Uncertainty Matters in Dynamic Gaussian Splatting for Monocular 4D Reconstruction
Authors: Fengzhi Guo
Chih-Chuan Hsu
Sihao Ding
Cheng Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: dynamic Gaussian Splatting
uncertainty estimation
4D reconstruction
graph model
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Uncertainty-aware dynamic Gaussian Splatting framework for monocular 4D reconstruction

Abstract: Reconstructing dynamic 3D scenes from monocular input is fundamentally under-constrained, with ambiguities arising from occlusion and extreme novel views. While dynamic Gaussian Splatting offers an efficient representation, vanilla models optimize all Gaussian primitives uniformly, ignoring whether they are well or poorly observed. This limitation leads to motion drifts under occlusion and degraded synthesis when extrapolating to unseen views. We argue that uncertainty matters: Gaussians with recurring observations across views and time act as reliable anchors to guide motion, whereas those with limited visibility are treated as less reliable. To this end, we introduce USplat4D, a novel Uncertainty-aware dynamic Gaussian Splatting framework that propagates reliable motion cues to enhance 4D reconstruction. Our approach estimates time-varying per-Gaussian uncertainty and leverages it to construct a spatio-temporal graph for uncertainty-aware optimization. Experiments on diverse real and synthetic datasets show that explicitly modeling uncertainty consistently improves dynamic Gaussian Splatting models, yielding more stable geometry under occlusion and high-quality synthesis at extreme viewpoints. Project page: https://tamu-visual-ai.github.io/usplat4d/.

Published as a conference paper at ICLR 2026
UNCERTAINTY MATTERS IN DYNAMIC GAUSSIAN SPLATTING FOR MONOCULAR 4D RECONSTRUCTION
Fengzhi Guo
Chih-Chuan Hsu'
Sihao Ding
Cheng Zhang'
Texas A&M University
Mercedess Benz Research & Development North America
ABSTRACT
Reconstructing dynamic 3D scenes from monocular input is fundamentally under- constrained, with ambiguities arising from occlusion and extreme novel views. While dynamic Gaussian Splatting offers an efficient representation, vanilla mod- els optimize all Gaussian primitives uniformly, ignoring whether they are well or poorly observed. This limitation leads to motion drifts under occlusion and degraded synthesis when extrapolating unseen views. We argue that uncertainty matters: Gaussians with recurring observations across views and time act as reli- able anchors guide motion, whereas those with limited visibility are treated less reliable. To this end, we introduce USPLAT4D, novel Uncertainty-awar dynamic Gaussian Splatting framework that propagates reliable motion cues enhance 4D reconstruction. Our approach estimates time-varying per- Gausiann uncertainty and leverages it to construct spatio-temporal graph for uncertainty- aware optimization. Experiments diverse real and synthetic datasets show that explicitly modeling uncertainty consistently improves dynamic Gaussian Splatting models, yielding more stable geometry under occlusion and high-quality synthesis at extreme viewpoints. Project page: https:/tamu-vviuualla -visunal-viaiiaiii www.ggihuu..o/useee..
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Input Trajectory
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We show challenging from the DyCheck dataset (Gao et al., 2022), where a person casually rotates backpack while being captured by a moving monocular camera. The goal to reconstruct the dynamic object for arbitrary viewpoints and timestamps. The state-of-the-e dynamic Gaussian Splatting methods, e.g.. Shapc-of-Motian (SoM) (Wang ct al., 2025a), struggle on extreme novel views far from the input trajectory, such opposite-side views (view 1) or large angle offsets (view 2). We propose USPLAT4D, an Uncertainty dynamic Gaussian Splatting model that produces more accurate and consistent reconstruction. Left: rendered dynamic scene from our model illustration alongside four sampled RGB inputs. Right: novel view synthesis at two extreme novel viewpoints. Please refer to the project page for clearer visual comparison.
Figure 1:

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Published as a conference paper at ICLR 2026
INTRODUCTION
Reconstructing dynamic 3D scenes from monocular input is a fundamental problem across a variety of tasks, including augmented reality, robotics, and human motion analysis (Slavcheva al., 2017; Li et al., 2023; Newcombe et al., 2015; Joo et al., 2014; Gao et al., 2022). However, despite its wide applicabillity, monocular dynamic reconstruction remains highly challenging (Liang et al., 2025c), particularly under occlusion and extreme viewpoint changes. Recently, the advent of 3D Gaussian Splatting (Kerbl et al., 2023) has enabled real-time photorealistic rendering and sparked a series of dynamic extensions (Luiten et al., 2024; Lei et al., 2025; Stearns et al., 2024; Liu et al., 2025; Duan et al., 2024; Huang et al., 2024; Yang et al., 2024b;c; Li et al., 2024; Sun ct al., 2024b; Duisterhof al., 2023; Das al., 2024; Lin al., 2024; Wu et al., 2025). These methods parameterize motion with shared canonical fields (Wu et al., 2024; Yang et al., 2024c; Liang et al., 2025b; Guo et al., 2024; Lu et al., 2024; Wan et al., 2024; Liu et al., 2025). deformation bases (Wang et al., 2025a; Das et al., 2024; Lin et al., 2024; Li et al., 2024), or direct 4D modeling (Duan et al., 2024; Yang et al., 2024b).
Despite their differences in formulation, existing dynamic Gaussian splatting methods often share common assumption: motion is optimized uniformly across all Gaussians using 2D) supervision such as depth (Yang et al., 2024a), optical flow (Teed & Deng, 2020), and photometric consistency (Doersch et al., 2023). This uniform treatment overlooks that some Gaussians are strongly constrained by recurring observations, while others are only weakly constrained. As a result, motion estimates drift under occlusion and synthesized views degrade at novel viewpoints.
To maintain prioritized and used to guide the optimization of less reliable ones. Consider the example in Figure 1, where a rotating backpack is captured by moving monocular camera. At any moment, a portion of the surface is self-occluded and invisible. Yer, humans can readily infer their appearance and motion by recalling previously observed surfaces and extrapolating with temporal continuity. Such ability anchors on the most reliable parts of the backpack, i.e., those clearly observed from other viewpoints and timestamps. This suggests a key principle: when observations are partial, reconstruction should be guided by confident cues and propagated structurally uncertain regions.
consistency, we argue that confidently observed Gaussians should be
Building on this insight, we propose USPLAT4D, a novel Uncertainty dynamic Gaussian Splatting framework for monocular 4D reconstruction. We first introduce principled method to estimate time-varying uncertainty for each Gaussian, capturing how reliably it is constrained by recurring observations. This uncertainty then guides the selection of anchor Gaussians and propagate motion across space and time. To realize this principle, we organize Gaussians into a spatio-temporal graph, where uncertainty determines node importance, edge construction, and adaptive loss weighting. The goal of the uncertainty-awe aware graph optimization is to ensure that confident parts of the scene dynamically guide the reconstruction of the rest, even in occluded or unseen views.
We validate our approach on various real and synthetic datasets on monocular 4D reconstruction. We show that explicitly leveraging uncertainty significantly enhances both motion tracking and novel view synthesis, with particularly strong gains under extreme viewpoints. Our framework, including uncertainty estimation, graph construction, and adaptive training, is model-agnostic and can be integrated into existing dynamic Gaussian splatting pipelines that parameterize per-Gaussiaa motion. Overall, USPLAT4D introduces a principled way to model uncertainty in dynamic Gaussian Splatting, yielding more stable motion estimates under occlusion and high-quality extreme view synthesis.
RELATED WORK
Dynamic Gaussian splatting. Recent advances in dynamic Gaussian Splatting have enabled monoc- ular 4D reconstruction via per- ver-Gaussian deformation or canonical motion modeling (Liang et al., 2025c; Yang et al., 2024b;c; Wu et al., 2024; Li et al., 2024) or high-fidelity dynamic scene recon- struction from multi-view inputs (Luiten et al., 2024; Wang et al., 2025b). To reconstruct 4D GS from the monocular video, methods such as SoM (Wang et al., 2025a), MoSca (Lei et al., 2025), Mar- bles (Stears et al., 2024), and 4D-Rotor (Duan et al., 2024) use low-rank motion bases to regularize deformation, while others model canonical flows (Liang et al., 2025b; Liu et al., 2025). Although they demonstrate fig-fiidlitty rendering on near-input validation views, they do not explicitly model the motion behind occluders or identify reliable Gaussians for motion guidance. MoSca (Lei et al., 2025) introduces soft motion score but lacks structured propagation. In contrast, USPLAT4D
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selects high-confidence Gaussians and constructs uncertaint--wwacc motion graph propagate motion through aaatiotempprrlly coherent connections, improving robustness in occluded regions and enabling localized refinement beyond low-rank modeling (Kim et al., 2024; Huang et al., 2024).
Uncertainty estimation in scene reconstruction. Uncertainty modeling has been widely explored in neural rendering (Shen et al., 2021; Li et al., 2022; Pan et al., 2022; Shen et al., 2022; Kim et al., 2022; Zhan et al., 2022; Yan et al., 2023; Lee et al., 2022; Sünderhauf et al., 2023), particularly to improve robustness under occlusion, sparse views, and ambiguity. To avoid overfitting on reconstructing the static scene, SE-GS (Zhao et al., 2025) designs an uncertainty-aware perturbing strategy by estimating the self-ensembling uncertainty. In dynamic settings, uncertainty has been used smooth motion or reweight gradients (Kim et al., 2024), but typically as an auxiliary signal decoupled from the underlying motion representation. In contrast, USPLAT4D treats uncertainty as a central modeling component. We estimate confidence of each Gaussian and use it to guide key node selection, edge construction, and loss weighting in a spatio-temporal graph. This allows high-confidence Gaussians to guide motion propagation while reducing the influence of uncertain regions. To the best of our knowledge, this is among the first attempts and analyses to model the uncertainty and directly integrate it into graph-based motion modeling for dynamic reconstruction.
3 PRELIMINARY
We first review dynamic Gaussian Splatting and its learning objective to establish the notation for Section 4. Our method is model-agnostic, building on this standard formulation and applicable to a wide range of dynamic Gaussian Splatting variants (Lei et al., 2025; Wang et al., 2025a).
Dynamic 3D Gaussians. Vanilla dynamic Gaussian Splatting (Luiten et al., 2024) represents a scene with set of time-varying 3D Gaussians. Formally, the state of Gaussian at time t is defined as
G = =pet st..
(1)
where Pa R3 denotes the position at time t, q: E R44 the quaternion rotation, I the scale, a E IR the opacity, and c E R. the color coefficients (c.g., spherical harmonics or RGB), N₂ the color dimension. The trajectory of Gaussian is then given by the sequence (G,) where T the number of frames. While early extensions introduce time-varying color (Yang et al., 2024b), this hinders 3D motion tracking. Recent methods, such as SoM (Wang et al., 2025a) and MoSca (Lei et al., 2025), restrict the color space to stabilize motion estimation and enable reliable tracking.
Learning objectives. To optimize the 4D Gaussian field, existing methods minimize combination of losses. A photometric reconstruction loss enforces consistency between rendered and ground-truth images, while motion-locality losses regularize the temporal evolution of Gaussians. These locality terms include isometry, rigidity, relative rotation, velocity, and acceleration constraints (Lei et al., 2025: Huang et al., 2024), which shrink the large motion search space and stabilize optimization.
Limitations. Although effective near input views, these objectives remain fragile under occlusion and extreme novel viewpoints, they rely heavily on unstable 2D priors such as depth, optical flow, or photometric consistency. As result, reconstructions often drift over time and lose geometric consistency across different views. To overcome this challenge, we introduce a dynamic uncertainty model that explicitly encodes the reliability of each Gaussian over time and forms the foundation of our framework. In particular, Gaussians are partitioned into key and non-key nodes and connected through an mmcertainty-weigheee graph, which enforces spatio-tempont consistency.
4 UNCERTAINTY-WWAR DYNAMIC GAUSSIAN SPLATTING
Overview. Given a monocular video, we begin by building on vanilla dynamic Gaussian Splatting models to estimate a time-varying uncertainty score for each Gaussian, explicitly capturing its reliability across frames (Section 4.1.. These uncertainty scores then guide the construction of an mncertainty-weigheee graph that systematically organizes Gaussians into key and non-key nodes (Section 4.2). The resulting graph subsequently drives optimization process that propagates reliable motion cues to uncertain regions, thereby refining both motion estimation and rendering quality of the dynamic scene (Section 4.3). An overview of the entire pipeline is illustrated in Figure 2.
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Gaussian Initialization
4.3 Unertainty-aware Optinization
Key mode mon-key nade peteeption losae:
Vanilla Dynamic GS
Extreme Novel
Spatio Semporal 4DGS
Input View
Extreme Novel View 21t
Dynaunic Uneertalaty Estimation
4.2 (b) Key Graph Construction
Key Nodes Colored Uncestaiity:
N
Uncertainty aware
Vime Coufideut node
odge
Vanilla Opvimization
Ours
39
Uneeraiiry Map
Non-key Graph Interpolation
Final RGB Readering:
Non-key sode ANN puth
Key mode
: DOB odge
4.2 (a) Key Node Selcetion
Finding ANNS
DQB Interpulation
Vanilla Optimization
Ours
Figure 2: Overview the proposed USPLAT4D. first estimate time-varying uncertainty for each Gaussian (Section 4.1). We then leverage these uncertainties to select reliable Gaussians as key nodes, while others are treated as non-key nodes for graph construction (Section 4.2). Finally, we optimize the spatio-temporal graph with uncertainty-weighiedd losses, yielding consistent 4D Gaussians (Section 4.3). The right column shows that approach significantly improves novel view renderings compared vanilla optimization.
4.1 DYNAMIC UNCERTAINTY ESTIMATION
Vanilla dynamic Gaussian Splatting optimizes all primitives uniformly. even though some are well observed across time while others remain ambiguous. This causes drift under occlusion and instability at extreme viewpoints. We therefore assign each Gaussian G; time-varyinn uncertainty to estimate its reliability per frame and use it to guide optimization a model-agnostic way.
Per-Gaussian scalar uncertainty. A straightforward way to capture reliability is to assign each Gaussian scalar uncertainty u, at every frame Intuitively, if primitive is frequently and clearly observed, its uncertainty should be small; if it is rarely visible or weakly constrained, its uncertainty should be large. The photometric loss is defined as:
C2.1 C2 = 5 ||C
No C. -C,11 where C. = Tha Ci.
(2)
Here, Ω is the pixel index set, Ch and C denote the ground-truth and rendered colors of pixel h at frame and e; is the color parameter Gaussian i. The rendered pixel color is obtained by a-blending, where the blending weight is given by T,, with Tht the transmittance of Gaussian at pixel h, o; its opacity, and N, is the number of Gaussians. By differentiating L2,t with respect to and applying the local minimum assumption, obtain the closed-form variance estimate (please see Appendix A. for detailed derivation):
o = (Enear (T(,a))2
where Si C Ωr is the set of pixels contributed to by that Gaussian. We thus take this variance as the scalar uncertainty, i.e., W4,t == of,-- However, the local minimum assumption may not hold everywhere. To account for unconverged pixels, we introduce an indicator function test per-pixel convergence:
1.(h) = -
if || -c 1|1 < Te: otherwise,
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where 7/ > 0 is color-error threshold. For Gaussian at frame t, we define the aggregate indicator 1, = Ihee, (h),, which equals only if all covered pixels are convergent. If Li, = 0, we assign a large constant & to indicate high uncertainty. Therefore, the final scalar uncertainty is:
Ui,t 1,,, 02 + (1-1:,)
(5)
This design jointly adjusts the strength according to convergence status: Gaussians that are well sup- ported by observations receive low Us,t, reflecting high reliability, while unreliable ones are assigned high values, which enables trustworthy primitives guide ambiguous ones during optimization.
From scalar to depth-aware uncertainty. While the scalar definition is intuitive, it implicitly assumes that uncertainty is isotropic in 3D space. This is problematic in monocular settings, where depth is much less reliable than image-plane coordinates. A uniform scalar tends to be over-confident along the camera axis, leading to geometric distortion. To address this, propagate image-space errors into 3D and represent each Gaussian by an anisotropic uncertainty matrix:
U = Roae U. R
where U. = diag(ra Ty TzMi,r).
(6)
Here, Rew is the camera-to- world rotation and ras Ty F2 are axis-aligned scaling factors. Note that only rotation is required to propagate uncertainty, since translation does not affect covariance. This transforms 2D uncertainty into axis-aligned 3D uncertainty, incorporating both the camera pose and the directional sensitivity of depth. A typical example is the "Camel" sequence in Figure 2 (also the supplementary video): without depth- aware uncertainty, the camel's 's body shrinks unnaturally, whereas our formulation preserves its correct shape.
4.2
UNCERTAINTY-FRNCORR GRAPH CONSTRUCTION
Per- Per-Gaussian uncertainty in Equation 6 provides a local measure of reliability, but treating Gaus- sians independently cannot guarantee spatio-temporal consistency. Neighboring primitives often share correlated motion, and reliable ones should anchor the optimization of uncertain ones. Prior graph-based methods (Huang et al., 2024; Lei et al., 2025) attempt to capture this correlation, e-g.. MoSca (Lei et al., 2025) introduces a 3D lifting graph. In contrast, we build the graph directly on uncertainty: Gaussians are ranked by reliability and partitioned into key and non-key nodes, so that stable primitives drive motion propagation while ambiguous ones are regularized. To realize this, we design an secertainty-wwarrr graph that encodes reliability in both node selection and edge connectivity, providing the foundation for the optimization described in the following sections.
Graph definition. We represent the scene with a directed graph g = (V,8), where each node E corresponds Gaussian G, and edges (i,j) E encode spatial affinity and motion similarity. Crucially, nodes are partitioned into a small key set V and a large non-key set Vn according to their uncertainties (u,,e) from Section 4.1. We define key nodes stable Gaussians that carry strong motion cues across time and views, while non-key nodes inherit motion from their key neighbors.
Key node selection. Our key node selection operates in 3D space. We employ the following two-stage strategy that balances spatial coverage and temporal stability:
1. Candidate sampling via 3D gridization. At each frame, partition the scene into 3D voxel grid. Voxels containing only high-uncertain Gaussians' are discarded. In the remaining grids, which may contain multiple now-uncertainty Gaussians, "uniform selection" ensures spatial coverage and reduces redundancy, unlike random selection without voxel uniformity.. This is based on the assumption that each meaningful motion will occupy a unique voxel in at least one frame; motions indistinguishable at this resolution are minor and handled by non-key interpolation (see below). Intuitively, distinct motions leave separable footprints, while small differences are smoothed out.
randomly select one per grid. This per-voxe voxel
Thresholding by significant period. For each Gaussian candidate, we compute its significant period, defined as the number of frames where its uncertainty stays below a threshold. We retain
We maintain typical 1:49 keynonn-ke ratio by selecting the top 2% (around 1000-th) most contident Gaussians. Ablations with ratios from 0.5% (please see Appendix D.1) show consistent performance across different scenes and models, with 2% lying on stable plateau balancing coverage and reliability.
"e test random selection with the same per-frame count but without per-grid uniformity. This produces non-uniform spatial distributions (i.e., some voxels oversampled, others missed). See Table 3(d) for analysis.
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only those with significant period of at least 5 frames, ensuring that key nodes have sufficient temporal support to contribute reliably to motion estimation. Candidates with insufficient temporal coverage tend to destabilize graph optimization and yield under-constrained solutions.
Edge construction. We construct edges separately for key and non-key nodes, as their roles differ: key nodes provide structural anchors for motion propagation, while non-key nodes interpolate appearance locally. The key graph captures long-range geometric and motion dependencies (c.g.. both ends of a limb moving coherently), which existing bassance-based heuristics such as local KNN (Huang et al., 2024) or global min-max distances (Lei et al., 2025) cannot robustly handle.
To address this, we adopt an Uncertainty- Aware ANN (UA-ENN). For key node i, we select neighbors only among other low-uncertainty key nodes, evaluated at its most reliable frame i arg min, } and measure distances with uncertainty weighting favor trustworthy connections:
E = ANN (IIP.. - Py.cll(u....
Here, the Mahalanobis metric up-weights directions of high uncertainty, so edges are formed between nodes that are both spatially close and reliable. As will be shown in Section 4.3, these edges are further pruned by the key graph loss for additional robustness and to prevent spurious long-range connections. For a non-key node i, we assign its closest key node across the sequence:
j = arg min Z== llp..
(8)
and connect & E, U Li), where is the closest key nodes. Intuitively, each uncertain non-key node is attached to the most reliable key node that stays close to it over time, so its motion can be regularized by stable anchors for consistency. In both cases, uncertainty kNN ensures edges are anchored by reliable nodes, promoting stable motion propagation and preventing drift from uncertain regions.
4.3 UNCERTAINTY-AWARE OPTIMIZATION
Vanilla optimization (Section 3) of dynamic Gaussians often fails under occlusion or extreme viewpoints. This leads unreliable primitives to drift, since they are optimized as strongly as reliable ones. To address this, we incorporate uncertainty into the objective: key nodes with stable observations serve as anchors, while non-key nodes are regularized more softly through interpolation. We design separate objectives for key and non-key nodes, then unify them in total loss.
Key node loss. Key nodes are low-uncertainty Gaussians that anchor motion. We encourage them to stay close to their pre- -petiiite positions:
key
Σ Eiery ||p... - -p, _mation.key
1=
where U.... down-weight directions of high uncertainty, ensuring motion is corrected mainly along reliable axes. Superscript denotes the parameters from the pretrained Gaussian Splatting model, optimization. The key node motion loss
which serves as initialization before our uncertainty- Comtion.key regularizes the temporal evolution of Gaussians by isometry, rigidity, rotation, velocity, and acceleration constraints, which is discussed in the Appendix A.2 in detail.
Non-key node loss. Non-key nodes are interpolated from nearby key nodes using Dual Quaternion Blending (DQB) (Kavan et al., 2007), which provides smooth motion by blending their neighbors:
DQB Pi
DQB q
DQB ((w))) T5.))see.).
(10)
where Why are normalized edge weights for blending, T₁, E SE(3) the transform of key node j. and pQu are the position and rotation of Gaussian at time t acquired by DQB, respectively. We then regularize non-key nodes to both their initialization and interpolated trajectory:
DQB and q,
P...
caon-key
V.,, |p..: - PPi.ell +
Σ
|p..: - P...
DQB llu. + + mmotinn.oo key (11)
Here, Cmmotionnnnn-key is the non-key node motion loss, which will also be discussed in Appendix A.2.
This non-key loss keeps non-key nodes close to their pretrained states while aligning them with motions propagated from reliable key nodes, preventing drift while ensuring coherence.

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Paper- seeer-winn windmill
Spin
Apple
Input
SoM
USplat4D (Ours)
MoSca
USplat4D (Ours)
GT
Figure Qualitative results on validation views of the DyCheck dataset (Gao et al., 2022). show comparisons with two strong baselines, SoM (Wang et al., 2025a) and MoSca (Lei et al., 2025). USPLAT4D improves visual quality and better preserves geometry (e.g., arms in "Spin", hands in "'pacceoul") and pose (c.g.. "Windmill"), Please zoom for details. Sec supplementary for more examples and other baselines.
Spin
Harussit
3D Rendering
SoM
USplat4D (Ours)
MoSca
USplat4D (Ours)
Figure 4: Qualitative results extreme novel views from DyCheck. Unlike Figure 3, these manually sampled extreme views (red cameras) lack ground-truth. USPLAT4D better preserves fine structures (e.g., the dog's head "haru-sit") and occluded regions (e.g., hand "spin") under large viewpoint shifts.
Total loss. The final objective combines the key and non-key node losses with the photometric loss:
total
cg + Ckey + Chonkeyy
(12)
Overall, the uncertainty in our framework serves three purposes: (1) re-weighting deviations of key nodes, (2) guiding the interpolation of non-key nodes, and (3) balancing their influence in the total loss. Our design mitigates drift under occlusion maintains geometric consistency at novel views.
EXPERIMENTS
SETUP
Datasets. (1) DyCheck (Gao et al., 2022): We follow prior works (Wang al., 2025a; Lei et al., 2025; Stearns et al., 2024) to evaluate on 7 scenes with validation views. Since these validation views near the input views, additionally sample extreme novel views for qualitative analysis.
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Table I:
Quantitative results on DyCheck. We report results scenes resolution scenes 2x resolution, following existing protocols. USPLAT4D consistently outperforms state-of the-art Gaussian Splatting based methods. See Figure for qualitative results on validation views and Figure 4 for extreme views.
Setting
Method
mPSNRf
mSSIM1
mLPIPS.
SC-GS (Huang et al., 2024)
14.13
0.477
0.49
Deformable 3DGS (Yang et al., 2024c)
11.92
0.490
0.66
4DGS (Wu ct al., 2024)
13,42
0,490
0,56
5 scenes
MoDec-GS (Kwak al., 2025)
15.01
0.493
0,44
x resolution
MoBlender (Zhang al., 2025)
16.79
0.650
0.37
SoM (Wang al., 2025a)
16.72
0.630
0,45
USPLATT4 (ours)
16.85
0.650
0.38
Dynamic Gaussians (Luiten et al., 2024)
7.29
0.69
4DGS (Wu al., 2024)
13.64
0.43
7
scenes
Gaussian Marbles (Stearns et al., 2024)
16.72
0.41
X resolution
MoSca (Lei al., 2025)
19.32
0.706
0.26
USPLAT4D (ours)
19.63
0.716
0.25
Camel
Car-rondashust
Car-tum
Helleopter
Breasddance
313 Rendering
SoM
USplat4D (ours)
313 Rendering
MoSca
USplat41) (ours)
Figure 5: Qualitative results on extreme novel views from DAVIS. For each case, we show an input-view rendering and compare the baseline (SoM (Wang et al., 2025a) or MoSca (Lci et al., 2025)) with our USPLAT4D extreme novel view (red). USPLAT4D yields clearer reconstructions under challenging conditions.
(2) DAVIS (Perazzi et al., 2016): To test generalization across different scenarios, qualitatively evaluate on challenging monocular videos using DAVIS dataset, which cover non-rigid motion, occlusions, and complex dynamics. (3) Objaverse (Deitke al., 2023): We construct synthetic benchmark from Objaverse by selecting 6 challenging articulated objects with diverse textures and motions. Specifically, around each object, always facing toward it. For validation, we render views at horizontal angular offsets of 30 inerements from 30 330", each with a fixed elevation of 35", order capture the object's s 3D structure from diverse viewpoints. Please see Appendix C.4 for additional details.
cameras follow circular trajectory of 121 frames (3 per step)
Baselines. We evaluate USPLAT4D against state-of-the-art dynamic Gaussian Splatting methods. We use SoM (Wang et al., 2025a) and MoSca (Lei et al., 2025) as our main base models. Specifically, SoM is widely adopted standard in the field, and MoSca represents the current state of the art. Notably, our framework is agnostic to the underlying architecture and is compatible with any method that estimates per-Gaure Gaussian motion. Please see Appendix B for additional implementation details (e.g., hyper- parametessssss for model training) and details of additional baseline methods.
'Existing benchmarks lack ground truth for extreme novel views, making systematic evaluation difficult. We therefore huild synthetic benchmark, where strong results support generalization claims (see experiments). Note that such synthetic setups widely used stress-test models (Yao al., 2025; Liang al., 2024).
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Table 2: Results on the Objaverse dataset. We evaluate novel view synthesis across increasing horizontal angular ranges: (0", 6.0". (60", 120"). and (120", 180"). USPLAT4D consistently improves over SoM (Wang et al., 2025a) and MoSca (Lei et al., 2025), with gains most pronounced at larger viewpoint shifts.
View Range (0", 60%
View Range (60",
120º]
View Range (120", 1809]
Method
PSNR1
SSIM
LPIPS
PSNRT
SSIM
LPIPS
PSNR1
SSIM
LPIPS
SoM
16.09
0.860
0.31
15.58
0.854
0.32
16.45
0.858
0.31
USPLAT4D
16.63.co
0.866 o07
0.27 .33
16.57.om .09
0.868.014
0.27,00
17.03.6s
0.872.u14
0.26.05
MoSca
16.18
0.881
0,24
15.74
0,875
0.25
15.89
0.876
0.25
USPLAT4D
16.22.on 04
0.885 004
0.22 02
15.98.24 24
0.884,00 ,099
0.23,02
16.31, 42
0.886.om 011
0.21.03 .03
Doctor
Rat
AngryBird
3D Rendering
SoM
USplat4D (ours)
MoSca
USplat4D (ours)
GT
Figure 6: Qualitative results on Objaverse. Each case shows 3D rendering from an input view and comparison between the baseline (SoM (Wang al., 2025a) MoSca (Lei et al., 2025)) and our USPLAT4D an extreme novel view (red). Please see the supplementary video for clearer visualization.
5.2
MAIN RESULTS
Results on DyCheck. Table reports results on DyCheck validation views, which are close the input trajectories. USPLAT4D consistently outperforms baselines across all metries; corresponding qualitative examples are shown in Figure 3. However, these validation views remain relatively easy due to the proximity to the input views. The more significant improvements emerge under extreme novel viewpoints (Figure 4), which are not included in Table but are essential for assessing robustness under severe viewpoint shifts. Our results avoids the collapse and distortions observed in the baselines. Please refer to Appendix for the tracking results and per-scene comparison.
Qualitative results of extreme novel view synthesis DAVIS, We evaluate selected monocular videos from the DAVIS dataset, which include fast motion, deformation, and self-occlusion. As ground-truth geometry is unavailable, USPLAT4D yields more plausible geometry and coherent reconstructions under extreme viewpoint shifts, where the baseline often exhibits distortion, blur, or artifacts.
focus qualitative comparisons. As shown in Figure 5,
Results on Objaverse. Table shows that USPLAT4D consistently surpasses SoM (Wang et al., 2025a) and MoSca (Lei et al., 2025), with gains most pronounced under (120%-180)). Figure 6 confirms these improvements qualitatively: our method preserves geometry and textures under extreme novel views, where baselines often blur collapse. Please refer to Appendix C for more quantitative and qualitative results.
large viewpoint shifts
Other results. We provide further results and analysis in Appendix C, including per-scene results on the DyCheck and Objaverse, evaluations on the NVIDIA (Yoon et al., 2020) and HyperNeRR (Park al., 2021) datasets, results of the tracking task, and comparisons with additional baseline methods.

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Figure 7: Key node weight matrix and Gaussian segmentation. The weight matrix visualizes edge connec- tions among key nodes. Nodes belonging to different diagonal blocks do share weights, indicating the no connections across those blocks. The images further illustrate the corresponding Gaussian segmentation, where colors represent the block assignment of each key and non-key node.
5.3 ABLATION AND ANALYSIS
We conduct ablation studies to assess key design choices in USPLAT4D, using MoSca (Lei et al., 2025) as the base model and evaluating on the DyCheck validation set (Gao al., 2022). As described in Section 4, our core design include the graph initialization strategy and the use of uncertainty cues during graph construction and optimization. Finally, we analyze the model's s capabilities.
Ablation study on uncertainty usage and key node selection. Table shows that uncertainty and key node selection are essential across key components of our framework. (a) When re- moved from key node selection and replaced with uniform 2D sampling, the graph fails to emphasize well-constrained Gaussians, leading to unstable anchors and degraded propagation. (b) Replacing UA-ANN with distance-only ANN weakens graph connectivity, as edges disregard node reliability and mistakenly connect unstable primitives. (c) Excluding uncertainty weighting from the training loss (i.e., applying the DQB loss without U) reduces PSNR/SSIM, since unreliable Gaussians are updated aggressively reliable ones, causing drift across frames. (d) Replacing the key node candidate sampling strategy from 3D grid-based sampling with spatially random sampling leads to performance degradation, since the non-uniform spatial distribution neither guarantees sufficient spatial coverage nor avoids redundant samples. Please see Appendix for additional ablation study (on hyperparameter key / non-key ratio, color threshold, significant period threshold, and scaling factor) and discussion on time complexity.
Table 3: Ablation study on uncertainty usage and key node selection. We assess the impact of uncertainty estimation and key node selection strategy by removing them from key components in USPLAT4D individually.
Ablation Setting
PSNR SSIM LPIPS.
USPLAT4D (full model)
19.63
0.716
0.25
(a) w/o key node uncertainty
18.86
0.688
0.28
(b) w/o UA-KNN
19.50
0.711
0.26
(C) w/o loss weighting
19.08
0.681
0.25
(d) w/o 3D gridization
19.50
0.712
0.25
Further analysis. Our model naturally supports multiple objects and inherently group Gaussians by motion. In key and non-key graphs, nodes with similar instance-level motion are strongly connected, while nodes from different instances have weak (or few) connections. As shown in Figure 7, the key-graph weight matrix (defined in Appendix A.2. becomes approximately block-diagons after reordering nodes with standard community detection (e.g., spectral clustering (Von Luxburg, 2007)). After clustering key nodes, assign non-key nodes to the same motion groups using their non-key graph connections. Each Gaussian gets a label, and when objects move differently, the segmentation closely matches instance segmentation with dynamic tracking. Besides, further discuss the challenging cases including textureless region, fast motion, and deforming objects in Appendix E.
DISCUSSION AND CONCLUSION
We introduce USPLAT4D, a novel dynamic Gaussian Splatting framework that demonstrates that how uncertainty estimation is pivotal for monocular 4D reconstruction. By integrating time-varying uncertainty estimation with an uncertainty-uuided graph, our approach significantly improves geomet- ric consistency and rendering quality, particularly under challenging extreme novel view synthesis. While our framework remains influenced by the computational overhead and inherent errors of underlying visual foundation models, we hope that our findings could encourage further work on leveraging uncertainty to advance robust 4D reconstruction and extreme novel view synthesis.
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APPENDIX
A ADDITIONAL DETAILS ON USPLAT4D
A.1 DERIVATION OF UNCERTAINTY (SECTION 4. 1 OF THE MAIN PAPER)
To derive our uncertainty model, we begin by analyzing the color blending function used in volumetric rendering. To simplify the notation, we omit time t. The rendered pixel color C' along a ray is computed as weighted sum of the color contributions from all Gaussians:
Ng
C¹ Σ aGG =
Σ
0 ci.
(SI)
where i is the index of Gaussians, T; the accumulated transmittance up to the i-th Gaussian, og is the opacity, ci is the color, and == Tia; denotes the blending weight. To estimate the color uncertainty, we derive closed-form expression for each Gaussian's color via maximum likelihood estimation (MLE) under an RGB &g loss:
L2 =
Σ | - 1h hER
(S2)
Here, & E Ω indexes the of all pixels, C is the ground-truth color, and Cth is the color rendered using the blending model in Eq. S1.
To find the optimal color d₁ for the k-tth Gaussian, we compute the gradient of the loss with respect to c and obtain:
AL(D; 0)
--E2 2( -C*)ot
Ock
OMLE
heΩ
(S3)
20t ut ufcx)of
where 'D is the training dataset, 0 is the learnable model parameter vector, and we define ig Cth Ejyx oftc cj to isolate the contribution of the k-th Gaussian to the rendered color at pixel h.
Assuming a local minimum, the MLE estimate satisfies:
AL(D; 0)
0
(S4)
8ck
1MLE
Solving this closed- form yields the optimal color for Gaussian A:
Ck Enen 6
(S5)
(o))
Finally, by assuming b/: ~ NN Te, <²) where 2 is the groundtruth Gaussian color and Var(t!) e². the corresponding closed-form Gaussian color uncertainty (i.e., of Var(cxlu()) under the unit- variance Gaussian noise model (i.c., e² = 1) can be derived as:
of
Σ (x MEn
(S6)
A.2 DETAILS OF TRAINING LOSSES (SECTION 3 AND SECTION 4.3 OF THE MAIN PAPER)
As described in Section 4.3 and Eq. 12 of the main paper, our total loss is: toott rg Ckey non-key
(S7)
where L is the perception loss and meyion, and mmntioon.onnkey the motion loss used in Lkey and C Coon-eey, which covers the motion locality of key and non-key nodes, respectively. this section, provide additional details.
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A.2.1 MOTION Loss
The vanilla Gaussian splatting approach (e.g., (Luiten al., 2024)) represents dynamic scene using 3D Gaussians with tim-vvryying motion and define motion loss. The recent paper also follow up on the motion loss and modifies it based on their tasks. Here, we give the general form of these motion losses. The isometry loss describing the distance constraint between Gaussians is defined as
T-1
C
ΣE LEV jEknn..
w Pi.ol2 - IP Pt, l22
(S8)
k/VI
where subscript means canonical space, is the Gaussian's 3D position vector, and Wy, represent the edge weight between i-th and j-th Gaussians. Since isometry does not take coordinates into consideration, we use rigidity loss unify the coordinates and constrain the relative motion between Gaussians. The rigidity loss is defined by
A
1
ΣEΣ Who ||P - TT == LEV jEknn.
(S9)
KV
where Te, is the i-th Gaussian transformation matrix at t-th time index and A is the time interval. With a larger weight Wiy,, the Gaussian pair is more rigid. Beyond that, we also constrain the relative rotation explicitly for finer control on the rotation penalty. The relative rotation loss is defined
T-1 EE E TEV JEknIG
wis (. - 9..97,A-
(S10)
kVI
where q is the quaternion representation of the rotation matrix. All there three loss define the deformation of the Gaussians. Besides, the object motion in the world space tends to be smooth, so we define the velocity and acceleration to regularize the Gaussian model to avoid overfitting on the training views. The velocity loss is defined as
veel
| - Pull + 1|q.-1199
(S11)
NEV
The acceleration loss is defined as
T-1
Cace
ΣΣ |P,,c22 2p + Pr.lll + ||:,t-2 (101-19971((9.-11110 (q.t-1977))
(S12)
t=2 LEV
The motion loss used in the recent works (Lei et al., 2025; Stearns et al., 2024; Huang et al., 2024; Luiten et al., 2024) can be obtained by the different combinations of these losses. Then, the motion locality loss is defined as
mootion
i Liso + Arigid + X X A Lvel + a Lace
(S13)
where the hyperparameter. Specifically, we set ilso and Ariidd to ensure the geometry preservation and assign rot, X vel and aace to 0.01 for rigid orientation and motion smoothness.
A.2.2 PERCEPTION LOss
The perception loss L includes a standard combination of RGB & loss and SSIM loss. Following the base models (SoM and MoSca), also incorporate 2D prior losses, i.c.. mask loss, depth loss, depth gradient loss, and tracking loss, into Le We emphasize that these additional terms are not part our proposed method, but are standard components inherited from the respective base models to ensure consistent training behavior.
B IMPLEMENTATION DETAILS
USPLAT4D is compatible with dynamic Gaussian splatting methods that provide initial motion parameters. In this section, describe implementation details for integrating USPLAT4D with two strong base models (i.e., SoM (Wang et al., 2025a) and MoSca (Lei et al., 2025)) along with our training and evaluation protocols.
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B.1 ADAPTATION OF SoM AND MOSCA
Both SoM (Wang et al., 2025a) and MoSca (Lei et al., 2025) adopt low-rank motion parameterizations to achieve compact, smooth, and rigid 4D representations. We first convert the outputs of SoM or MoSca into the data structure required by USPLAT4D. Specifically, we extract the 4D Gaussian primitives (G,) N₂ from each method and reformat them into unified representation that supports our graph construction and uncertainty modeling pipeline. To mitigate inconsistencies in Gaussian scale and distribution, unify the spatial volume when selecting key Gaussians and normalize the uncertainty threshold used in both key node selection and edge construction. This ensures that method behaves consistently across different base models. Since our method introduces additional optimization on top of the pretrained SoM and MoSca models, we ensure fair comparison by continuing to train the original SoM and MoSca with the same number of additional iterations as our baselines.
B.2 TRAINING USPLAT4D MODEL
During preprocessing of USPLAT4D, we sequentially initialize the key and non-key graphs. To train the USPLAT4D model, we build on pretrained base models and allocate additional training iterations for fair comparison. Specifically, we train SoM for 400 extra epochs and MoSca for 1600 extra steps, using consistent batch size of 8. These schedules are empirically chosen to ensure good convergence while aligning with the respective optimization routines of the base models. For the first 10% and the last 20% of the training duration, we disable both density control and opacity reset to maintain stability. For the remaining iterations, we enable density control and opacity reset to improve rendering quality. The per-epoch and per-step training time is similar to that of SoM and MoSca, respectively.
C ADDITIONAL EXPERIMENTAL RESULTS
C.1 COMPARISON WITH ADDITIONAL HERF-BASER METHODS ON DYCHECK DATASET
In Table of the main paper, we primarily compare with Gaussian splatting based methods that use explicit 3D representations. Here, extend the comparison by including additional NeRF-based methods (Miao et al., 2024; Park et al., 2021; Gao et al., 2022; Li et al., 2023; Sun et al., 2024a; Kappel al., 2025; Zhao al., 2024) that adopt implicit neural radiance fields. While these methods are effective for static or mildly dynamic scenes, our experiments show that Gaussian splatting based approaches consistently outperform NeRF-basee methods on the DyCheck dataset (Gao et al., 2022). This comparison serves as an extension of Table 1 and the results are summarized in Table S1.
C.2 TRACKING RESULTS ON THE DYCHECK DATASET
We report 3D keypoint tracking results in Table S2, following the evaluation protocols of MoSca and SoM. Our method achieves consistent improvements in all tracking metrics, including Percentage of Correct Keypoints (PCK) @ (5%, 5cm, 10cm) and End-Point Error (EPE). When applied to MoSea (Lei et al., 2025), our method yields higher PCK @ 5% (+2.1). Integrated with SoM (Wang et al., 2025a), USPLAT4D achieves notable gains in EPE and PCK at both 5cm and 10cm thresholds, especially improving PCK @ 5cm by over 11.4%. These results show that our uncertainty-awant graph construction not only enhances visual quality but also improves apatio-tempora consistency for dynamic object tracking.
C.3 PER-SCENE RESULTS ON THE DYCHECK DATASET
We further present per-scene breakdown of the DyCheck results reported in Table 1 of the main paper. Results are shown in Table S3. Our method consistently improves per-scene performance across most scenes across PSNR, SSIM, and LPIPS.
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Table S1: Quantitative results on the DyCheck dataset (Gao al., 2022). We compare our method applied to two dynamic Gaussian baselines (SoM and MoSca) with NeRF-based and Gaussian- based methods. We report results on 5 scenes at 1 x resolution and scenes at 2x resolution, following existing protocols. Our approach consistently improves the base models across all metrics and outperforms other baselines in both settings. Best results are in bold, second-best are underlined.
Setting
Method
mPSNRf
mSSIM
mLPIPS.
DynlBaR (Li et al., 2023)
13.41
0.48
0.55
HyperNeRF (Park et al., 2021)
15.99
0.59
0,51
T-NeRF (Gao et al., 2022)
15.6
0.55
0.55
SC-GS (Huang et al., 2024)
14.13
0,477
0,49
Deformable 3DGS (Yang et al., 2024c)
11.92
0.490
0.66
5 scenes
4DGS (Wu al., 2024)
13.42
0.490
0,56
X resolution
MoDec-GS (Kwak al., 2025)
15.01
0.493
0.44
MoBlender (Zhang et al., 2025)
16.79
0.650
0.37
HiMoR (Liang et al., 2025a)
0.46
SoM (Wang et al., 2025a)
16.72
0.630
0.45
USPLAT4D (ours)
16.85
0.650
0.38
D-NPC (Kappel et al., 2025)
16.41
0.582
0.319
CTNeRF (Miao et al., 2024)
17.69
0.531
DyBluRF (Sun et al., 2024a)
17.37
0,591
0.373
HyperNeRF (Park et al., 2021)
16.81
0.569
0.332
T-NeRF (Gao et al., 2022)
16,96
0.577
0.379
7 scenes
PGDVS (Zhao al., 2024)
15.88
0.548
0.34
resolution
Dynamic Caussians (Luiten et al., 2024)
7.29
0.69
4DGS (Wu al., 2024)
13.64
0.43
Gaussian Marbles (Stearns et al., 2024)
16.72
0.41
MoSca (Lei al., 2025)
19.32
0.706
0.26
USPLAT4D (ours)
19.63
0.716
0.25
Table S2: Tracking results on the DyCheck dataset (Gao et al., 2022). We follow the evaluation protocols of MoSca and SoM to report 3D keypoint tracking metrics. USPLAT4D outperforms both basclines by large margin. Please refer supplementary video demo for qualitative results.
Method
PCK (5%)t
Method
EPE,
PCK (Sem)
PCK (10cm)
MoSca (Lei et al., 2025)
82.4
SoM (Wang et al., 2025a)
0.082
43.0
73.3
USPLAT4D (ours)
84.5
USPLAT4D (ours)
0.072
54.4
75.8
C.4 DATA CURATION FOR THE OBJAVERSE DATASET
To evaluate our model under large view shifts, which are rarely present in existing realistic datasets such DyCheck (Gao al., 2022) and DAVIS (Perazzi et al., 2016), we curate high-quality dynamic 3D dataset sourced from Objaverse-1 1.0 (Deitke et al., 2023). Each curated scene contains single object with one or more animation labels; by default, we animate the object using its first animation label. As described in Section 5.1 of the main paper, we render 121 frames for each view. Because the total number of animation frames Objaverse typically smaller than 121, we cycle through the animation frames in zigzag pattern to maintain motion continuity across the rendered sequence. We place cameras evenly around the object at 30 horizontal intervals and move them along circular trajectory in counterclockwise direction with a 3" step per frame. For each frame, we render RGB, depth, and mask images using Blender (Blender Online Community, 2018) and record the corresponding camera intrinsics and extrinsics ground truth. In our experiments, one of the monocular videos as input and utilize the left 1 monocular videos to evaluate rendering quality over three angular ranges: (0 60%), (60", 120%). and (120", 180%].
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Table S3: Per-scene results on DyCheck dataset (Gao et al., 2022). provide a detailed breakdown of the results summarized in Table of the main paper. *: results reproduced by us, as the original numbers in Table I were directly reported from the SoM and MoSca papers. Minor discrepancies may exist due to differences in training. We note that the validation views are near the training views in the DyCheck dataset. Therefore, the observed improvements do not fully reflect the advantages of USPLAAT4 in extreme novel view synthesis. Results are formatted as PSNR (1) SSIM (t) LPIIS (L).
(a) 7 Scenes, 2 x Resolution
Method
Apple
Block
Spin
Paper Windmill
4DGS (Wu al., 2024)
15.41 /.4550
11.28 0.633 /- 17.42
14.42/0.339/
15.60 /0.297 /-
Gaussian Marbles
17.70 / 00.92
0.384/
18.88 0.428 --
17.04 0.394 /
MoSca* (Lei et al., 2025)
19.46 0.809 /0.34
18.17/0.677 0.678 0.32 21.26 /1226/0.7522 0.19 22.36/ 0.743 / 0.16
USPLAT4D (ours)
19.53 0.813/0.32 18.49/ 0.681 0.31 21.77 0.772/0..16
22.55 / 0.753 0.14
Method
Space-Out
Teddy
Wheel
Mcan
4DGS (Wu et al., 2024)
14.60 / 0.372/
12,36 0.466 /-
11.79 / 0.436 -
13.64 / 0.428/ /
Gaussian Marbles
15.94 / 0.435 /-
13.95 / 0.442
16.14 / 0.351 /-
16.72 / 0.418 /-
MoSca* (Lei al., 2025)
20.50 0.664 0.26
1 0.35
18.16
0.684/ 0.23 19.35 0.706 / 0.26
USPLAT4D (ours)
20.81 0.666 0.24 15.78 0.625 0.36 18.46 0.696 / 0.22
19.63 0.716 0.25
(b) Scenes, x Resolution
Method
Apple
Block
Spin
SC-GS (Huang ct al., 2024)
14.96 0.692 / 0.51
13.98 / 0.548 0.48
14.32/ 0.400 0.45
MoDec-GS (Kwak et al., 2025)
16.48 0.699 0.40
15.57 0.590 0.48
15.53 / 0.433 0.37
SoM* (Wang al., 2025a)
16.56 0.749 0.54
16.27 0.652 0.43
17.32 0.710 0.29
USPLAT44 (ours)
16.94 0.754 0.49
16.12 0.653 0.45
17.75 0.711 0.27
Method
Paper Windmill
Teddy
Mean
SC-GS (Huang al., 2024)
14.87 0.221 0.43
0,516 0.56
14.13 0.437 0.49
MoDee-GS (Kwak et al., 2025)
14.92 0,220 0.38
12.56 0.521 /0.60
15.01 0.493 0.44
SoM* (Wang et al., 2025a)
19.46 / 0.557 0.20
13.88 / 0.556 / 0.52
16.70 / 0.645 0.39
USPLAT4D (ours)
19.69 0.555 0.19
13.77 0.551 0.50
16.85 0.645 0.38
W Woman
Panda
Chick
3D Rendering
SoM
USplat4D (ours)
MoSca
USplat4D (ours)
GT
Figure S1: Additional qualitative results on Objaverse. Each case shows a 3D rendering from an input view and comparison between the baseline (SoM (Wang et al., 2025a) or MoSca (Lei et al., 2025)) and our USPLAT4D extreme novel view (red). Please see the supplementary video for clearer visualization.
C.5 ADDITIONAL RESULTS ON OBIAVERSE DATASET
Along with the qualitative results in Figure 6 of the main draft, three additional cases provided in Figure S1 for further comparison. We further provide per-scene results for the Objaverse dataset in Table $4, complementing the summary reported in Table of the main draft. Our method consistently
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Table S4: Per-scene results on the Objaverse dataset. We provide detailed breakdown of the results summarized in Table 2 of the main paper. We evaluate novel view synthesis across increasing horizontal angular ranges: (0",6") 60"). (60", 120%). and (120", 1100"1801"" 180%).
View Range (0", 66")
View Range (60", 120%]
View Range (120", 180"]
Scene
Method
PSNRT
SSIM LPIPS
PSNR1
SSIMf
LPIPS
PSNR
SSIM
LPIPS
SoM
14.26
0.852
0.34
13.86
0.845
0.37
14.70
0.847
0.36
USPLAT4D
14.59
0.852
0.32
14.60
0,853
0.32
15.13
0.857
0.31
Chick
MoSca
14.69
0.869
0.27
14.35
0.864
0.29
14.47
0.867
0.29
USPLAT4D
14.82
0.880
0.24
14.42
0.876
0.27
14.97
0.878
0.25
SoM
15.15
0.857
0.30
14.51
0.843
0.33
15.06
0.852
0.31
USPLAT4D
15.51
0.873
0.26
15.28
0.874
0.26
15.38
0.874
0.26
Panda
MoSca
15.37
0.888
14.83
0.881
0.23
15.15
0.880
0.22
USPLAT4D
15.08
0.888
0.22
14.33
0.883
0.24
15.03
0.886
0.22
SoM
18.47
0.900
0.21
17.92
0.894
0.22
19.85
0.904
0,20
USPLAT4D
20.34
0.920
0.13
20.28
0.920
0.13
21.07
0.923
0.12
Woman
MoSca
16.67
0.910
0.15
16.48
0,908
0.15
16.79
0.910
0.14
USPLAT4D
17.00
0,910
0.13
17.07
0.911
0.13
17.16
0.911
0.12
SoM
16.61
0.833
0.36
15.81
0.837
0.38
16.08
0.835
0.37
USPLAT4D
16.73
0.833
0.35
16.66
0.837
0.35
17.04
0.835
0.34
Rat
MoSca
17.94
0.859
0.27
17.19
0.848
0.29
16.85
0.847
0.30
USPLAT4D
18.53
0.873
0.23
18.09
0.872
0.24
18.27
0.874
0.24
SoM
17.78
0,877
0.28
17.51
0.874
0.29
18.18
0.876
0.28
USPLAT4D
17.68
0,885
0.25
17.35
0.885
0.26
17.77
0.887
0.25
Doctor
MoSca
17.12
0,896
0.23
16.16
0.887
0.26
16.76
0.888
0.24
USPLAT4D
17.07
0.898
0.21
16.32
0.894
0.24
17.10
0.897
0.22
SoM
14.23
0.839
0.34
13.85
0.831
0.34
14.84
0.836
0.32
USPLAT4D
15.24
0.843
0.32
15.27
0.845
0.30
15.79
0.855
0.29
Angry Bird
MoSca
15.04
0.864
0.29
15.16
0.859
0.28
14.97
0.859
0.29
USPLAT4D
14.78
0.864
0.28
14.60
0.862
0.28
15.07
0.868
0.26
achieves lower LPIPS than both SoM (Wang et al., 2025a) and MoSca (Lei et al., 2025) across all angular ranges and scenes. For PSNR and SSIM, our model is also generally superior to SoM and MoSca in most angular ranges and scenes.
C.6 RESULTS ON NVIDIA DATASET
Our main quantitative analysis focuses on the DyCheck-iPhie dataset (Gao et al., 2022) in the main paper, which aligns well with our goal of synthesizing dynamic objects captured by moving camera (see Section of the main paper). Here we also evaluate our method on the NVIDIA dataset (Yoon et al., 2020) for broader comparison. We follow the evaluation pipeline in RoDynRE (Liu al., 2023). The results are shown in Table S5. We note that USPLAT4D also improves performance when applied to state-of-the-an MoSca (Lei et al., 2025) on the NVIDIA dataset, although the gains are marginal. This is expected, as the NVIDIA dataset (or other datasets such as HyperNeRF (Park et al., 2021) dataset) differs from DyCheck to feature input views with more limited motion. contrast, datasets with larger camera movement or more challenging dynamies (DyCheck and Objaverse) benefit more from our design, leading to more substantial improvements.
Table S5: Results on the NVIDIA dataset (Yoon et al., 2020).
Method
PSNRT
SSIM
LPIPS
MoSca Lci al. (2025)
26.77 26.93
0.854 0.855
0.07
MoSca + G2DSplat
0.07
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28 59
0.101
o 0122
25.97
22.
0.998
0.887
20.68
26.21
0.995
D-953
23102
QUe67
0 0.362
0,448
0.247
03.82
35.10
0.997
0 0.986
0.015
Groundtruth
4DGS
USplat4D (Ours)
Groundtruth
4DGS
USplat4D
Comparison on HyperNeRE dataset. The groundtruth, 4DGS, and USPLAT4D (ours) are arranged from left to right. The red and green boxes highlights regions with fine-gra fine-grained details. The values displayed in the top-right corner represent PSNR, SSIM, and LPIPS, arranged from top bottom. Our method reconstructs high- feggurryyy structures more faithfully and preserves finer geometry compared with both 4DGS.
Figure S2:
C.7 COMPARISON ON HYPERNERF DATASET
We further provide a qualitative comparison among 4DGS (Wu et al., 2024), and USPLAT4D (ours) on the HyperNeRF (Park et al., 2021) dataset, as shown in Figure S2. By focusing the dynamic, detail-rice regions, we observe that USPLAT4D restores noticeably finer details and sharper high- frequency structures. While 4DGS tends oversmooth or lose small geometric components, our uncertainty-guiee graph model consistently preserves these structures, leading to more stable and detailed 4D) reconstructions.
D ADDITIONAL ABLATION AND ANALYSIS
D.1 ABLATION STUDY ON KEY NON-KEY RATIO.
We select the uncertainty threshold by keeping the top 2% (1000-th) of all Gaussians. This strategy ensures broad spatial coverage while filtering unreliable nodes. find this setting to be robust across scenes and base models. To further evaluate the impact of the threshold, we conduct ablation study with different thresholds. As shown in Table S6, the results remain stable, and our method is not sensitive to this parameter. We vary the ratio from 0.005 to 0.04 and observed consistent performance, including our chosen value of 0.02, which demonstrates that using this well-justifie parameter is reasonable and effective.
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Table S6: Ablation study on the uncertainty thresholds. Results are reported relative our chosen ratio
threshold (0.02).
% of Key Gaus.
Relative PSNR1
Relative SSIM
Relative LPIPS
MoSca (baseline)
0.994
0.995
1.024
0.005
1.000
1.000
1,000
0.010
1.001
1.001
0,999
0,020 (reference)
1.000
1.000
1.000
0.030
1.001
1.001
0.998
0.040
1.001
1.000
1.000
D.2 ABLATION STUDY ON COLOR THRESHOLD
Table S7:
Ablation study on the color threshold l/e using Objaverse dataset. Results are reported relative to our chosen threshold (n: = 0.50). 1.00 means no thresholding.
7/
Relative PSNR
Relative SSIM
Relative LPIPS.
MoSca (baseline)
0.980
0.992
1.115
0.01
0,993
0,999
1,023
0.10
0,996
1.000
1,01
0.20
0.994
1.000
1.014
0.40
0.998
1.000
1.008
0.50 (reference)
1.000
1.000
1,000
0.80
1.001
1.000
1.001
1.00
0.997
1.000
1.010
The color threshold ne (see Equation 4) is introduced prevent incorrect uncertainty estimation from Equation 3 when the prior Gaussians have not yet converged on certain pixels in the input images. We further study the influence of ne on the Objaverse dataset. As reported in Table S7, performance degrades when is set to 1.0, which corresponds to disabling the threshold entirely. In this case, the model incorrectly trusts Gaussians with large color errors and assigns them falsely low uncertainty. On the other hand, when is reduced below 0, many genuinely low-uncertainty Gaussians are mistakenly treated as unreliable, which again harms reconstruction quality. Although these two extremes lead to worse performance, we find a broad performance plateau between [0.4, 0.8), showing that the hyperparameter is easy to select and not sensitive in practice. Across all tested values of nJc, approach consistently outperforms MoSca.
D.3 ABLATION STUDY ON THE SIGNIHICANT PERIOD THRESHOLD
As discussed in Section 4.2 of the main paper, Threshold or SPT) to filter out Gaussians that are well observed for only a short duration (no more than four frames). Such transient Gaussians contribute little to motion propagation and often lead to weak or unreliable graph connections. In our experiments, we uniformly set the SPT to 5 for all datasets. To further assess its influence. we evaluate several values of SPT, as shown in Table S8. We observe a broad performance plateau when SPT > 3, indicating that the model is stable across a reasonable range of choices. Performance drops when the threshold is set to (i.e., disabling the filtering by SPT) which introduces short-lived Gaussians into the key graph, which matches the intuition described above. For all choices SPT, our method consistently achieves higher performance than MoSca across all tested values.
peper--prrmeter (namely, Significant Period
D.4 ABLATION STUDY
SCALING FACTOR
The scaling factor [a,, T2] controls the relative weight when transforming 2D uncertainty into axis-aligned 3D uncertainty. The key insight that the reliability of depth varies with camera motion. In scenarios where the camera undergoes large translation in the z-y plane, the depth becomes better constrained. In such cases, down- down-weightn weighting the depth component (using a smaller r' reduces noise in the uncertainty estimate. This intuition aligns with geometric models such Direct Sparse Odometry
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Table S8: Ablation study on the Significant Period (SPT). Results are reported relative our chosen significant period threshold (SPT 5).
SPT
Relative PSNR1
Relative SSIM1
Relative LPIPS].
MoSca (haseline)
0.980
0.992
1.109
(i.c., no thresholding)
0.994
0,999
1.016
3
0.998
1.000
1.006
5
1.000
1.000
1.000
0.996
1.000
1.007
10
0.996
1.000
1.004
Table $9: Ablation study on the depth uncertainty scale ratio T=,. Results are reported relative to our chosen ratio threshold (1.00).
T= Baseline
Relative PSNR
Relative SSIM
Relative LPIPS
MoSca (baseline)
0.989
0,992
1.100
0.01 1.00 (ours, reference)
1.001
0,999
1.005
1.001
1.000
1.000
(Engel al., 2017), which models depth estimates with variance term that inversely proportional to the baseline, i.c., AZ x t. where AZ is depth standard deviation and 6 is camera baseline or translation.
Our design principle is that the default setting [1, 1, is valid, and once the camera motion is unbalanced across directions, adjusting the weights can improve robustness. For all experiments, we set [y,, rs] [1. 1, 0.01]. We also evaluated other values of and did not observe notable differences small value works consistently). For datasets such as DyCheck, DAVIS, and NVIDIA, which have limited camera movement along the depth axis and often emphasize rotation or small shifts, using a smaller Ta improves the stability the uncertainty estimation. For objaverse, where the camera moves across all three spatial directions, adjusting rs from 1.0 to 0.01 has minimal side effect, as shown Table $9.
Table $10: Ablation for comparable time on DyCheck dataset.
Setup
PSNR1
SSIM
LPIPS.
MoSca (baseline)
19.32
0.706
0.264
Ours (same time with MoSca)
19.41
0.710
0.254
Ours (full time)
19.63
0.715
0.249
D.5 ANALYSIS ON RUNTIME
We provide a detailed runtime analysis (all measured on single NVIDIA H100 GPU): (1) Uncertainty estimation: since the rendering speed is fast (> 60 FPS), this step is efficient and introduces negligible
overhead. (2) Graph construction: -3 sec/image. For a typical input sequence of 200 images in DyCheck (for DAVIS, it ranges 50~90 frames), this totals around 10 minutes.
(3) Training
time: -4 sec/image, leading to 13 minutes for 200 input images. Importantly, our method does not require a fully optimized base model, which. To assess efettifffecivesess,, we run USPL.AT4D on an under-trained base model for the same total time fully trained bascline and observe comparable performance as shown in Table $10. Although the performance drops compared with our full model, it still have better performance than MoSca training with the same time.
To better understand how the time usage increases with video length, we further curate training scenes with different sequence lengths from the Objaverse dataset. Figure S3 shows the runtime of MoSca and our method relative to the input video length. The runtime our method exhibits a smaller slope in both stages compared with MoSca.
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MoSca Precompute
MoSca Reconstruct
120
USp st4D Graph initialization
100
Training
USp at4D Total
60
40
100
200
300
400
500
600
700
800
Video Length
Time usage. MoSca involves a preprocessing stage followed by reconstruction stage. MoSca Total is the of MoSea Precompute and MoSca Reconstruct. With the Gaussian priors from MoSca, method includes graph initialization stage (which covers uncertainty estimation and graph construction) and an overall training stage. USplaI4D Total is the sum of Gruph Initialization and Overll Training.
Figure S3:
1= 0
1 32
Input at 32
Input
SoM
USplat4D (Ours)
Groundtruth at t 32
Figure $4: Failure tracking on textureless surface. The input camera moves a circular trajectory around a chick that remains quasi-static. We show the tracking and reconstruction results at frame 32 for both SoM and USPLAT4D (ours). Because the chick's 's surface lacks texture, the tracks sampled at the initial frame (t 0) drift and accumulate incorrect motion under SoM, eventually causing the reconstruction collapse. In contrast, method able to partially recover geometry and produce more stable tracking.
E ANALYSIS OF CHALLENGING CASES
Although our model partially relies on the tracking quality of the initial 4D Gaussians and the geometric cues available during their observation period, both the quantitative and qualitative results demonstrate strong robustness in restoring or preserving shape under partial visibility and imperfect tracking. The propagate stable cues across space and time. However, the framework still inherits the fundamental limitations of the underlying 4D prior. When the prior does not contain any reliable motion or geometric information, the uncertainty model has meaningful signal to propagate, and therefore cannot fully recover the missing structures. Here, we further examine the impact of most common challenging cases: textureless regions, fast motion, and deformable objects.
iettaainty-guided graph allows the model to downweight unreliable motion and
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Input
MoSce
Curs
MoSce
OuT
Input
MoSce
Ours
MoSca
Ours
Comparison of reconstruction results fast-motion objects. For each scene, the top shows the overall view rendered from input views novel views, the three rows below highlight the fast-motion regions across ten successive time frames.
Figure S5:
E. TEXTURELESS REGION
In textureless areas, the vision foundation model produces unreliable tracks, which turn causes SoM and MoSca to generate incorrect dynamic Gaussian priors. When our model receives these flawed priors, the uncertainty-guider graph attempts to propagate geometric information under the guidance of uncertainty across time and space. However, when the underlying correct information is rare, the propagated motion inevitably suffers from the poor initial tracking and reconstruction. In the Objaverse experiments, such textureless regions noticeably reduce quantitative performance compared to real-word weal-world datasets with richer appearance cues. Under our cireling-caman cameea setup, a typical failure case is shown in Figure $4. The Gaussians from SoM drift with the camera instead
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Input
MoSea
USplat4D (Ours)
Input
MoSca
USppaa4D (Ours)
Figure S6: Qualitative deforming objects. First (Davis dancing scene): The subject's
exhibit strong non-rigid motion throughout dance. Second row (DyCheck handwavy scene): The hand undergoes continuous pose changes with significant articulation. Third row (DyCheck mochi- mochi-high-liv high-five scene): The cat's head rotates substantially. MoSca often produces drifting geometry where the cars or back of the neck separate from the head, while our method preserves consistent structure across time and viewpoints.
of representing stable geometric structures, while method stabilizes the overall motion but still inherits errors local regions where tracking is fundamentally unreliable.
E.2
FAST MOTION
Monocular 4D reconstruction under fast motion remains highly challenging. yet our method demon- strates strong robustness in such scenarios. By employing an uncertainty-weighted DQB loss together with trainable motion parameters Pt,z, the model deviate from node-based interpolation when uncertainty signals unreliable priors, enabling recovery of local fast motion even under incorrect initialization (Figure S5). This capability arises from relaxing the hard constraints imposed by mode-based interpolation. In contrast, existing monocular 4D Gaussian splatting approaches, such as SoM and MoSca, rely heavily on motion regularizers that favor smooth, temporally coherent trajectories, implicitly biasing them toward slow moderate motion. Their motion models further limit fast dynamics: SoM restricts motion to small set of bases, capping allowable local deformation, while MoSca interpolates trajectories via DQB on scaffolds, producing stable but nearly rigid motion. When scaffolds are missing in high-motion regions, this interpolation becomes a hard constraint that prevents Gaussians from following rapid. input-aligned motion. Despite these improvements, reconstructions in fast-motion regions still inherit rolling-shutter distortions from the input video and rapid motion further challenges the preservation of multi-view consistency. Addressing such artifacts remains an open problem in monocular 4D reconstruction and is beyond the scope of this work.
E.3 DEFORMING OBJECTS
Considering that deformable objects pose greater challenges than near-rigid ones, conduct qualitative evaluation of monocular 4D reconstruction in highly deforming scenarios. Figure S6 compares novel-view synthesis results of our method and MoSca across several subjects. Our reconstructions align more faithfully with the underlying geometry, particularly in regions with large non-rigid motion. This robustness stems from our uncertainty- spatio-temporal graph and nncertainty-weighter DQB loss, which softly regularize non-key nodes while allowing deviations when priors are unreliable. Consequently, even with imperfect Gaussian initialization from MoSca, framework recovers fine-scale deforming geometry while maintaining spatio-temporal consistency. In contrast, MoSca interpolates all Gaussians using scaffold- ccaffold-bssed skinning weights. While effective with sufficient scaffold coverage, this design becomes brittle when scaffolds fail to capture corners, extremities, or thin structures due to hyperparameter sensitivity or inaccurate 2D priors. Gaussians in these regions become overly constrained, leading to distortions missing parts, seen in highly
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deformable structures such human limbs or the cat's ear in Figure S6. Despite these improvements, reconstructing highly deformable regions remains challenging when priors are severely inaccurate or observations sparse, indicating directions for future work.
F SOCIAL AND BROADER IMPACT
Our work advances dynamic 3D scene reconstruction and novel view synthesis from monocular videos, with potential applications in AR/VR, physical scene understanding. digital content creation, and human-computer interaction. By modeling uncertainty and improving synthesis under extreme viewpoints, our method contributes to more robust and accessible 4D modeling. While the method has positive cases, could be misused for synthetic content manipulation. Our method does involve sensitive data, and care should be taken in downstream applications to ensure responsible use.
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Reviews:
[{"review_id": "T1pwD5c5FQ", "reviewer": "Reviewer_3BJu", "summary": "This work introduces USplat4D, an uncertainty-aware dynamic Gaussian Splatting framework for monocular 4D scene reconstruction. The key idea is to explicitly estimate per-Gaussian, time-varying uncertainty and leverage it to organize Gaussians into a spatio-temporal graph. This uncertainty-aware graph informs the selection of reliable (“key”) and less reliable (“non-key”) nodes, guiding motion propagation and adaptive optimization for more robust geometry and novel view synthesis, especially under occlusions and extreme viewpoints. Experiments on several real and synthetic datasets demonstrate superior consistency, quality, and robustness versus state-of-the-art baselines in both quantitative and qualitative settings.", "questions": "1. How does the choice of the threshold $ \\eta_c $ in Eq. 7, or the scaling constants $ (r_x, r_y, r_z) $ in Eq. 9, affect downstream geometry and rendering quality? How is the method robust to different settings? \n2. Can you provide more analysis on the time consumption.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Strength:\n1. The paper is well-written and easy to follow.\n2. Usplat achieve good result against recent baselines.", "weaknesses": "Major Weakness:\n1. The overall pipeline involves multiple stages and components, baseline reconstruction model Mosca is already complex, USplat makes it appear rather complex and add time complexity.\n2. Usplat ommit a evaluation on the nvidia dynamic dataset.\n\nMinor Weakness:\n1. In line 60, The should be \"the\"", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "8IGwPTgUnD", "reviewer": "Reviewer_1czs", "summary": "This paper presents USPLAT4D, a method for reconstructing 4D scenes from monocular video that explicitly models per-Gaussian, per-frame uncertainty and integrates it into the optimization process. The approach consists of three main components: (1) uncertainty estimation based on color reconstruction variance and convergence indicators, (2) uncertainty-aware k-NN (UA-kNN) graph construction with key/non-key node partitioning, and (3) unified optimization using strong anchor losses for key nodes and Dual Quaternion Blending (DQB) interpolation with uncertainty-weighted losses for non-key nodes.", "questions": "- What modifications would be needed to handle scenes that don't fit the single-object, near-rigid assumption?\n- Are there quantitative metrics that could predict when the method will fail based on scene characteristics (e.g., rigidity, object count, motion complexity)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Clear conceptual contribution: The paper articulates a compelling and principled insight—\"frequently observed Gaussians serve as stable motion anchors\"—and successfully integrates this into a quantitative optimization framework. The uncertainty-aware formulation is well-motivated.\n2. Effective spatial-temporal propagation: The UA-kNN approach with key/non-key partitioning provides an elegant mechanism to propagate motion from reliable nodes to uncertain ones, effectively suppressing drift in occlusion scenarios.\n3. Depth-aware uncertainty modeling: The propagation of 2D errors to 3D anisotropic covariances via camera rotation explicitly accounts for depth-directional uncertainty, addressing a practical issue (depth distortion) that many methods overlook.", "weaknesses": "1. Dependence on initialization: The method relies on pretrained dynamic Gaussian models (SoM/MoSca) for initialization. When initial tracking completely fails (e.g., textureless regions, extremely fast motion), uncertainty estimation alone cannot recover from poor initialization. While the authors acknowledge this, more analysis of failure modes would be valuable.\n2. Hyperparameter dependencies: Multiple design choices affect performance (key/non-key ratio ~2%, significant period ≥5 frames, threshold η_c). The automatic adaptability to different scene characteristics appears limited, and more ablation studies on these choices would strengthen the paper.\n3. Potential graph connectivity issues: The uncertainty-weighted edge formation may exclude low-uncertainty nodes, potentially missing important scene structures that are simply less frequently observed. This could lead to incomplete propagation in certain scenarios.\n4. More fundamentally, this approach appears inherently limited to single-object, near-rigid scenarios. The core assumption—that frequently observed Gaussians serve as stable motion anchors—explains why results are strong on object-centric scenes like \"paper-windmill,\" \"spin,\" and Objaverse datasets, but raises concerns about applicability to scenes with independent motions or highly non-rigid deformations. The authors should explicitly acknowledge this as a scope constraint and provide analysis beyond curated object-centric benchmarks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "fSANwSlSmp", "reviewer": "Reviewer_57Ji", "summary": "This work reconstruct 4D Gaussian Splatting for dynamic scenes from monocular videos. The proposed technique refines an initial 4DGS reconstructed by other method. They first classify each Gaussian into key and non-key nodes based on the uncertainty derived from rendering loss. Key nodes are finetuned to remain their original position and motion, while non-key nodes are regularized by the blended motion from nearby key nodes. Results on three datasets show improvement comparing to previous work.", "questions": "Can the author provide some space and time complexity analysis for the graph pre-processing step (how they scale with the number of Gaussians)?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "I like the idea of to identify anchor Gaussians based on the rendering loss uncertainty. This may be useful for many other tasks which require some estimate of the importance of a Gaussian.", "weaknesses": "The original 4DGS seems to have much better quantitative and qualitative results from their paper, while the reported 4DGS looks much worse. For example, they report >30 PSNR on the synthetic dataset but the PSNR are all <20. I wonder the reason. Does the evaluated dataset in this paper exhibit some more challenge? Also, is will be even more convincing if the proposed method can be evaluated on the same set of datasets used in 4DGS.\n\nI'm concern about the scalability of the graph pre-processing stage to scene with more Gaussians (e.g., larger scene, more complex scene, or long sequence). The existing graph pre-preprocessing already requires additional 10 more minutes. The computation and storage cost may increase even further and make it unpractical.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "qiLlrPMIEt", "reviewer": "Reviewer_SfVo", "summary": "This paper presents a novel framework for monocular 4D scene reconstruction, emphasizing the critical role of uncertainty estimation in dynamic Gaussian splatting. The authors address key challenges in reconstructing dynamic scenes from monocular videos, particularly under occlusions and extreme viewpoint changes, by explicitly modeling the uncertainty associated with each Gaussian component in the scene. The proposed approach constructs an uncertainty-aware graph that guides the selection of reliable scene components (key nodes) and propagates motion information accordingly, resulting in improved geometric consistency and rendering quality.", "questions": "How does the uncertainty-guided graph approach specifically improve robustness in occluded or fast-moving regions? Are there quantitative metrics or qualitative assessments that distinctly highlight these enhancements?\n\nWhat is the added computational cost of incorporating the uncertainty estimation and graph optimization steps? Are there insights into throughput or latency impacts, especially for real-time or large-scale scenarios?\n\nDid the experiments include scenarios with severe occlusion, sparse observations, or highly ambiguous regions? How does the model's uncertainty estimation perform under these challenging conditions?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The primary contribution of explicitly modeling and utilizing uncertainty within the scene reconstruction process represents a significant advancement. This approach effectively addresses a persistent challenge in monocular 4D reconstruction—robustly managing occlusions and view-dependent ambiguities.\n\nThe paper presents a systematic development of the framework, encompassing uncertainty estimation, graph construction, and optimization. It offers a comprehensive and cohesive pipeline that integrates seamlessly with existing Gaussian splatting methods. The ablation studies convincingly demonstrate the importance of each component, underscoring how uncertainty modeling enhances reconstruction quality.\n\nThe authors validate their approach across multiple datasets, including challenging synthetic benchmarks designed to rigorously test robustness. Both quantitative metrics and qualitative results substantiate that USPLAT4D delivers superior performance, especially in maintaining scene details from extreme viewpoints.", "weaknesses": "The effectiveness of the proposed framework hinges critically on the precision of the uncertainty estimation. Any inaccuracies in this component could propagate through the graph construction and optimization stages, potentially compromising the stability and fidelity of the reconstructed scene.\n\nWhile the paper demonstrates significant improvements under various conditions, it provides limited discussion on scenarios where the uncertainty modeling may underperform. A more comprehensive analysis of failure modes—such as highly textureless regions or rapidly changing scenes—would strengthen the robustness claims.\n\nIncorporating per-Gaussian uncertainty estimation and uncertainty-guided graph optimization introduces additional computational complexity. The paper lacks detailed analysis regarding the impact on processing time, resource requirements, and scalability to larger or real-time applications.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)