methods to distinguish between specular effects and small object motions especially under small baselines.
on NeRF-DS dataset, including TiNeu
4.8 Finding 8: Foreground/Backen Backgroond Separation Clarifies Static/Dynamic Results
Inspecting the 3DGS performance in Table 2 we note that it performs surprisingly well, considering that it is a static scene representation. This is partly because the moving foreground constitutes small fraction of the pixels in the given datasct sequences. We also find that 3DGS is able to smuggle a pseudo-dynamic scene into its representation, by inserting Gaussians which only render from certain viewpoints (and therefore only certain timesteps), and thereby reduce loss on dynamic parts of the training images. Please see the supplementary material for visualizations of this effect.
To better evaluate Dynamic Gaussians' advantage over 3DGS, we evaluate mLPIPS, mPSNR, MSSIM and mMS-SSTM (Figure 15 Comparing Figure 5 and Figure 15 we see that Dynamic Gaussians' performance does not change much after masking, but 3DGS's performance worsens dramatically, suggesting that the dynamic scene representations are indeed better at capturing the moving part of the scene.
5 Discussion on Motion Representations and Reconstruction Quality
Extending static 3D Gaussian Splatting (3DGS) to handle dynamic scenes from a monocular camera requires carefully choosing how to represent time and motion. Our experiments (Table 2 and Figure 5) show that the parameterization of motion can significantly affect both reconstruction quality and robustness -ytt single representation is universally superior (Finding 3). In this section, we summarize how these different representations influence performance, why certain approaches may excel in particular scenarios, and how scene complexity factors like multi-view coverage can overshadow the differences bet methods.
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Figure 14: Results on our instructive synthetic dataset. Top Left: LPIPS, heatmap shows how camera baseline and object motion range affect performance across all methods average. Decreasing object motion range (right to left) affects reconstruction performance positively; decreasing camera baseline (bottom to top) has the same effect. Values over 0.3 usually represent failure to reconstruct the dynamic object. Top Right: Ranking of methods by average LPIPS over all scenes. Solid bars represent score computed over all scenes. Textured bar represents metrics computed on wide camera baselines. Bottom Left: Masked LPIPSI heatmap: with the focus on dynamic object reconstruction, the metrics become worse on average. Bottom Right: Ranking for masked LPIPS.
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5.1 Why Motion Representations Matter
From optimization standpoint, any dynamic splatting algorithm must determine where and how Gaussians move as the scene evolves over time. The chosen motion representation fundamentally affects:
Expressivity: Does the method capture simple object translations only, or is it flexible enough for highly non-rigid deformations?
Robustness: Under srricly-mooncully settings (e.g., iPhone dataset), can it avoid overfitting (spurious geometry) collapsing solutions?
Efficiency: How does the motion model affect memory use, training speed, runtime performance?
Even small changes in parameterization (e.g., low-order polynomials vs. MLP-based fields vs. 4D embeddings) can cause large performance variations, especially under challenging setups with limited camera baselines or significant. ccenefooregroum motion (e.g., Finding 6 and Figure 14
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5.2 Per-Gaussian 3D Motion Field- Based 3D Motion 4D Representations
Now-Dimensional 3D. One main distinction is whether each Gaussian carries its own motion parameters
(o.g., low-order polynomials EffGS (Katsumata et al. (2023) ). STG (Li et al. (2023a ) or whether a shared deformation field provides offsets (DeformableGS Yang al. (2023)) 4DGS (wu et al. (2023 ))- Per-Gaussian approaches are highly flexible, since each splat can move independently. However, this flexibility can also encourage overfitting (Finding 5, Figure 10 and Figure 12), particularly if the camera only sees each Gaussian from limited span of views. By contrast, field-based methods encourage spatial coherence among Gaussians potentially boosting fidelity in dynamic regions, but at the cost of higher complexity and slower
inference (Table Figure 9 Figure 14
Figure 15
4D Gaussians (RTGS). An alternative paradigm is to embed space and time directly in I removing the need for any explicit motion function. RTGS (Yang et al. (2024)); ) for example, defines cach Gaussian in a unified 4D volume. While this representation is appealing for complex deformations, Finding and Figure A show that it can overfit severely in narrow-baseline (also see Finding 5). Rather than guaranteeing better fidelity, the added degrees of freedom can produce flickering or distorted geometry when the data insufficient to anchor Gaussians in time.
fast-motion scenarios if multi-view cues are lacking
5.3 Overfitting and Adaptive Density Control
All dynamic Gaussian splatting methods rely on adaptive density control to split, clone, prune Gaussians over the course of training. While this enhances expressivity (capturing fine details as the scene changes), it also heightens the risk of overfitting. Figure 10 shows how these methods tend have a larger train-test gap than simpler baselines like Vox (see Finding 1). In addition, adaptive density can lead to unpreiitabble Tuning this mechanism thus remains a critical factor for
optimization ion failures (Finding 4, Figure 11 successful reconstructions.
5.4 Dataset-Specific Factors Often Dominate
Despite theoretical differences among motion models, scene complexity and dataset factors often override these distinctions:
with fast-moving objects and smaller camera. baselines reveal bigger gaps in performance; most dynamic Gaussian methods lag behind a voxel-based approach (TiNeuVox) that slower to render but less prone overfitting (see Finding 5).
Soonculy Sequences: iPhone data (see Figure 12
High-Frequency or Reflective Surfaces: Scenes contain visual cues that mimic motion can confuse any parameterization (as in NeRF-DS, Figure 13).
Fast Foreground Motion: Our synthetic experiments (Finding 6, Figure 14 Figure 15 show how narrow camera baselines plus large object motion are especially prone to reconstruction failures.
Thus, which motion model you choose is only part of the story; the constraints provided by the scene (e.g., multi-view coverage, stable lighting, fewer specularities) can overshadow method-centric differences.
5.5 Guidelines and Implications
Taken together, our findings suggest:
Match Representation to Motion Demands. Field-based (shared) motion can capture non-rigid deformations more effectively, but might be computationally heavier (Finding 2, Table 2). Per-Gaussian polynomial or Fourier motion is simpler to train yet risks localized overfitting if the camera baseline is too small.
Simplify When Data Are Limited. In challenging scenarios (e.g., narrow baselines, specular objects), strongly constraining motion often prevents degenerate solutions (Finding 5).
Use Caution with Adaptive Density Control. Splitting and pruning Gaussians can add detail but also destabilize optimization (Finding 4).
Consider Foreground vs. Background Metrics. Masked metries (Finding 8) reveal a more accurate picture of dynamic-object quality; a static 3DGS approach may score well on overall PSNR most pixels are stationary, even though it fails in regions with actual motion.
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Ultimately, our evaluations suggest that monocular dynamic gaussian splatting is fast and brittle, and scene complexity rules. In other words, no single motion represenlation is universally ideal. Instead, the "best" " approach highly context-dependent, shaped by the complexity of the scene, the camera trajectory, and the interplay of adaptive density updates. Even so, awareness of each representation's 's inherent trade-offs guide practitioners in selecting or developing dynamic Gaussian splatting methods that balance robustness, expressivity, and computational cost real-world scttings.
6
Conclusion
With the emerging popularity of Gaussian splatting, multiple works were recently introduced simultaneously that tackle the challenging setting of dynamic scene view synthesis using only monocular input, each claiming superior performance even with only minor methodological differences and inconsistent evaluation settings. We organize, consolidate, benchmark, and analyze many of these Gaussian splatting- based methods and provide shared, consistent, apples-to-apples comparison between them instructive synthetic data and complex real-world data. We also defiee conceptual differences between these methods and analyze their impact on variely of performance metrics. This work may lead to further progress dynamic GS methods, with broader impact in areas such video editing, visual art, and 3D scene analysis for industrial applications.
Evaluations like ours would benefit from being able to describe each sequence's reconstruction complexity independently of any method's performance upon that sequence. With this, it would be simpler to find insights by correlating methods with scene difficulty. Establishing such a metric is a thicken-and-egg problem: it requires measuring the geometry and motion of dynamic scene, which is our initial problem. Past works have proposed approximations to a scenc's difficulty, but so far these metrics do not explain reconstruction quality well, e.g., DyCheck's (Gao et al.) 2022) w requires only camera pose but does not correlate to rectnstruction quality (Figure 16). Similarly, ground-truth evaluation of reconstruction such as for depth or motion would also aid in this task; however, capturing dense ground truth is difficult for real-world dynamic scenes (e.g., DyCheck's Ot requires sparse ground truth points and so cannot be widely used). Next, in this rapidly-growing field, could not include all concurrent works in our analysis; our publicly-relcasod code and data will make it possible to supplement our analysis with additional works. Finally, we note that various regularization ion stralegies have been proposed lo enhance stability and resolve ambiguities. We regard these promising directions for future exploration and provide brief survey in Appendix F
Limitations.
Acknowledgements. YL, MO, RL, JT thank NSF CAREER 2144956 and NASA RI-SONSSC23M0075.
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Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Wang Xinggang. 4d gaussian splatting for real-time dynamic scene rendering. arXiv preprinl or Xio:2310. Xio:2310.08528, 2023.
Yuting Xiao, Xuan Wang, Jiafei Li, Hongrui Cai, Yanbo Fan, Nan Xue, Minghui Yang, Yujun Shen, and Shenghua Gao. Bridging 3d gaussian and mesh for freeview video rendering, 2024. URL https:
//arxiv. abs/ /203. 11453
Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. Neural fields in visual computing and beyond. Computer Graphies Forum, 2022. ISSN 1467-8659. doi: 00.1111/cgf.14055
Zhiwen Yan, Chen Li, and Gim Hee Lee, Nerf-ds: Neural radiance fields for dynamic specular objects. In Proceedings of the IEEE/CV Conference on Computer Vision and Pattern Recognition, pp. 8285-8295, 2023.
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Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin. Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction. arXiv preprint arXiv: 13101, 2023.
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Published in Transactions on Machine Learning Research (06/2025)
A
Additional Results
Please check the webpage for qualitative results on both existing datasets and our instructive dataset.
A.1
Quantitative Results Per Dataset
Table 3:
Summary of Quantitative Results. Table shows summarized quantitative evaluation of all
methods averaged across D-NeRF dataset.
Method Metric
PSNR1
SSIMI
MS-SSIMI
LPIPS}
FPS1
TrainTime (s)
TiNeu Vox
33.03
0.97
0.99
0.03
0.42
2582.54
3DGS
20.64
0.92
0.88
0.12
340.85
928.83
EITGS
30.52
0.96
0.98
0.04
289.47
1042.04
STG-decoder
25.89
0.91
0.90
0.17
160.32
3462.29
STG
17.09
0.88
0.66
0.29
208.98
4889.50
DeformableGS
37.14
0.99
0.99
0.01
50.78
2048.38
4DGS
33.27
0.98
0.99
0.02
134.13
1781.46
RTGS
28.78
0.96
0.96
0.05
192.37
1519.60
Table 4: Summary of Quantitative Results. methods averaged across HyperNeRF dataset.
Table shows summarized quantitative evaluation of all
Method Metric
PSNR1
SSIM
MS-SSIMI
LPIPS}
FPS1
Train Time (s)
TiNeu Vox
26.45
0.74
0.88
0.38
0.12
2604.45
3DGS
20.98
0.69
0.76
0.37
244.87
2587.02
EffGS
22.24
0.70
0.79
0.37
138.79
4119.66
STG-decoder
23.92
0.73
0.83
0.34
66.72
7423.41
STG 22.92
0.70
0.78
0.41
183.21
5729.80
DeformableGS
24.58
0.74
0.83
0.27
10.91
8855.46
4DGS
25.70
0.79
0.89
0.23
37.46
10170.86
RTGS
22.99
0.71
0.79
0.35
104.64
11507.80
Table 5: Summary of Quantitative Results. Table shows summarized quantitative evaluation of all methods averaged across NeRF-DS datasct.
Method Metric
PSNR1
SSIMI
MS-SSIM1
LPIPS$
FPSt
Train Time (s)
TiNeu Vox
21.54
0.83
0.81
0.22
0.49
2696.48
3DGS
20.29
0.78
0.73
0.28
353.99
1066.48
ERGS
21.28
0.78
0.77
0.25
307.70
1597.90
STG-decoder
21.73
0.80
0.81
0.20
212.24
2131.48
STG
20.13
0.69
0.72
0.39
302.89
2214.62
DeformableGS
23.42
0.84
0.88
0.16
30.27
2885.07
4DGS
20.25
0.73
0.72
0.24
100.22
4075.43
RTGS
19.88
0.75
0.73
0.30
259.83
3116.21
19

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Published in Transactions on Machine Learning Research (06/2025)
Table 6: Summary of Quantitative Results. Table shows a summarized quantitative evaluation of all methods averaged across Nerfies dataset.
Method Metric
PSNRT
SSIMT
MS-SSIMT
LPIPS4
FPSt
Train Time (s)
TiNeu Vox
22.89
0.45
0.71
0.68
0.11
3145.67
3DGS
20.14
0.46
0.66
0.52
209.80
2823.33
BIGGS
20.13
0.43
0.61
0.65
159.17
3249.33
STG-decoder
20.93
0.47
0.67
0.58
90.46
6050.42
STG
20.55
0.46
0.66
0.62
142.30
5264.58
DeformableGS
21.26
0.43
0.66
0.54
14.99
6950.94
4DGS
21.84
0.50
0.72
0.47
35.70
9797.50
RTGS
20.06
0.42
0.62
0.61
153.38
9059.54
Table 7: Summary of Quantitative Results. Table shows a summarized quantitative evaluation of all methods averaged across iPhone dataset.
Method Metric
PSNRT
SSTM
MS-SSTM
LPIPS4
FPSt
Train Time (s)
TiNeu Vox
19.35
0.52
0.63
0.47
0.36
2632.17
3DGS
16.41
0.44
0.48
0.47
140.50
5777.45
EIGGS
16.82
0.47
0.50
0.47
99.64
6275.33
STG-decoder
16.85
0.47
0.52
0.49
86.18
7694.83
STG
15.60
0.40
0.45
0.57
114.95
6629.61 ..64
DeformableGS
16.56
0.46
0.48
0.45
10.47
7278.28
4DGS
15.13
0.42
0.43
0.52
42.53
14205.48
RTGS
15.36
0.39
0.44
0.52
101.33
8484.05
20

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Published in Transactions on Machine Learning Research (06/2025)
A.2
Quantitative Result Plots Per Metric
A.2.1
PSNR
TiNeuVox
EfGGS RTGS 4DGS
STG-decoder
3DGS
STC
35
DeformableCS
30-
20
iphone
nerlies
hypernerf
nerids
duerf
Figure 17: Per-dataset Quantitative Results. Test set PSNR along with error bars for all methods on each the datasets. (f). We see that the datasets have different winning methods.
TiNeuVox
DeformableeGS
STG-decoder
3DGS
EffGS
4DGS
STG
25
PSNR
20
15
Iphone
Derfles
hypernert
nerfds
Figure 18: Foreground-Only PSNRs Evaluation (t).
25
ToNeuVox
ENGS
STG-decoder
3DGS
RTCS
STG
20
DeformableCS
4DGS
PSNR-gap
15
10
iphone
nee feefies
Ity Hypenner
nerlds
dnerf'
Figure 19: Train-Tess Performance Gaps. We show the difference between the average PSNR on the train and test set, where a larger gap indicates more overfitting to the training sequence.
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Published in Transactions on Machine Learning Research (06/2025)
A.2.2 SSIM
TiNeuVox
ENGGS RTGS
STG-decoder
3DGS
STG
DelormableGS
4D3GS
SSIM
0.8
0.6
0.4
iphone
nerfies
bypernerf
nerids
dnerf
Figure 20: Per-dataset Quantitative Results. Test set SSIM along with error bars for all methods each of the datasets. (t). We see that the datasets have different winning methods.
TiNeuVox
DeformableCS
RTGS
STG-decoder
3DGS
EMGS
4DGS
STG
0.6
0.4
iphone
hypernerf
nerfds
Figure 21: Foreground-Only SSIMs Evaluation (t)-
TiNeuVox
EttGS
STG-decoder
3DGS
RTGS
STG
DeformableGS
4DGS
MS-SS(M-4ap
iphone
nerfies
hypernerf
nerfds
dnerf
Figure 22: Train-Tess Test Performance Gaps. We show the difference between the average SSIM on the train and test set, where larger gap indicates more overfitting to the training sequence.
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Published in Transactions on Machine Learning Research (06/2025)
A.2.3
MS-SSIM
TiNeuVox
ENGGS RTGS
STG-decoder
1.0
3DGS
STG
DelormableGS
4D3G
MS-SSIM 1
0.8
0.4
iphone
nerfies
bypernerf
nerids
dnerf
Per-datasct Quantitative Results. Figure shows the test set MS-SSIM along with error bars for all methods on each of the different datasets. Note that higher is better. We see that the datasets have different winning methods.
Figure 23:
TiNeuVox
DeformableG'
STG-decoder
1.0
3DGS
EffGS
4DGS
0.8
Iphone
nerfles
hypernert
nerfds
Figure 24: Foreground-Only MS-SSIMs Evaluation (t).
0.6
TiNeuVox
EffGS
STG-decoder
3DGS
RTGS
STG
0.5
DeformableGS
4DGS
VS-SSSM-3app
0.4
0.3
0.2
0.1
0.0
iphone
nerfies
hypernerf
nerfds
Figure 25: Train-Test Performance Gaps. We show the difference between the average MS-SSIM on the
train and test set, where larger gap indicates more overfitting to the training sequence.
23

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Published in Transactions on Machine Learning Research (06/2025)
A.2.4
Train Time and FPS
TiNeuVox
ERGGS
STG-decoder
15000
3DGS
RTGS
STG
TrainTime (s)
Deformable(15
4DGGS
10000
5000
'phone
nerfies
bypernerf
nerfds
dnerf
Figure 26: Per-dataset Quantitative Results. Figure shows the test set Train Time along with error bars for all methods on each of the different datasets. Note that lower is better. We see that the datasets have different winning methods.
TiNcuVox
BRGGS
STG-dccoder
400
3DGS
RTGS
STG
Deformable(1S
300
FPS
200
iphone
nerfies
hypernerf
nerfds
doerf
Figure 27: Per-dataset Quantitative Results. Figure shows the test set render FPS along with error bars for all methods on each the different datasets. Note that higher better. We see that the datasets have different winning methods.
A.2.5 Relative Gap vs. Absolute Performance on NeRF-DS dataset
In section 4.4 we noted that refloctive objects cause consistent difficulties for all methods, thereby "flattening" performance disparities. By "flattening, we were referring to the relalive gaps among the methods, rather than an absolute drop in metrics. Because each method struggles similarly with specularities, the difference in performance between methods shrinks, and no single approach outperforms the others by a large margin. However, this Appendix section shows that, in absolute sense, average-quality measures (PSNR, SSIM, etc.) on NeRF-DS do not degrade as dramatically as one might expect. We attribute this to two factors:
1. Static Background Influence. Like many datasets, NeRF-DS contains substantial non-reflective or relatively static image regions. Even with reflections, each method can still capture those simpler background regions well enough that the scene-level averages do not plummet.
Global Averaging Subtle Artifacts. Reflective artifacts often occur in smaller foreground regions. While these artifacts degrade local image quality, they may have limited effect on global metrics averaged over entire test frames. As a result, the overall PSNR/SSIA may remain comparable to those on other datasets.
short, the "flattening" reflects how the presence of specularities hinders every method similarly, reducing the relative performance spreads. Yet, on the absolute scale, large swathes of non-reflective or simpler regions help keep dat averages from showing dramatic overall performance drop.
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Published in Transactions on Machine Learning Research (06/2025)
A.3
Ablations for Instructive Dataset
Figures
28
and contain ablation heatmaps for PSNR, SSIM, MS-SSIM, and their masked variants. 29 31
and 34 show the aggregate metrics and rankings. The rankings are similar to those in We note that 3DGS often ends up higher in the ranking for pixel-difference metrics mPSNR, mSSIM, MMM-S than mLPIPS likely due to averaging with strong performance on the static part of the dataset. (motion range- 0) and pixel- -ixee-difeeeece metrics' propensity to overfit.
Figures
30
fig. 14
A.3.1 LPIPS and mLPIPS
Average LPIPS for EffGS
Average LPIPS for RTGS
Average LPIPS for 4DGS
Average LPIPS for DeformableGS
030
0.61
0.14
Camera's baseline
Camera's baseline
Camera's baseline
Camera's baseline
0.32
0.44
0.62
0.28
0.40
0.67
0.06
0.12
0.42
6.0
0.05
0.08
0.35
6M
0.19
0.26
0.61
10:0
0.30
0.28
0.52
8:30
0.03
0.07
0.25
1039
0.03
0.06
0.13
8.23
022
10
0.09
0.10
0.30
10
0.13
0.18
0.54
19
0.02
0.04
0.09
10
0.01
0.03
0.07
20
20
625
20
625
20
0.01
635
0.0
5.0
10.0
0.0
5.0
10.0
0,0
5.0
10.0
0.0
5.0
10.0
Motion range
Motion range
Motion range
range
Average LPIPS for 3DGS
Average LPIPS for STG
Average LPIPS for STG-decoder
Average LPIPS for TiNeuVox
Camera's baseline
0.12
0.43
0.60
Camera's baseline
1643
Camera's baseline
0.40
0.38
0.55
0.4
Camera's baseline
0.34
0.27
0.21
645
3
0.06
0.12
0.36
m
0.13
0.17
0.30
m
0.16
0.13
0.20
8.b
1.30
5
8:30
5
10
0.91
0.05
10
0.20
0.35
0.40
0.06
0.13
10
0.11
0.09
0:13
6:20
0.0
620
20
0.01
0.04
0.11
20
0.17
0.35
0.38
0.07
0.03
0.06
20
0.17
0.17
0.16
5.0
10.0
5.0
10.0
10.0
Motion range
Motion range
Motion range
Motion range
Average mLPIPS for EffGS
Average mLPIPS for RTGS
Average mLPIPS for 4DGS
Average mLPIPS for DefommabeeGS
0.80
0.69
1.02
0.60
0.70
0.78
6.17
0.66
0.89
0.14
0.43
0.81
Camera baseline
Camera's baseline
Camera's baseline
Camera's baseline
3
0.40
0.66
0.93
0.23
0.40
0.73
m
0.07
0.29
0.04
0.50
5
0.31
0.23
0.05
10
0.11
0.13
0.45
0.13
0.23
0.69
0.0.
0.13
0.23
0.03
0.10
0.27
20
0.40
0.27
0.42
20
0.10
0.17
0.36
20
0.02
0.06
0.14
20
0.02
0.00
0.19
0.0
5.0
10.0
0.0
5.0
10.0
5.0
10.0
0.0
5.0
10.0
Motion range
Motion range
Motion range
Motion range
Average mLPIPS for 3DGS
Average mLPIPS for STG
Average mLPIPS for STG-decoder Average mLPIPS for TiNeuVox
0.19
0.80
0.92
0.35
0.46
0.64
0.48
0.39
0.42
Camera's baseline
Camera's 's baseline
Camera's baseline
Camera's beseline
0.13
0.43
0.75
0.13
0.31
0.45
0.19
0.28
0.38
in
5
0.07
10
0.04
0.23
0.34
0.17
0.25
0.50
0.04
0.15
0.29
10
0.07
0.15
0.25
20
0.03
0.19
0.32
20
0.02
0.22
0.46
20
0.02
0.11
0.21
20
0.11
0.26
0.26
0.0
10.0
5.0
10.0
5.0
5.0
Motion range
Motion range
Motion range
Motion range
Figure 28: Individual ablations for the instructive dataset, measured by LPIPS. The figure shows camera baseline and motion range ablations for each method separately. Top Two Rows: LPIPS heatmaps. Bottom Two Rows: mLPIPSJ heatmaps. On average the reconstruction becomes harder with increase of the motion range (left to right) and with decrease of the camera baseline (bottom top). Missing values indicate non-converging model.
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Published in Transactions on Machine Learning Research (06/2025)
A.3.2
PSNR and mPSNR
Average PSNR
EffGS
Average PSNR RTGS
Average PSNR for 4DGS
Average PSNR for DeformableGS
15.80
15.15
12.58
12.42
12.52
10.54
18.85
17.48
11.90
22,49
22:56
18.40
Camera's baseline
Camera's baseline 10
Camera's baseline
3
22.50
20.27
19.12
m
23.64
19.91
12.09
28.39
25.30
30
19.34
27.99
25.59
22.22
in
in
Camera's
10
29.05
27.57
23.69
30.05
26.91
20.51
36.79
23.87
27.87
10
37.02
30.55
29.08
20
24.69
27.63
24.73
31.39
27.05
24.02
20
40.39
33.69
30.61
20
42.09
34,96
31.21
5.0
Motion range
Motion range
Motion range
Motion range
Average PSNR for 3DGS
Average PSNR for STG
Average PSNR for STG-decoder
Average PSNR for TiNeuVox
20.77
13.51
26.32
Camera's baseline
Camera's baseline
Camera's baseline
Camera's baseline
27.26
25.16
20.56
25.95
24.53 11.51
21.
30.02
27.10
26.23
32.45
27.01
24.59
in
29.61
26.52
23.91
33.84
27.76
27.23
10
37.21
30.
27.77
20 10
26.28
24.59
23.05
35.1
29.86
26.93
10
31.84
28.95
20
40.53
20
20
29.34
0.0
5.0
10.0
0.0
5.0
10.0
0.0
5.0
10.0
0.0
5.0
10.0
Motion range
Motion range
Motion range
Motion range
Average mPSNR for EffGS
Average mPSNR for RTGS
Average mPSNR for 4DGS
Average mPSNR for DeformableGS
15.53
11.95
15.65
Camera's baseline
Camera's baseline
Camera's baseline
Camera's baseline
17.77
15.45
15.27
19.01
16.79
11.18
28.08
17.20
16:20
31.14
17.75
16,44 46
19.65
17:27
15.19
17.60
17.57
15.68
29.31
17.20
16.81
31.19
17. 17.33
18.05
22
10
10
15.57
10
18.57
10
37.20
20
10.65
19.10
16.70
20
27.66
19.30
17.32
20
32.97
21.24
10.34
20
34,29
21.67
10.52
0.0
5.0
10.0
0.0
5.0
10.0
0.0
5.0
10.0
0.0
5.0
10.0
Motion range
Motion range
Motion range
Motion range
Average mPSNR for 3DGS
Average mPSNR STG
Average mPSNR for STG-decoder Average mPSNR for TiNeuVox
78.58
15.17
17.01
18.02
Camera's baseline
Camera's baseline
Camera's baseline
Camera's baseline
29.34
16.87
15.96
22.25
16.09
16.27
22.34
16.98
17.17
30.23
17.52
16.42
23.51
16.26
16.47
24.58
16.42
17.05
10
31.53
19.08
17.40
10
23.23
17.70
16.80
20 10
27.06
17.47
16.30
29.18
20.18
14.24
20
33.53
20
20
0.0
5.0
10.0
0.0
5.0
10.0
0.0
5.0
10.0
0.0
5.0
10.0
Motion range
Motion range
Motion range
Motion range
Figure 29: Individual ablations for the instructive dataset, measured by PSNR. The figure shows camera baseline and motion range ablations for each method separately. Top Two Rows: PSNR heatmaps. Bottom Two Rows: mPSNRt heatmaps. On average the reconstruction becomes harder with increase of the motion range (left to right) and with decrease of the camera bascling (bottom to top). Missing values indicate a non-converging model.
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Average PSNR over all methods and scenes
19.73
19.27
15.76
Average
Camera baseline
Camera baneère Camera baselines
m
in
26.04
23.60
10
33.63
28.95
26.18
20
35.66
31.19
28.00
STG-demide
1h26
Detormstinss TiMeists
0.0
5.0
10.0
Motion range
Average mPSNR all methods and
19.56
15.59
14.39
Average mPSNR method
Camera* s baseline
Camera benelite x
24.27
16. 73
15.50
camera baaeditas
25.15
17.08
16.52
10
27.06
16.63
17.00
20
29.62
20.26
Method
0.0
5.0
10.0
Motion range
Figure 30: Results on our instructive synthetic dataset. Top Left: PSNR1 heatmap shows how camera baseline and object motion range affect performance across all methods on average. Increasing object motion range (left to right.) affects reconstruction performance negatively; decreasing camera baseline (bottom to top) has the same effect. Top Right: Ranking methods by average PSNR over all scenes. Solid bars represent score computed over all scenes. Textured bar represents metrics computed wide camera baselines. Bottom Left: Masked PSNR* heatmap: with the focus on dynamic object reconstruction, the metries become worse on average. Bottom Right: Ranking for masked PSNR.
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Published in Transactions on Machine Learning Research (06/2025)
A.3.3
SSIM and mSSIM
Average SSIM for EffGS
Average SSIM for RTGS
Average SSIM 4DGS
Average SSIM for DeformableGS
0.47
9.44
0.49
0.19
0.26
0.27
0.73
0.55
0.43
0.82
0.59
0.52
Camera's baseline
Camera's baseline
10s
Camera's baseline
Camera's baseline
00
3
0.57
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m
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m
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m
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Motion range
Motion range
Motion range
Motion range
Average SSIM for 3DGS
Average SSIM for STG
Average SSIM for STG STG-decoder
Average SSIM for TiNeuVox
0.93
Camera's is baseline
Camera's baseline
Camera's baseline
Camera's baseline
0.96
0.93
0.72
0.89
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0.82
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0.69
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-01
10
0.98
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10
0.77
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10
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..4
20
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0.
0.92
20
0:75
20
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20
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0.0
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range
Motion range
Motion range
Motion range
Average mSSIM for EffGS
Average mS5IM for RTGS
Average mSSIM for 4DGS
Average mSSIM for DeformableGS
0.14
0.15
Camera's baseline
Camera's baseline
Camera's baseline
Camera's baseline
0:24
0.13
0.15
0.24
0.17
0.11
0.70
0.15
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0.83
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85
0.37
0.21
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0.28
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0.68
0.13
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0.74
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-04
-04
10
0.38
0.27
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0.60
0.28
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10
0.84
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0.82
0.33
0.26
20
20
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20
0.0
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Motion range
Motion range
Mation range
Motion range
Average mSSIM for 3DGS
Average mSSIM STG
Average mSSIM STG-decoder
Average MSSIM TiNeuVox
0.57
0.14
0.14
0.35
0.17
0.11
0.23
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0.16
Camera's baseline
Camera's baseline
Camera's baseline
0.67
0.18
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Camera's
10
0.79
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10.0
Motion range
Motion range
Motion range
Motion range
Figure 31: Individual ablations for the instructive dataset, measured by SSIM. The figure shows camera baseline and motion range ablations for each method separately. Top Two Rows: SSIM heatmaps. Bottom Two Rows: mSSIMf heatmaps. On average the reconstruction becomes harder with increase of the motion range (left to right) and with decrease of the camera baseline (bottom to top). Missing values indicate a non-converging model.
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Average SSIM over all methods and scenes
0.63
0.54
0:47
Average
Camera's baseline
Camera baselie 5
0.84
0.76
0.60
Allcerera basslies
0.88
10
20
0.92
0.90
0.86
DeMemadeez
0.0
5.0
10.0
Motion range
Average mSSIM over all methods and scenes
05
0.31
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0.13
Average MSSIM by method
Camera's baseline
baaditee
0.52
0.15
1.14
0.57
0.16
0.18
MNSSI
0.71
0.29
0.22
20
0.80
0.38
0.22
67A
PMCS
RIGS
3066
Deformntncs
Method
0.0
5.0
10.0
Motion range
instructive synthetic datasct. Top Left: SSIM heatmap shows how camera baseline and object motion range affect performance across all methods on average. Increasing object motion range (left to right) affects reconstruction performance negatively; decreasing camera baseline (boltom to top) has the same effect. Top Right: Ranking of methods by average SSTM over all scenes. Solid bars represent. score computed over all scenes. Textured bar represents metrics computed on wide camera baselines. Bottom Left: Masked SSIM heatmap: with the focus on dynamic object reconstruction, the metrics become worse average. Bottom Right: Ranking for masked SSIM.
Figure 32: Results
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A.3.4
MS-S5IM
Average MS- SSIM for EttGS
Average RS-- for RTGS
Average RS-SSIMI for 4DGS Average MS SSIM for DeformableGS
Camera's baseline
0.34
0.25
0.30
Camera's baseline
0.21
0.23
0.17
Camera's baseline
0.84
0.53
0.29
Camera's baseline
0.90
0.62
6.44
10
0.57
0.39
m
0.99
0.55
?
0.53
in
0.73
0.61
in
0.80
0.99
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10
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10
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Motion range
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Motion range
Average MS- SSIM for 3DGS
Average MS SSIM for STG
Average MS SSIM for STGodeccodervvrrgee MS SSIM for TiNeuVox
0.97
0.59
0.37
0.64
0.65
0.45
0.67
0.73
0.67
Camera baseline
65
Camera's baseline
63
Camera's is baseline
Camera's baseline
3
0:93
m
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10
1.00
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30
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range
Motion range
Motion range
Average mMS-55IM for EffGS
Average mMS-5SIM for RTGS
Average mMS-55IM for 4DGS
Average MMS-S5IM for DeformableGS
Camera's bazeline
Camera's baseline
Camera's bazeline
0.17
Camera's baseline
0.34
0.24
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range
range
Motion range
range
Average mMS-55IM for 3DGS
Average mMS-55IM for STG
Average MMS-5SIM for STG-decoder
Average mMS-55IM for TINeuVok
Camera's bazeline
0.23
Camera's baseline
Camera's baseline
Camera's baseline
0,02
0.20
0.76
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Motion range
Motion range
Motion range
Motion range
Figure 33: Individual ablations for the instructive dataset, measured by MS-SSIM. The figure shows camera baseline and motion range ablations for each method separately. Top Two Rows: MS-SSIM heatmaps. Bottom Two Rows: mMS-SSIM heatmaps. On average the reconstruction becomes harder with increase of the motion range (left to right) and with decrease of the camera bascline (bottom to top). Missing values indicate a non-converging model.
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Average MS-SSIM over all methods and scenes
0.65
0.51
0.39
Camera's baseline
0.88
0.77
carrera baodines
20
0.94
0.94
0.87
Method
0.0
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10.0
Motion range
Average mMS-SSIM over all methods and scenes
0.53
0.30
0.22
Average mMS-S5IM by method
Camera's baseline
Cemera baaditee
0.7.
0.42
0.28
0.78
0.46
0.36
0.87
0.60
0.42
20
0.89
0.70
0.52
676
PMCS
R7GS
4365 6T6 drowdr
TIMeeAte Deformatenss
Method
0.0
5.0
10.0
Motion range
instructive synthetic dataset. Left: MS-SSIM heatmap shows how camera baseline and object motion range affect performance across all methods on average. Increasing object motion range (left to right) affects reconstruction performance negatively; decreasing camera baseline (boltom to top) has the same effect. Right: Ranking of methods by average MS-SSTM over all scenes. Solid bars represent. score computed over all scenes. Textured bar represents metrics computed on wide camera baselines. Bottom Left: Masked MS-SSIMI heatmap: with the focus on dynamic object reconstruction, the metrics become worse on average. Bottom Right: Ranking for masked MS-SSIM.
Figure 34: Results
B Additional Data-related Details
Our work shows a greater set of comparisons than existing works as they typically only report on a subset of the data by selection (Table
Table 8: Previous works typically only report subset of data.
Method
D-NeRF Nerfles HyperNeRF NeRF-DS iPhone
Yang et al.
2024
x
X
X
RTGS
Ours
/
/
/
/
Yang al. Ours
2023
X
/
X
DeformableGS
Wu et al.
2023
X
of 13
X
4DGS
Ours
/
Katsumata et al.
2023
X
4 of 13
X
X
BRGS
Ours
J
Li et al.
2023a
STG
Ours
/
/
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B.1 Dataset Descriptions
D-NeRF (Pumarola et al. (2020)) is synthetic dataset containing eight single-object scenes captured by 360-orbit inward-facing cameras. Test views are rendered at unseen angles. The test set is misaligned in the lego sequence, and so use lego's validation set for testing instead following Yang et al. 2023
Nerfies (Park et al. (2021a)) and HyperNeRRF (Park et al. (2021b)) contain general real-world scenes of kitchen table top actions, human faces, and outdoor animals. Thus far, most methods (Wu al. 2023 Fang et al., 2022 report results four sequences from the 'vrig' split of HyperNeRF dataset that uses second test camera
to the first, rather than the 'interp' split to render held-out frames from single camera (an easier task). Some works report their own sequence splits from HyperNeRF (Kratimenos et al.,
2023 We include all 17 HyperNeRF sequences and 4 Nerfies sequences unless otherwise noted.
NeRF-DS (Yan et al. (003)) contains many reflective surfaces in motion, such as silver jugs or glazed ceramic plates held by human hands in indoor tabletop scenes. This contains seven sequences. Test sequences are again generated by sccond rigidly-attached camera. Lastly, the iPhone (Gao et al. (2022)) datasct's 14 scenes include large dynamic objects often undergoing large motions, with relatively small camera motions.
Test views are from two static witness cameras with a large baseline difference.
We do not include the NVIDIA Dynamic Scene dataset (Yoon et al. 2020) because they sub-sample fake monocular camera sequences from a 12-camera wide baseline setup. degree of scene motion between frames.
This creates an unrealistically large
B.2 Erroneous Camera Pose
The HyperNeRF data has known bad camera poses. To quantify the camera pose error's effect on reconstruction quality, we improve the camera poses in HyperNeRR by segmenting out moving objects and highly specular regions and rerunning COLMAP; we report these experiments result in Appendix [3.2]
Camera pose is required input for most dynamic Gaussians methods for monocular videos. However, it's difficult to measure precise poses with real-world dynamic videos, leading to pose errors. Erroneous camera poses can be detected by simply applying static Gaussian Splatting Kerbl et al. (2023 to dynamic frames. If we observe blurry static background renderings as output, then this indicates erroneous camera poses.
HyperNeRF (Par et al., 2021b) is dataset that suffers badly from camera pose inaccuracy, as dynamic regions were not masked for COLMAP (Schöuberger & Frahm 2016) during their camera pose estimation. To address this problem, we use SAM-Track (Cheng al. (222)) plus human labor to mask dynamic, reflective and textureless regions, and then rerun COLMAP with these masks and customized arguments for each scene. Figure 35 shows improved static Gaussian rendering in background areas. Out of Hyper.eRRF's 17 scenes, we improve the camera poses in 7 scenes and slightly improve them in 3 scenes. We evaluate the pose quality by reporting mPSNR of static 3DGS on static regions, using the inverse of our motion masks, as summarized in Table
We use No Diff/ /Wore group's 7 sequences from fixed and corresponding 7 sequences from original HyperNeRF dataset to show the effect of camera posc inaccuracy on reconstruction (Figures 36
37
38
39
Performance is improved after pose fixes across for EffGS, STGs and RTGS. But, even though static regions are now deblurred in the standard 3DGS rendering showing improved poses, surprisingly not all method improve: TiNeu DeformableGS, and 4DGS instead often become worse.
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Before
After
Ground- Ghound-Truth Truth
Figure 35: Comparison of ground-truth and static Gaussian Splatting (Kerbl et al. 2023 results on before and after correcting the camera poses for HyperNeRF (Park et al. 2021b) scenes (vrig-chicken, slico-banan banana, vrig-3dprinter, and keyboard).
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Result
Ratio Used
Scene
Original Poses mPSNR.
Corrected Poses mPSNR
2x
chickchicken
22.290
23.503
2x
cut-lemon1
24.053
24.809
1x
keyboard
22.952
24.831
Better
2x
slice-banana
22.794
24.690
4x
tamping
20.817
21.671
1x
vrig-chicken
23.334
26.075
1x
vrig-3dprinter
17.685
22.118
2x
aleks-teapot
22.343
22.543
Slightly Better
1x
broom2
21.987
22.810
2x
cross-handsl
19.552
19.987
1x
americano
24.348
24.418
1x
espresso
22.707
22.291
2x
handl-dense-v2
25.547
25. 25.214
No Diff/ Worse
1x
oven-mills
25.565
19.891
1x
split-cookie
24.389
24.277
2x
torchocolate
23.345
18.356
2x
vrig-peel-banana
23.427
22.251
Table 9: Static region masked PSNR results for HyperNeRRP scenes before and after correcting the camera
poses.
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TiNeuVox
3DG5
DeformableGS
21
EffGS
RTGS
22
4DGS
STG-decoder
STG
fixed
hypernerf
Figure 36: PSNR the same set of HyperNeRE sequences to show Camera inaccuracy's effect on recon- struction. Higher is better.
0.800
TiNeuVox
0.775
3DGS
DeformableGS
SSIM
0.725
EffGS
RTGS
0.700
4DGS
0.675
STG-decoder
STG
0.650
fixed
hypemerf
Figure 37: SSIM on the same sel of HyperNeRR sequences to show Camera inaccuracy's effect on reconstruction.
Higher better.
0.90
ToNeuVox
0.85
3DGS
MS-SSIM
DeformableGS
EffGS
RTGS
0.75
4DGS
STG-decoder
STG
0.70
fixed
hypermert
Figure 38: MS-SSIM on the same set of HyperNeRF sequences to show Camera inaccuracy's effect on reconstruction. Higher is better.
B.3 Camera Pose Sensitivity Study on Instructive Dataset
We perform an additional study similar Appendix B.2 where introduce rotational noise into our instructive datasct's 's camera poses. Namely, we rotate the camera by U[-a, al where a E {19,5% We perform the study on non-rotating cube sequences.
Overall results of all methods in Table 10 indicates that there is a significant drop in quality (>3dB PSNR) between 0 noise and 1 Further increasing the noise to 5" degrades most metrics as well, but not as dramatically. Qualitatively, we observe (see Figure 41 a jumping camera effect, that demonstrates the overfit. happening due to the noise, and blurriness of the background.
Per-method analysis in Table 11 shows that although the noise largely does not affect the ranking of the methods by average performance (Figure 40), we observe a certain flattening of PSNR results, where the gap between best-performing methods like DeformableGS, and the worst ones tightens. At the same time, degradation for LPIPS and SSIM stays more uniform across the board.
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0.45
TiNeuVox
3DGS
0.40
DeformableGS
LPIPS
0.35
EffGS
0.30
RTGS
4DGS
0.25
STGG-decoder
0.20
STG
fxed
hypernerf
Figure 39: LPIPS on the same set of HyperNeRF sequences to show Camera inaccuracy's effect on recon- struction. Lower is better.
Perturbation
PSNR f
PSNR
SSIM t
A SSIM
LPIPS 4
A LPIPS
Angle
0"
25.97
0.00
0.78
0.00
0.23
0.00
1
22.65
-3.32
0.55
-0.23
0.31
+0.08
5"
22.78
-3.19
0.54
-0.24
0.37
+0.14
Table 10: Performance metrics at different perturbation angles with delta from baseline (0" noise).
PSNR 1
SSIM f
LPIPS 1
Method
Angle
Value
A
Value
A
Value
A
0'
29.41
0.00
0.87
0.00
0.12
0.00
DeformableGS
1
24.92
-4.49
0.58
-0.29
0.17
+0.05
5'
24.66
-4.75
0.57
-0.30
0.21
+0.09
27.71
0.00
0.87
0.00
0.16
0.00
3DGS
1
22.52
-5.19
0.56
-0.31
0.28
+0.12
5
22.61
-5.10
0.53
-0.34
0.38
+0.22
0'
27.26
0.00
0.84
0.00
0.16
0.00
4DGS
1 22.92
-4.34
0.56
-0.28
0.23
+0.07
5'
23.98
-3.28
0.56
-0.28
0.24
+0.08
0"
27.29
0.00
0.83
0.00
0.18
0.00
STG-decoder
1.
24.24
-3.05
0.58
-0.25
0.27
+0.09
5"
23.63
-3.66
0.56
-0.27
0.34
+0.16
22.55
0.00
0.63
0.00
0.36
0.00
ERGSS
1a 21.85
-0.70
0.55
-0.08
0.40
+0.04
5
22.42
-0.13
0.55
-0.08
0.45
+0.09
(
21.00
0.00
0.60
0.00
0.40
0.00
RTGS
1 19.22
-1.78
0.47
-0.13
0.49
+0.09
5"
19.87
-1.13
0.47
-0.13
0.54
+0.14
0"
7.51
0.00
0.21
0.00
0.80
0.00
STG
1º 9.26
+1.75
0.22
+0.01
0.75
-0.05
5
5.55
-1.6
0.14
-0.07
0.87
+0.07
Table 11: Performance metrics by method at different perturbation angles with delta from baseline (0" noise).
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PSNR
30-
25
25
PSNR
22-
PSNR
PSNR
500
4025
Flededdd
300
3003
4000
Methad
Method
Method
(a) PSNR rankings by angle noise
Aversue SSIM by method for perlutbation angll
Auer SSIM by moeitod for ove lus ba rio ana w
Averrage SSIM by method for pertarbution angle
SSIM
SSIM
5SIM
0.4
0.2
0.2
0.7
0.0
64
m02
Ti-drrddr
odemaaless
5008
4005
500
3000
4035
Ft rodddd
Method
Method
Method
(b) SSTM rankings by angle noise
LPIPS method perturiaation
LPIPS perturbation
3.2
0.5
0.8
LPIPS
0.7
0.5
LPIPS
0.6
610
102
modcodr
2005
De00 malllss
3005
stodeoddr
310
3003
1005
Det MAMMAG
Method
Method
Method
(c) LPIPS rankings by angle noise
Figure 40: Rotational pose noise influence over methods ranking. We observe that although the pose noise is influencing the absolute metrics, the rankings themselves mostly unchanged.
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(a) Clean pose
(b) Noisy pose
Figure 41: Qualitative effect of adding rotational noise to the pose. Left: Reconstruction using clean
pose. Right: Reconstruction using di-distorted poses. Both reconstructions are performed with DeformableGS.
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C Experimental Implementation
C.1 Shared Hyperparameters
Most of GS-related hyperparameters are inherited from 3DGS paper, and identical among algorithms with
objective function (Equation (4):
C 1 prod 1 + ASSIM (1 (SIM(ppred: 4z) ASSIM 0.2
(4)
For learning rales, people set spherical harmonics feature learning rate as 0.0025, opacity learning rale 0.05, scaling learning rate as 0.005, rotation learning rate as 0.001, position learning rate as 0.00016 that exponentially decrease to 0.0000016 with learning rate delay 0.01, after 30000 iterations.
Gaussian densification starts from 500th training iteration, and ends until 15000th training iteration. Densification gradient threshold is set to 0.0002. The spherical harmonics (SH) degree is set to be 3 in 3DGS, percent dense is set to be 0.01.
To align with dynamic Gaussian works' implementation, a few hyperparameter might be different from 3DGS default setting:
Batch size 3DGS's default setting is to use batch size during training. RTGS uses 4 for real-world scenes, 4DGS uses 2 for real-world scenes, and ApacceiimGGausinnn use 2 for all scenes.
Warm up stage DeformableGs, 4DGS and EffGS first fix the motion component, and train the static part for the first 3000 steps.
Opacity reset During training, 3DGS set Gt's opacity o, to 0 periodically every 3000 iterations during densification phase. 4DGS disable this opacity reset action when dealing with real-world sequences.
Initialization Original 3DGS suggests initialize G,s with 100, 000 random points uniformly sampled in space for synthetic scenes, and with structure-fromd footuurrefrommooiiio (SfM) (Schönberger & Frahm 2016) point cloud for real-world scenes. RTGS suggests appending extra uniformly-sampe
random point cloud to SfM point of SfM point cloud for the same dataset. We follow 3DGS's suggestion.
cloud for a multi-view roconstruction dataset (Li et al.) 2022). EffGS uses random point cloud in the place
Apart from shared hyperparameters, each motion model also introduces extra hyperparameters.
C.2 Extra Hyperparameters: Deformation
Deformation Learning Rate starts from 0.00016, and exponentially decays to 0.0000016 with delay multiplier 0.01 after certain steps. Deformation happens on position 24, scale 8; and rotation qi, with ds, a unchanged across time.
DeformableGS (Yang et al. 2023 Following original paper, we set network depth as 8; network width as 256; time embedding dim 6 for synthetic, 10 for real-world; position embedding dim as 10; time is additionally processed with an embedding MLP with 3 layers, 256 width before feed into deformation network for synthetic scene; exponential decay max step as 40000.
4DGS (Wu et al., 2023 Following original paper, we set network depth as for synthetic, for real-world; width as 64 for hetic, 128 for real-workd, plane resolution as [1, 2] for synthetic, 21, 4] for real-world; exponential decay max step as 30000; plane TV loss weight as 0.0001 for synthetic, 0.0002 for real-world; time smoothness loss weight as 0.01 for synthetic, 0.0001 for real-world; L1 time planes loss weight as 0.0001; Grid Learning Rate similarly exponentially decay like Deformation Learning Rate, except for going from 0.0016 to 0.000016; grid dimension as 2, imput coordinate dimension as 4, output coordinate dimension as 16 for synthetic, 32 for real, grid resolution as [64, 64, 64, 100] for synthetic, [64, 64, 64, 150] for real.
To stabilize training of deformation networks, perform gradient norm clipping by 5.
C.3 Extra Hyperparameters: Curve
EffGS (Kasummta et al. 2023) uses Fourier Curve for z; motion, and Polynomial Curve for qi motion. Curve order is set to 2 for x;, I for ge- An optical-low--based loss is additionally introduced to augment eq. only for multi-view dataset, and here we do not include the flow loss as default.
STG (Li et al. 2023a] Original code is only supporting multi-view dataset as Gaussians are initialized by per-frame dense point cloud. To exlend to monocular case, we copy point cloud for 10 times along time axis uniformly. During rendering, one option is to directly rasterize SH appearance as in 3DGS, but another
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Published in Transactions on Machine Learning Research (06/2025)
option is to rasterize feature instead, which would be later fed into decoder network D to generate color
image. D's trained alongside with learning rate 0.0001.
C.4 Details of Dynamic 3D Gaussian Implementation
We update the codebase from Luiten et al. 2023) to support our evaluation across datascts. We use the HyperNeRRP scenes with our improved cameras poses to minimize the reconstruction of static areas.
D Mathematical Motion Model Definitions
D.1 Polynomial
Curve methods (Katsumata et al. (2023) Lin et al. (2023 et al. (2023l) may use a polynomial basis of order to define a I over time that determines Gaussian offsets, with coefficients ai and time offset f;z Here, we denote I. of G; as the subset of parameters to change:
f(i,t) = 2 (t tz)
1=0
(5)
G = Σ ais tt) + G,
5=0
D.2 Fourier
f could also use Fourier basis with coefficients aij. ai.jbaaj = 04, and time offset 4;
G Σ (oi t4)) biocc - t,))) + G,
(6)
D.3 RBF
f could be Gaussian Radial Basis function with scaling factor a and time offsct t;
Gi = G, exp(-c;(t t2)
(7)
D.4
RTGS's 3D Rendering
RTGS Yang el al. 2024 induces 3D world from 4D representation by obtaining the distribution of a 3D
position conditioned on a specific time t:
Tjjt = az[: 3] + E 3J2, (3,)
(8)
Dglt = [ 3, 3] -EJ 3, 3) [3 3]
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Published in Transactions on Machine Learning Research (06/2025)
E
Related Work
Dynamic scene reconstruction has been a longstanding problem in computer vision (Newcombe et al,, Slavcheva et al.) 2017; Li et al. 2023b; Luiten et al. 2023). Earlier works utilize accurate depth cameras (Newcombe et al.) 2015 Slavcheva et al., 2017) and reconstruct dynamic scene using monocular RGBD video. On the other hand, Joo et al. (2014) propose to use MAP visibility estimation with a large number of cameras and correspondences from optical flow to reconstruct dynamic scene. In contrast, the focus of our study is on using a single-view RGB video to reconstruct dynamic scenes without taking depth or correspondences as imput.
2015:
(NeRFs) revolutionized 3D reconstruction allowing for the reconstruction of a static 3D scene only given 2D input images. This succoss led to various extensions
Recently, neural radiance fields (Mildenhall et al. 2020
onto the dynamic setting, i.e, the 4D domain (Ramasinghe et al.) 2024 Wang et al. 2021 Song et al. 2023
Li et al.
2021 Gao et al. 2021: 2022 Cao & Johnson| 2023 [ridovich-Kell et al. 2023; Bui et al. 2023 Jang
& Kim 2022 by treating time as an additional dimension in the neural field. Other relevant approaches
include combining a 3D NeRF representation with a learned time-dependent deformation field (Liu et al.
2023; Fang et al. 2022 Park et al. 2021a Johnson et al.) 2023 Kirschstein et al.] 2023 Wang al.) 2023
Tretschk ct al. 2020 and lcarning a set of motion basis [Li ct al. 2023b) to optimize a dynamic seene.
2023 Keselman & The more recent explosion of works representing scenes with Gaussians (Kebll et al.
Hebert 2022 Chen & Wang 2024) such as 3D Gaussian Splatting (3DGS) (Kerbl et al. 2023 has led to
a number of promising extensions to its scene representation into the 4D domain which is the main focus of study in our work. As discussed in the earlier sections, one option to extend static 3DGS is to model motion as 3D trajectory through time, learning motion by iteratively optimizing for per-Gaussian offsets into the next frame (Luiten et al.] 2023; [Snn et al. 2024 Duisterhof et al.] 2023). Methods also represent these 3D trajectories differently such as using explicit motion basis (Das et al., 2023 Katsumata et al.) 2023
Lin et al. 2023; Li et al. 2023a Yu et al. (023) or sparse control points (Huang et al.) 2022).. Another line
of works [Wu et al. 2023 Yang et al. 2023 Liang et al.) 2023 Guo et al. 2024 Lu et al. 2024 Liu et al.
2024
Diwen Wan 2024] learn a 3D Gaussians that live in canonical space and optimize a line-conditooned
deformation network to warp Gaussians from canonical space to each timeframe. Lastly, few works (Duan
ct al.
2024 Yang et al. 2024) directly model Gaussians in 4D, i.c. extending across space and time.
F
Discussion on Additional Regularization for Monocular Scenes
Reconstructing 3D scenes from only a monocular video often faces severe ambiguities, especially when camera motions are small or objects move rapidly. Although our analysis in carlier sections suggests that the chosen motion representation strongly inffuences stability, the intrinsic constrrennt nature of monocular data remains a fundamental limitation. As a result, several recent works have introduced additional regularization or supervision signals to guide dynamic Gaussian splatting methods toward more stable solutions.
F.1 Smoothness and Rigid Priors
When motion is unknown unsteady, enforcing physically plausible constraints can help reduce ambiguity. For instance, Luiten et al. (2023) use a suite of smoothness losses that encourage nearby Gaussian points (determined by k-ncarost neighbors) to move in a more rigidly consistent manner, proserving relative distances
over time. Subsequent works [Kratimenos et al. 2023 Lin et al.) 2023 Bac et al. 2024 Stearns al.)
2024
Wang et al. 2024) adopt similar ideas to curb erratic Gaussian transtormations. Mcanwhile, Wu et al. 2023
employ a valitiioiiii regularization on their discretized field, and Lin et al. (2023 ; Lu et al. 2024
both penalize changes in Gaussian parameters across short time windows to encourage piecewise stationarity.
Even in purely 4D models (Duan et al. 2024), such smoothness priors can be extended to higher dimensions by promoting local rigidity and entropy- consistency in opacity.
F.2 Additional Supervision Signals
Instcad of relying solely on RGB reprojection loss, one can incorporate complementary constraints like optical flow, depth, or pixel/particle tracks. Although these signals must themselves be estimated from data polentially introducing noise they can nonetheless reduce ambiguity when monocular cues alone are insufficient.
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Optical Flow. Methods such as Katsumata al. (2023) and Gao et al. (2024) compare rendered flow map to optical flow predictions (e.g., from RAFT ("Teed & Deng 2020) ), adding a loss lerm that ties the 2D motion field to the 3D Gaussian motion.
Depth Supervision. Depth can be rasterized alongside color by projecting Gaussian splats onto depth buffer. Wang et al. (2024) enforce an & penalty between rendered and ground-truth depths, while Liu et al. (2024) incorporate ordinal depth loss handle inconsistencies in monocular depth estimates. Such approaches anchor Gaussians in 3D space more reliably, especially for scenes with limited camera coverage.
Pixel/Partions Tracks. Another variant is to align Gaussian motion with pixel-level correspondences. For instance, Stearns al. (2024) introduce a loss matching 2D pixel trajectories inferred by a separate tracking method. Similarly, Wang et al. (2024) impose consistency between splat trajectories and user-defined automatically derived point tracks, including those along the z-axis.
F.3 Scope and Outlook
Although these regularization or supervision mechanisms can significantly bolster stability, their integration typically requires additional computational overhead and careful tuning. Inferred optical flow or monocular depth may contain its own noise and domain biases. As result, these strategies must be adopted judiciously, balancing the risk of error propagation against the potential benefit of stronger geometric constraints.
In summary, while our work does not focus on adding external priors or supervision, these methods highlight promising avenues for addressing the inherent ambiguities of monocular dynamic reconstruction. They offer valuable tools for future research seeking to further improve the reliability and fidelity of dynamic Gaussian splatting pipelines in challenging, real-world environments.
42


Reviews:
[{"review_id": "djbPlKYPFV", "reviewer": "Reviewer_uwmz", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1. This work is valuable itself. It targets at the problem of lacking fair comparison for various homogenized methods and provides valuable insights through thorough benchmarking.\n2. In general the paper is well-written and clearly clarifies most of its details and findings.\n\n\nWeaknesses:\n1. Some of the experiment settings need to be further clarifed and this may influence the plausibility of the findings. Please refer to the **Requested Changes**.\n2. The findings of the work are extensive and impressive. While the work selects representative methods from different motion model groups, it does not conclusively determine which motion representation is most advantageous. I understand that a method may perform better on one dataset and worse on another, but I would appreciate a more detailed explanation of why certain methods excel. Specifically, is the performance difference directly related to the chosen motion representation? How does the motion representation influence the reconstruction quality? A deeper exploration of these questions would add significant value to the work.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper presents a thorough investigation into dynamic gaussian splatting methods from monocular video. By selecting representative methods with various motion models, the study provides comprehensive comparisons across multiple datasets, including a newly introduced synthetic dataset. The work also proposes several key findings that might be helpful for further research.\n\nThe key contributions are:\n1. Comprehensive Benchmarking and Analysis. The paper sets up a new codebase for fair comparison of different methods across a range of datasets.\n2. Valuable Insights from Benchmarking. For example, the study highlights that the adaptive density control can lead to increased overfitting and occasional optimization failures.\n3. Systematic Categorization of Motion Representations. The paper gives a clear categorization of Gaussian splatting methods based on their motion representations, ranging from per-Gaussian motion models to field-based approaches and even 4D Gaussian representations.\n4. Introduction of a Novel Synthetic Dataset. A new instructive synthetic dataset is introduced, allowing for fine-grained control in camera and object motion.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. **Equation (1) Correction**\n   Update Equation (1) to include the missing index k. Maybe it reads:\n   $$\\prod_{k=1}^{j-1}(1-\\alpha_k p_k(u,v))$$\n   *Impact:* Would strengthen the work.\n2. **Clarification of Motion Model Categorization (Section 3.3)**\n   Could you please explain why EffGS is categorized as using a local low-order polynomial basis while STG, which also employs a 1st-order polynomial basis, is not considered in the same category.\n   *Impact:* Would strengthen the work.\n3. **Details on Implementation of the Single Codebase (Section 3.3)**\n   Section 3.3 states, \"To minimize differences in implementation except for the motion model, we integrate these algorithms into a single codebase.\" Does this mean that all other implementation differences have been standardized or minimized? If so, could you provide more details on how this was achieved—for example, which aspects of the original implementations were unified and how any remaining differences were handled?\n   *Impact:* **Critical** to securing the recommendation.\n4. **Clarification on Dataset Averaging (Section 4.1)**\n   It would be better to change “a summary of the findings, averaged across 5 datasets” to explicitly list which datasets are included—especially noting whether the Instructive synthetic dataset is part of the evaluation.\n   *Impact:* Would strengthen the work.\n5. **Explanation of Discrepancy in Table 3 for RTGS Performance**\n   Table 3: In the original RTGS paper, the authors compare RTGS with TiNeuVox on the D-NeRF dataset and report that RTGS significantly outperforms TiNeuVox. However, your results in Table 2 do not reflect such a pronounced advantage. Could you clarify the reasons behind this discrepancy? This explanation is critical, as it directly impacts the reliability of your Table 2 results and the related findings.\n   *Impact:* **Critical** to securing the recommendation.\n6. **Error Bar Details in Figure 5**\n   Could you provide more information on how error bars were calculated, like the number of runs.\n   *Impact:* Would strengthen the work.\n7. **Reformatting Section 4.4 Paragraphs**\n   It seems that the first and second paragraphs in Section 4.4 should be combined.\n   *Impact:* Would strengthen the work.\n8. **Further Explanation of Efficiency and Overfitting Claims (Section 4.4)**\n   Section 4.4 states, \"(1) the efficiency (in both optimization and rendering) can shift undesirably across scenes, (2) the risk of overfitting increases and (3) optimization occasionally fails completely.\" Could you provide a more detailed explanation of how Figure 6 supports these claims?\n   *Impact:* **Critical** to securing the recommendation.\n9. **Clarification on NeRF-DS Dataset Results (Section 4.4)**\n    Section 4.4 states, \"The only exception is in the NeRF-DS dataset, where we hypothesize that the reflective objects in that data cause issues for all methods, flattening the performance disparities between them.\" However, Appendix A indicates that the methods do not perform significantly worse on NeRF-DS compared to other datasets. Could you explain this discrepancy?\n    *Impact:* **Critical** to securing the recommendation.\n10. **Discussion on Motion Representation Effectiveness**\n    Overall, it is challenging to identify which motion representation is clearly superior. While Section 4.6 provides a clear ranking of the different methods, it does not sufficiently explain why one representation outperforms another. A more detailed discussion of the underlying factors would be highly valuable.\n    *Impact:* Would strengthen the work.\n11. **Title Justification for \"Smooth Motion Helps\"**\n    The title suggests that \"Smooth Motion Helps,\" yet the paper does not clearly demonstrate or explain how smooth motion contributes to improved performance.\n    *Impact:* Would strengthen the work.", "broader_impact_concerns": "No", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "HNg8rRO2nn", "reviewer": "Reviewer_mEie", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1. This paper is well-written with a clear structure.\n2. The paper studies an interesting and consolidated problem about the insufficient evaluation of dynamic Gaussian Splatting methods.\n3. Some findings are inspiring, such as the problem caused by the adaptive density control\n\nWeakness:\n1. Some experimental settings are not so clear. The training sets of different datasets are not clear. As pointed out by DyCheck, most methods adopt the \"telescoped\" camera setting such that these methods use different cameras on different timesteps to create a pseudo monocular video. However, such a telescoped camera does not exist in the real world. Some discussions are missing here.\n2. Synthetic data too simple. The proposed new synthetic dataset only involves a moving cube, which is too simplistic for real-world applications. It is not too difficult to involve some complex motions such as dancing avatars or using physics simulation.\n3. Some claims are not well-supported with rigorous experiments. For example, \"motion locality helps quality\" is demonstrated by the comparison between \"Deformable GS\" and \"EffGS\". However, these two methods adopt different complexity in terms of motion function but not only in using per-gaussian representation or not. It's not so difficult to control all the variables here but only change one factor in experiments for demonstration.\n4. Camera pose rectification is only conducted on HyperNeRF but not on the iPhone dataset. According to my experience, the iPhone dataset involves more severe camera pose errors, which may affect the accuracy of the conclusions.\n5. The incorporation of monocular depth is a trending strategy but is not discussed. Since reconstructing 3D from a monocular video is too ill-posed with small camera motions, there is a trend to adopt recent accurate single-view depth estimation or video depth in the pipeline. It would beneficial to include more discussions.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces a comprehensive and systematic evaluation of recent dynamic Gaussian Splatting methods for the dynamic novel view synthesis task. The evaluation is conducted on 5 commonly-used datasets and 1 new synthetic dataset. The paper claims 8 findings through the systematic evaluation, ranging from the complexity of the motion model to the choice of Gaussian copy or splitting strategies, etc.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "I think the authors are encouraged to provide\n1. A clear discussion about the telescoped monocular setting.\n2. Conduct experiments on slightly more complex synthetic data.\n3. Conduct experiments in a controlled manner and only change one setting (motion complex, motion field, specular objects, etc.) to demonstrate the claims.\n4. Discuss more about the effectiveness of monocular depth estimation in DVS.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "9YZ0xKUYAj", "reviewer": "Reviewer_oxJq", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strength \n- The paper introduces a large-scale and integrated evaluation of several dynamic Gaussian Splating methods on diverse datasets, leading to fair and transparent cross-method comparisons. \n- A new custom synthetic dataset separates major factors (camera baseline, object speed), publishing how each method reacts to these variables under controlled settings. \n- The paper goes beyond the standard metrics to analyze the foreground performance, overfitting preferences, and the role of adaptive Gaussian density for insight into the stability and failure mode of each algorithm. \n- Replicable results build upon their findings by releasing code, improved camera poses, and segmentation masks. \n- The paper concludes that simple, low-dimensional motion models often perform more strongly and that purely monocular settings remain challenging.\n\nWeaknesses \n- The paper contributes to the existing dynamic Gaussian Splatting (approaches by integrating, evaluating, and comparing. However, it does not propose a new technique for dynamic splatting. This lack of a novel method can reduce the technical contribution of the paper, even if the benchmarking itself is valuable. \n- Authors refine the camera pose for HyperNeRF scenes, which can blur the stable background, but they do not systematically measure how each dynamic GS method handles different degrees of pose error. An intensive study (e.g., adding synthetic disturbances to take advantage of scenes with known poses will clarify the strength to maintain uncertainties. \n- Empirical findings of the paper show how different motion representations or adaptive mechanisms may fail. However, this does not present a theoretical examination of why some configurations become unstable in monocular settings, nor does it propose a new stabilization strategy (e.g., additional priors or losses).", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper provides a benchmark of Dynamic Gaussian Splatting methods for monocular view synthesis, unifying several motion models (e.g., polynomial, Fourier, deformation fields, 4D Gaussians) in one codebase and testing them on 50 real-world scenes and a new synthetic dataset. Key findings show that simpler and low-dimensional motion models are more robust, adaptive densification often leads to overfitting, and strictly monocular data remains challenging. A voxel-based baseline (TiNeuVox) outperforms many GS methods in quality but is slow. The paper releases open-source code, improved camera poses, and segmentation masks to enable fair comparisons and guide future research.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- For each dynamic GS method, list the default values ​​, any visual-specific tweaks, and major hyperparameters (e.g., motion-model degree, deformation-field depth, regularization loss weight, and learning rate). \n- Either (1) Add synthetic noise to (1) known ground-truth camera poses (in a scene where you have an accurate pose) or (2) choose some real-world sequences with different levels for these errors and determine flexibility. For example: \n  - Introduce random disturbances to rotation/translation (like a small Gaussian offset) on different quantities (e.g., 0.5 °, 1 °, 3 °). \n  - Compare the final rendering quality (PSNR, SSIM, LPIPS, etc.) before and after the pose disturbances. \n- Discuss potential strategies to improve stability or address ambiguity in monocular dynamic scenes. Examples may include: \n  - Optical flow: incorporating a penalty term that encourages stability consistency with 2D optical flow. \n  - Smoothness / rigid prior: Additional constraints on Gaussian deformation fields so that local neighborhoods move consistently.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper presents a thorough comparative study and analysis of dynamic Gaussian splatting methods from monocular videos. The contribution is timely and provides valuable insights (e.g., the drawbacks of adaptive density control). The systematic categorization of motion and the new synthetic dataset offer fine-grained analysis that cannot be obtained by existing datasets.

The main weakness is that the paper tests individual algorithms as is, without factoring out individual design variables, potentially obscuring the analysis. 

Overall, all three reviewers agree that benchmarking existing dynamic Gaussian splatting algorithms is a good contribution to the community. The authors have also addressed the reviewers' feedback sufficiently. The AE thus recommends to accept the paper.
Decision: Accept (poster)