MoE-GS: Mixture of Experts for Dynamic Gaussian Splatting
Authors: In-Hwan Jin
Hyeongju Mun
Joonsoo Kim
Kugjin Yun
Kyeongbo Kong
Venue: ICLR 2026 Poster (2026)
Keywords: Gaussian Splatting
Dynamic Scene Reconstruction
Mixture of Experts
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: MoE-GS reconstructs dynamic scenes using 2D weight splatting and learnable routing in a Mixture-of-Experts framework

Abstract: Recent advances in dynamic scene reconstruction have significantly benefited from 3D Gaussian Splatting, yet existing methods show inconsistent performance across diverse scenes, indicating no single approach effectively handles all dynamic challenges. To overcome these limitations, we propose Mixture of Experts for Dynamic Gaussian Splatting (MoE-GS), a unified framework integrating multiple specialized experts via a novel Volume-aware Pixel Router. Unlike sparsity-oriented MoE architectures in large language models,
MoE-GS is designed to improve dynamic novel view synthesis quality by combining heterogeneous deformation priors, rather than to reduce training or inference-time FLOPs. Our router adaptively blends expert outputs by projecting volumetric Gaussian-level weights into pixel space through differentiable weight splatting, ensuring spatially and temporally coherent results. Although MoE-GS improves rendering quality, the increased model capacity and reduced FPS are inherent to the MoE architecture. To mitigate this, we explore two complementary directions: (1) single-pass multi-expert rendering and gate-aware Gaussian pruning, which improve efficiency within the MoE framework, and (2) a distillation strategy that transfers MoE performance to individual experts, enabling lightweight deployment without architectural changes. To the best of our knowledge, MoE-GS is the first approach incorporating Mixture-of-Experts techniques into dynamic Gaussian splatting. Extensive experiments on the N3V and Technicolor datasets demonstrate that MoE-GS consistently outperforms state-of-the-art methods with improved efficiency. Video demonstrations are available at cvsp-lab.github.io/MoE-GS.

Published as a conference paper at ICLR 2026
MOE-GS: MIXTURE OF EXPERTS FOR DYNAMIC GAUS- SIAN SPLATTING
In-Hwan Jin Hyeongju Mun Joonsoo Kim?
Kugjin Yun?
Kyeongbo Kong"t
Pusan National University, {ihjin, 201924128, kbkong) @ @pusan.acaa ac.kr
2 Electronicc and Telecommunications Research Institute, (joonsookim, kjyun) @etri.re.kr
ABSTRACT
Recent advances in dynamic scene reconstruction have significantly benefited from 3D Gaussian Splatting, yet existing methods show inconsistent performance across diverse scenes, indicating no single approach effectively handles all dynamic chal- lenges. To overcome these limitations, we propose Mixture of Experts for Dynamic Gaussian Splatting (MoE-GS), a unified framework integrating multiple specialized experts via a novel Volume-aware Pixel Router. Unlike sparsity-oriente MoE archi- tectures in large language models, MoE-GS is designed to improve dynamic novel view synthesis quality by combining heterogeneous deformation priors, rather than reduce training or inference-time FLOPs. Our router adaptively blends expert outputs by projecting volumetric Gaussian-level weights into pixel space through differentiable weight splatting, ensuring spatially and temporally coherent results. Although MoE-GS GS improves rendering quality, the increased model capacity and reduced FPS are inherent to the MoE architecture.
mitigate this, we explore two complementrry directions: (1) single-pase multi-expert rendering and gate- aware Gaussian pruning. which improve efficiency within the MoE framework, and (2) distillation strategy that transfers MoE performance to individual experts, enabling lightweight deployment without architectural changes. To the best knowledge, MoE-GS is the first approach incorporating Mixture-of-Exxe of-Experts techniques into dynamic Gaussian splatting. Extensive experiments on the N3V and Technicolor datasets demonstrate that MoE-GS consistently outperforms state- of-the-art methods with improved efficiency. Video demonstrations are available at cvsp-lab. gi github..t.. 1o/MoE-GG GS
INTRODUCTION
Realistically modeling dynamic scenes from real-world data is fundamental challenge for training future AGI models, creating immersive content for spatial computing. and enabling embodied agents to effectively perceive and interact with their environments. Recent advances in novel view synthesis, particularly Neural Radiance Fields (NeRF) (Mildenhall et al.) 202I]. have significantly improved the quality of static scene reconstruction. However, NeRF's implicit representation and intensive ray-tracing introduce substantial computational overhead, limiting real-time applicability. To address these limitations, explicit representations such as 3D Gaussian Splatting (3DGS) (Kerbl al. 2023) have emerged, achieving real-time rendering without compromising visual fidelity.
Recent efforts have extended Gaussian-based representations dynamic scenes, introducing diverse approaches such as MI.P-asee deformation networks (Wu et al. 2024 Bae et al. 2024), polynomial- based motion models (Li al. 2024), and interpolation- these methods achieve promising results in handling specific deformation types, our empirical analysis (Fig. reveals that no single approach consistently generalizes across varied real-worke dynamic scenarios. Specifically, our analysis identifies three key limitations:
methods (Lee al., (024). Although
Different reconstruction methods exhibit significant variability in performance across scenes, indicating that each model has a restricted range of optimal applicability (Fig. [T)) Such scenc-specific performance variations underscore the necessity for adaptive model selection.
Scene-level variations:
Spatial-level inconsistencies: Within same scene, reconstruction quality varies across different spatial regions when processed by distinct methods (Fig. (p). This spatial variability demonstrates that no single model consistently excels across all regions of a given scene.
'Equal contribution.
Corresponding author.

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Published as a conference paper at ICLR 2026
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a. Scene PSNR per Model Different models perform best on different scenes
b. Patch-wise PSNR per Model
c. Time PSNR Madel Different models perform best at different timesteps
Different models perform best in different spatial regions
Ex4DOS (mowr)
40Gaussians (Hex.)
E-D3DGS (Per)
STG (Paly)
Figure I: Limitations of existing dynamic Gaussian splatting methods. (a) Scene-level: No single method consistently dominates across scenes. (b) Spatial-leved Different spatial regions favor different deformation models. (c) Teepl:al-level: The best- cest-performig method changes over time within the same scene. We also visualize representative motion trajectories of four (Green), STG (Purple), F-D3DDS (Pink), and Ex4DGS (black)-to to illustrate their distinct motion behaviors. Additional video results are provided on the project page.
Temporal fluctuations: Within a video sequence, the best rest-performing method changes dynamically from frame to frame, reflecting the inherent temporal complexity of real- world dynamics (Fig. [T]. Such temporal fluctuations highlight the inability of individual models to consistently capture complex temporal patterns.
These variations arise from the heterogeneous inductive biases of existing dynamic GS methods. Each deformation model is naturally suited a particular motion regime: HexPlane-based canonical deformation produces smooth, highly regularized trajectories that work well in static or low-motion regions: per-Gaussian volumetric deformation captures fast yet coherent flows; polynomial trajectory models favor globally smooth, low-curvature motion; and interpolation-based methods often yield locally diverse and irregular trajectories. Because real-world scenes typically contain mixture of these motion patterns, single deformation prior can perform optimally across all spatial and temporal regions. A more detailed, expert-specifee motion analysis is provided in Appendix [
Motivated by these insights, we propose Mixture of Experts for Dynamic Gaussian Splatting (MoE- GS), the first unified framework integrating multiple dynamic Gaussian models through a Mixture- of-Experts (MoE) architecture. Instead of relying selects and blends multiple specialized experts, dynamically adjusting its decisions according to spatial, temporal, and scene-specific dynamics. It is important clarify that, unlike sparsity-driver MoE architectures in large language models. MoE-GS not intended to reduce FLOPs or memory usage. Our primary goal is to increase representational capacity and improve dynamic reconstruction quality by combining heterogeneous deformation priors; the efficiency techniques we introduce are complementary mechanisms to keep this additional cost practical.
single expert model, MoE-GS adaptively
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Published as a conference paper at ICLR 2026
A key innovation of MoE-GS is the Volune-aware Pixel Router, which adaptively blends expert outputs with spatial and temporal coherence. Rather than assigning routing weights solely at the pixel or Gaussian level-either ignoring volumetric structure or facing optimization difficulties our router projects Gaussian-level decisions into pixel space using differentiable weight splatting. This provides adaptive, per-pixel weighting that effectively captures temporal and view-dependent variations, ensuring stable optimization. While MoE-GS significantly improves rendering quality, its multi- expert inference inherently increases computational overhead. To address this, we explore two independent strategies. First, propose single-pass paagle-passssse multi-expent rendering and gate-aware Gaussian pruning, which improve runtime efficiency by eliminating redundant rasterization and removing low-contributing primitives. This strategy is particularly effective when the number of experts is small (e.g., N = or N = 3). Second, we introduce a knowledge distillation strategy that trains individual expert models using ground-truth supervision and pseudo-labels generated by the optimized MoF model, weighted at the pixel level by the router's 's predictions. This allows each expert to approximate the performance of the full MoE-GS without modifying its architecture. The benefit of this approach becomes more evident as the number of experts increases (e.g., N = 4 or higher), where directly running the full MoE incurs substantial computational cost.
In summary, our contributions include:
MoE-GS, the first dynamic Gaussian splatting framework employing a Mixture-of-Expert architecture, enabling robust and adaptive reconstruction across diverse dynamic scenes.
A novel Volume-aware Pixel Router that integrates expert outputs through differentiable weight splatting, achieving spatially and temporally coherent adaptive blending.
Efficiency of MoE-GS is improved through single-pass multi-expert rendering and gate- aware Gaussian pruning, while separate knowledge distillation strategy trains individual experts with pscudo-labels from the MoE model, enhancing quality without modifying the architecture.
RELATED WORKS
2.1 DYNAMIC NOVEL VIEW SYNTHESIS
Dynamic Novel View Synthesis aims to reconstruct novel views from sparse observations of tempo- rally varying scenes, a challenging yet crucial task in computer vision (Gao et al. 2021 Park et al.) 2021). Effectively modeling complex scene dynamics often requires advanced temporal modeling techniques, which substantially increase computational complexity and memory usage.
Implicit methods address scene dynamics by learning deformation fields that map points from canonical space to observed frames. These methods typically rely on MLPs but differ in how they embed spatial and temporal information. Wu et al. (Wu al., 2024) (4DGaussians) employ coordinate- conditioned embeddings (similar to HexPlane-style features) combined with lightweight MLPs for deformation estimation. Bae et al. (Bae al. 2024) (E-D3DGS) instead adopt Per-Gaussian embed- dings with multiple volumetric fields and coarse-to-fine strategy to improve temporal modeling. In contrast, explicit methods directly parameterize temporal variations of Gaussians without relying on canonical deformation fields. Polynomial-bas models such as STG (Li et al. 2024) describe trajec- tories with parametric functions, while interpolation- based models such as EX4DGS (Lee et al. 2024) reconstruct dynamics by interpolating between keyframes. These approaches simplify optimization by avoiding canonical deformation fields, but often struggle with complex motion.
While these methods achieve state-of-the-at results for specific types of deformations, they lack the flexibility to generalize across diverse dynamic scenarios. contrast, our MoE-GS framework integrates multiple Gaussian-bart based models within mixture-of of-reee-tt--etsss-------- architecture, dynamically selecting and blending expert outputs to adaptively reconstruct a wide range of scene dynamics.
2.2 MIXTURE OF EXPERTS
MoE is an ensemble learning technique where multiple expert models specialize in distinct subtasks, with a gating network dynamically selecting the most relevant experts per input instance. MoE architectures have demonstrated scalability and efficiency by introducing sparsity (Shazeer et al. 2017), enabling conditional computation to scale model capacity without excessive computational
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Mixture of Experts Dynamic Gaussian Splatting
Candidate Experts
HexPtane embedding
MLP
Rastertzer
Rasterizer
Rasterizer
Per Gaussian embedding
Rsi
Ag. a.
MLP
Exy
Expert
Expert
Expert N
Interpolation
Expert
Expert
Expert N
Router
Polynomial
Stage 1
Stage 2
Figure 2: Overview of the MoE-GS framework. In Stage 1 (Expert Training), each expert is inde- pendently trained to reconstruct the dynamic scene by optimizing its own Gaussian representation, ensuring diverse modeling capabilities. In Stage (Router Training), with all expert parameters fixed, the Volume-aware Pixel Router learns to dynamically blend expert-rendered images by com- puting spatially and temporally adaptive gating weights. The Candidate Experts (right) illustrate diverse Staussian-based reconstruction methods integrated into our framework, including HexPlane Embedding- Per-Gaussian embedding-basee Interpolation-basee and Polynomial-bass ap- proaches, each suited for capturing different scene dynamics.
cost. This approach has been particularly successful in large-scale deep learning models, including large language models (LLMs) for machine translation (Lepikhin et al. [222] Fedus et (0022) and computer vision applications such as multi-task learning (Ma et al. 2018). face forgery detection (Kong et al. 2022). and anomaly detection (Meng et al. 2024).
Inspired by MoE's s ability to dynamically integrate specialized models, we propose applying MoE to Dynamic Gaussian Splatting, enabling learnable expert selection process for dynamic scene reconstruction. Unlike existing methods, which statically select a single model for all frames, our MoE-GS learns adaptively combine multiple experts based scene characteristics. Furthermore, to mitigate the increased computational overhead associated with MoE, we introduce a knowledge distillation pipeline (Hinton al. 2015 Xie al. (2024), allowing experts to achieve near-MoB performance with significantly reduced computational cost.
3 METHOD
We propose a framework that closely follows the classic MoB structure, leveraging its proven effectiveness across diverse fields to address the problem of dynamic scene reconstruction.
3.1 PRELIMINARY
We briefly review the standard Mixture-of-Expeer (MoE) architecture, which forms the basis for our proposed method. A standard MoE consists of multiple parallel expert networks E1, E2,..., Ex and a Router that adaptively combines expert outputs based on the input. Formally, given an input x, the MoE output is computed as follows:
MoE(r) = - Ex(x).
k=1
where FA (z) is the output of the k-th expert, and (x) represents the corresponding gating weight computed by the Router. The gating weights are typically computed by a Router, often implemented as lightweight neural network (e.g., linear layer or MLP), defined as:
Ge (x) Softmax R2 (z));
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Pixel Router
Volume-aware Pixel Router
Pixel weight
Pixel weight
Softmax
Time
Shared MLP
Shared
Shared
MLP
MLP
View
Expert
Image
Volume-sware Representation
Rasterizer
Rasterizer
Rasterizer
Volume Router
Par-Gaussian weight
weight
Copy
Volume aware Representation
Volume: aware Representation
Easy optimize Router
Easy optimize
Figure 3: Comparison of Router Architectures. The Pixel Router (top-left) assigns weights purely the pixel level, ignoring volumetric features. The Volume Router (bottom-left) uses Gaussian-level weights but is difficult to optimize. Our Volume-aware Pixel Router (right) combines Gaussian-level weights with rasterization-base splatting.
where Rg (x) is the router output for the k-th expert, computed as:
R4 (z) x T Wr,
with W,, being the k-th column vector of trainable weight matrix W,. This structure enables adaptive blending of experts based input characteristics, enhancing performance and flexibility.
In the following sections, we detail how we extend this basic MoE architecture for dynamic scene reconstruction using Gaussian-based representations.
3.2 MIXTURE OF EXPERTS FOR DYNAMIC GAUSSIAN SPLATTING
As illustrated in Fig. 2 propose adaptation of the MoE architecture dynamic Gaussian splatting. Our framework integrates diverse Gaussian- based dynamic reconstruction models, each treated expert, allowing us leverage their strengths to improve rendering fidelity. Unlike traditional MoE models operating solely in feature space, our approach performs expert selection and blending directly on rendered 2D images, enhancing flexibility and reconstruction quality across complex temporal and spatial dynamics.
Volume-aware Pixel Router, simple router might determine gating weights based purely on pixel-level features extracted by MIP conditioned on time and view direction (Fig. 3 top-left). However, without considering intrinsic Gaussian properties (position, rotation, scale, and opacity), this approach lacks volumetric awareness and struggles to accurately capture temporal and view- dependent variations.
Another approach assigns gating weights directly per Gaussian in 3D space (Fig. bottom-left). Here, each Gaussian's learned opacity is modulated by gating weights before rasterization into the 2D image space. However, demonstrated in experiments, directly optimizing gating weights in the fixed 3D Gaussian domain is challenging due to the indirect and complex relation between Gaussian parameters and their resulting pixel contributions.

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Published as a conference paper at ICLR 2026
To address these issues, introduce the Volume-aware Pixel Router (Fig. right), featuring three innovations: (1) explicit temporal and view-dependent encoding through learnable per-Gaussian weights; (2) intrinsic volumetric awareness via Gaussian attributes, enabling better expert differ- entiation; (3) rasterization-bann weight splatting into pixel space, facilitating pixel-level adaptive blending informed by Gaussian-level features.
This approach effectively combines volumetric richness with temporal-view consistency, enabling stable optimization and high-quality rendering.
Per-Gaussian Weight. Gaussian Splatting (Kerbl et al. 2023) represents a scene as set of Gaussians, each Gaussian 9. parameterized by position Jrg, rotation R2 and scale S
G e [x (x-))))
where Σ, R.S.STRR During splatting, overlapping Gaussians each pixel are depth-orderes and the pixel color C' is computed by combining each Gaussian's 's opacity o, and color e;:
C= E c
((--a)) with ag o.9.. j=1
(5)
To construct our router, we duplicate each Gaussian G. and replace its color attribute c, with learnable per-Gaussian weights encoding temporal and view-dependent variations:
w per
[w:,, w w. "
(6)
where each scalar adapts dynamically according to the viewing direction and time step f.
Adaptive Expert Gating via Weight Splatting. Inspired by STG (Li al. 2024), which uses feature splatting to compactly encode view-dependent radiance, we extend this concept by introducing rasterization-bast basedd weight splatting for adaptive expert gating. In contrast to STG's color refinement, our method dynamically regulates expert contributions through per- Gar-Gansiinn weights. Each 3D per-Gaussian weight w in Eq. (6) is projected onto the image plane through the differentiable Gaussian rasterizer, producing pixel-aligned embeddings (2 w2y (u). time (u) that aggregate the contributions of Gaussians overlapping pixel u. Specifically, these rasterized pixel-level weights are then refined by a lightweight MLP Φ:
R'(w) (2)( + o (wd) W2D
dir wiime(u) r(u)),
where r(u) is the pixel viewing direction. Expert gating weights G% are computed via softmax, then used to blend expert-rendered outputs TE,:
(M) = Σ Ch(l)) (u,, (n,
Gb(u)) = Softmax(Ri (u)).
We optimize Router parameters (w.er and b) using standard Gaussian Splatting loss terms (Ll and SSIM). Given varying expert convergence rates, we adopt two-stage training strategy to ensure stable and efficient optimization: experts are first optimized independently, and then the router is trained with these fixed experts (details in Appendix[B.]
Gaussian-Leve Interpretation of Pixel Gating, Although the router outputs per-pixel weights, these gating decisions originate from nar-Gaussian 3D signals (depth, visibility, deformation) and therefore carry 3D geometric structure. Because the weights are defined before rasterization, they can be naturally lifted back to the Gaussian domain in a eesponibilityyweighhe post-hoc 3D fusion. The responsibility-base lifting procedure is provided in Appendix D
manner, enabling simple
3.3 RENDERING AND PRUNING FOR EFFICIENCY
MoE-GS improves fidelity by leveraging multiple experts but introduces inefficiency -amely, repeated rasterization and many low-contribution Gaussians. To address this, we propose two tech- niques: Single-Pass Multi-Expert Rendering, which shares projection and rasterization across all experts, and Aware Gaussian Pruning, which removes Gaussians with negligible influence.

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Published as a conference paper at ICLR 2026
Single-Pass Multi- Rendering. In the baseline pipeline each expert is rasterized indepen- dently, leading to repeated projection and visibility computation across passes. We address this redundancy by merging all Gaussians into single batch and assign each Gaussian one-hot hot expert identity ej E RRK, The expert-specific color at pixel is computed as
CR(u) Σ (z(() (ej)s, Ø=1
where M is the total number of Gaussians across experts, (n( [1 am (u)) is the transmittance, aj the opacity, and (ey)a: selects Gaussians of expert k. This design computes projection and visibility only once for all Gaussians, while expert-specific outputs are separated during alpha blending. As result, redundant kernel launches and memory traversals inherent in the multi-pass pipeline are eliminated, improving GPU utilization without altering the rendering formulation.
Gate-Aware Gaussian Pruning. Independently trained experts often produce overlapping Gaus- sians with low contribution. To selectively remove these, accumulate the gradient of gating weights G2 with respect to the per-Gaussian weights w."", measuring how strongly each Gaussian influences the MoE image. The importance of Gaussian across all training views D is computed as
& Σ
(G D 3ro! (v)
1
(10)
Gaussians with & < 7 are progressively pruned, yielding a compact yet faithful representation. This strategy is effective because Gaussians with negligible gradients consistently show little impact the gating weights and thus contribute minimally to the final image. By removing only these Gaussians, the model reduces rendering cost without sacrificing visual fidelity, unlike naive ratio-based pruning which destabilizes optimization.
However, aggressive pruning alone is insufficient: as the number of experts increases, the total Gaussian count grows proportionally, and naively discarding large portions destabilizes optimization. To overcome this limitation, we introduce a complementary distillation strategy.
3.4 DISTILLATION- BASSED EXPERT TRAINING
To further reduce inference cost while maintaining stability, we adopt knowledge distillation (Hinton et al., 2015) Xie et al. 2024). Each expert F, is trained from seratch using both ground-truth images and MoE outputs supervision. We use the MoE-rendered image IMOE as pseudo-ground truth and routing weights G₁ as confidence scores. The distillation loss is
CKD X A(G LE., Gy (G.T)T) + (1 A) -(((1 (()-)))) IE., (1- Gb)) -MOB),
(11)
where combines LI and SSIM losses, and balances ground-truth vs. MoE supervision. This encourages each expert to specialize in reliable regions guided by ground truth while leveraging MoE outputs in uncertain areas. As a result, individual experts approximate the performance of the full MoE-GS model with significantly reduced complexity, enabling efficient real-time deployment.
EXPERIMENTS
This section evaluates the effectiveness of MoE-GS through experiments assessing its generalization, scalability, and deployability. We present results MoE-GS, including comparisons with baselines and detailed analyses of expert configurations. We further conduct ablation studies to first examine the contribution of the MoE architecture, specifically the individual experts and the routing mechanism. Next, we analyze the impact of our proposed efficiency strategies, such as single-pass rendering and pruning. Finally, we investigate the role and impact of our distillation strategy the model's overall performance.
4.1 EXPERIMENTAL SETUP
Implementation Details. To evaluate the robustness of MoE-GS under diverse real-world deforma- tions, we conduct experiments on two standard benchmarks for dynamic scene reconstruction: Neural 3D Video (N3V) (Li et al. 2022 and Technicolor (Sabater et al. 2017). Per-Gaussiaa weights
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Table Performance comparison on the N3V dataset (Li et al. 2022). Models were trained
dataset split into 150 frames. highlight best and second-best values for each metric.
PSNR (dB)
Model
Coffee Martini
Cook Spinach
Roasted Beef
Flame Salmon
Flame Steak
Scar Steak
Average
HyperReel Attal et al.
2023
28.37
32,30
32,92
28. 26
32.20
32.57
31.10
K-Planes Fiidoiich Kei ct 2023
29.99
32.60
31.82
30.44
32.38
32.52
31.63
Mix Moxelx
Wang et
29.63
32.25
32.40
29.81
31.83
32.10
31.34
3DGStream
Sun ct al.
2024
27.75
33.31
33.21
28.42
34.30
33.01
31.67
DASS (Liu ct 2024
28.15
33.83
33.54
28. 28.84
34,26
33.33
31.99
SaRO CS Yan
28.96
33.19
33.91
29.14
33.83
33.89
32.15
SwinGS Liu Banerjec 2024
27.99
33.66
34,03
28.24
32.94
33.32
31.69
4DCaussians
2014
29.09
29.76
32.01
E-D3DGS Bae 2024
30.04
33.11
33.85
30.49
32.77
33.70
STG' [Li
28.16
33.09
29.09
33,25
33.77
Ex4DGS Loe Loe
2024
28.72
33,24
33.73
29.33
33,91
33.69
32.10
MoE-GS (N=2)
30.27
33.43
34.05
30.66
32.92
33.90
32.54
MoE-GS (N=3)
30.27
33.86
34,90
30.92
34.52
34.88
33.23
MoE-GS (N=4)
30.43
34.24
35,20
30.92
34.38
34.42
33.27
Table 2: Comparison results on the Technicolor dataset Sabater al.
2017
PSNR (dB)
Model
Birthday
Fabien
Painter
Theater
Train
Average
DyNeRF Li
2022
32.76
35.95
31.58
31.80
HyperReel Attal et al.
2023
29.99
34.70
35.91
33.32
29.74
32.73
4DGaussians Wi al.
2024
30.87
33.56
34.36
29.81
25.35
30.79
STG (Li ct 2024
32.16
35.70
37.18
31,00
32.39
33.69
E-D3DGS Bac
2024
32.38
34.24
36.20
31.10
31.37
33.06
Ex4DGS
2174
32.35
35.18
36.60
31.77
31.37
33.45
MoE-GS (N=3)
33.26
36.26
37.63
32.88
32.89
34.55
optimized with the RAdam optimizer (Liuu et al., 2019), using learning rate of 0.5. Experiments are performed on NVIDIA A6000 GPUs. Additional implementation details are provided in Appendix
MoE-GS Expert Configurations. For clarity and reproducibility. we fix the expert sets used in all experiments. evaluate MoE-GS with = 3, experts using the following heterogeneous combinations: N = 2: {Ex4DGS (Lee al. 2024). STG (Li et al. 2024)); N ==3: 3: E-D3DGS (Bae et al., (002)); N 4: 4DGaussians (Wu et al.) (024). These fixed sets cover diverse deformation
families, and used consistently across all experiments. We note that this design choice is for clarity; MoE-GS itself remains deformation-agnostie
4.2 RESULTS ON MoE-GS
The expert set MoE-GS consists of multiple Gaussian- based dynamic reconstruction models, each capturing temporal deformations in distinct way. We include embedding- based methods (4DGaussians (W et al. 2024), E-D3DGS (Bae et al. 2024)), an interpolation-base (Ex4DGS (Lee 2024)), and polynomial-base method (STG (Li et al., 2024)), ensuring diversity across deformation representations. For broader comparison, we also evaluate against representative beRe-based baselines.
Baselines.
model
Quantitative Evaluation. Tables I and 2 present the per scene results on the N3V et 2022) and
Technicolor (Sabater al., 2017) datasets. MoE-GS achieves ffte-of-the--ad average performance on both datasets, consistently outperforming most baseline methods across individual scenes. This demonstrates its strong ability to generalize across diverse scenarios by selectively leveraging different experts. In addition, Table presents efficiency analysis of MoE-GS, showing that its inference and memory overhead can significantly reduced via Gate-Awe Aware Pruning. MoE-GS (N=2), which integrates STG (Li et al. 2024h and Ex4DGS (Lee et al. 2024) experts, outperforms both in PSNR while exhibiting moderate computational overhead. This suggests that combining distinct expert models enhance reconstruction quality without incurring excessive Detailed per-metric results, as well as additional evaluations on the monocular HyperNeRE dataset (Park et al. 2021). ) are provided in the Appendix C.I
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MoE-GS
4DGaussians
E-D3DGS
Cook, Cook_Spinaed Spinach
Ground Truth
EX4DGS
STG
Figure 4: N3V Qualitative Results Comparison of our MoE-GS with other dynamic Gaussian splatting methods on Neural 3D Video dataset (Li et al. 2022). black background highlight the method that produces the most visually accurate result among the baselines for each region.
Qualitative Evaluation. As shown in Figure 4 we present the gating weights predicted by the router, the outputs of individual experts, and the resulting MoE-GS image synthesized through their combination. These expert contributions align closely with the router's s predictions, indi- cating that MoE-GS effectively blends experts spatially adaptive manner. Additional quali- tative results provided in the Appendix C.2
Table 3: Efficiency evaluation with N=2 Expert
Variants on N3V (Li et al. 2022).
Model
PSNR
FPS
Memory
Li al. 2024
31.92
88.5
609.5
Ex4DGS [Le
2024
32.01
120
122.8
MoE-GS (N=Z)
32.82
44
878.7
MoF-GS ( 55% pruning)
32.80
351.2
MoE-GS ( 75% pruning)
32.45
101
281.3
Analysis of MoE Configurations. We conduct ablation studies to evaluate the design choices in MoE-GS, focusing on the architecture of the MoE router and efficiency optimizations.
1) MoE Router Variants We compared three router architectures for integrating expert out- puts: Pixel Router, Volume Router, and our pro- posed Volume-aware Pixel Router. Pixel Router performs blending in 2D image space using lightweight MIP, which supports stable opti- mization in image space. However, shown in Table Maple-and and 5 underperforms in quan- titative metrics and produces overly smooth results, failing to capture sharp boundaries, likely due to its lack of 3D spatial context. Volume Router, which blends expert Gaussians 3D space by adjusting their opacities, better preserves geometric structure but underperforms in detail fidelity. We observed frequent optimization instability and oversmoothing artifacts, especially in regions with fine Volume-aware Pixel Router achieves better balance: it maintains sharp
Table 4: Performance Comparison of Different MoE Router Variants
Model
PSNR
SSIM
LPIPS
Pixel Router Volume Router
31.12
0.952
0.022
32.05
0.951
0.022
Volume-aware Pixel Router
33.23
0.954
0.021
textures. In contrast, structural details and consistently outperforms other variants both quantitatively and qualitatively. We attribute this its 2D blending formulation, which supports stable training, combined with its use of 3D-aware weight splatting during training that injects geometric context into the routing process.
2) Efficiency Optimizations We evaluate the ef- fectiveness of our efficiency techniques-Sinn Pass Multi-Expert Rendering and Gate-Aware Gaussian Pruning by measuring their impact on PSNR, FPS, and memory usage. Without Single-Pass, each expert is rasterized indepen- dently, repeating projection and blending steps
Table 5: Ablation of Efficiency Optimizations.
Model
PSNR
FPS
Memory
w/o Single-Pass Pruning
32.54
36
747
w/o Single-Pass
33.23
40
270
w/o Pruning
32,54
60
747
MoE-GS (N=3)
33.23
270
and leading to significant FPS drops due to GPU overhead (Table 3) Without pruning, all Gaussians are retained-inchueee
those with negligible contributions which preserves reconstruction quality but reduces rendering and memory efficiency. When both techniques are removed, the baseline suffers from compounded overhead in rendering and memory usage. In contrast, our full model (MoE-GS, N=3) incorporates both strategies, eliminating redundant computation and discarding uninformative

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Published as a conference paper at ICLR 2026
Pixel Router
Per-Gaussian Router
Volume-aware Pixel Router
PSNR
PSNR
Figure 5: Qualitative comparison of MoE Router variants. Each router applies a different routing
strategy, resulting in varied structural consistency and detail across the outputs.
Gaussians, As result, it achieves comparable PSNR to the baseline while significantly improving FPS and reducing memory footprint (Table
Analysis of training Cost. Since MoE-GS employs multiple dynamic GS experts, we analyze how expert training budget influences overall performance. In principle, using N experts could increase training time by a factor of N, but we find that MoE-GS remains effective even when experts are trained with significantly reduced budgets.
1) Partial expert training. To quantify the practical training overhead of MoE-GS, we experiment with reducing the training bud- get of each expert. Here, budget of 100% corresponds to the expert's 's full training time (approximately 4.2 hours for E-D3DGS, 2.2 hours for STG, and 1.5 hours for 4DGaus- sians), while 50%, 20%, and 10% correspond to proportional reductions in wall-clock time. Thus, the budget axis directly reflects the actual training time per expert. Table that even when each expert is trained with only 20% of its usual budget (e.g., reducing 4.2-hour expert about 50 minutes, or a 1.5-hour expert under 20 minutes), MoE-GS still outperforms fully trained single-expert baselines. This suggests that MoE-GS does not rely on fully converged experts and that meaningful gains can be achieved with substantially reduced per-expert training time.
Table Effect expert training budget on MoB-GS (N=3).
Model
50%
E-D3DGS (Bac 2024
32.33
32.19
31.87
30.60
STG (Li 2024
31.92
31.76
31.41
31.19
4DGaussaans wu at al. 2024
31.43
31.02
30.90
30.64
MoE-GS (N=3)
33.23
32.71
32.60
32.14
2) Router overhead. The router itself is lightweight and adds less than 5% computation relative to expert training, making its additional cost negligible. These results show that MoE-GS does not require fully converged experts to achieve performance, and that its practical training overhead is far lower than linearly scaling with the number of experts.
4.3 DISTILLATION BASED EXPERT TRAINING
We compared expert models retrained from scratch with distilled versions trained under iden- tical settings, differing only in supervision via MoE-generated images and routing weights. Ta- 7 a consistent improvement in PSNR, SSIM, LPIPS across all expert types, indicating the effectiveness of MoE-guided supervision. This demonstrates that when the number of ex- perts grows large, applying distillation to each expert remains effective way to achieve sig- nificant performance gains. Qualitative results and weighting ablations are provided Appendix C.2 and
Table 7: Ablation Studies on MoE-GS Distillation Methods on Technicolor dataset
Model
PSNR
SSIM
LPIPS
E.DGGSS
Bae et
2024
32,88
0.902
0.111
E-D3DGS Bag ct
2024
(Dstilled)
33.67
0.915
0.091
STG
I et
al 2024
32,83
0.915
0.083
STG
1
ct
2174
(Distilled
33.10
0.917
0.082
ExIDGS
Lee
et
2024
33.57
0.918
0.086
Ex4DGS
Lee et al.
2024
(Distilled)
33.91
0.923
0.079
CONCLUSION
We propose MoE-GS, the first Mixture of Experts framework for dynamic Gaussian splatting, enabling adaptive and high-fidelity scene reconstruction. Our method introduces a Volume-awen aware Pixel Router that combines pixel-based and volumetric expert blending while reducing computational cost through knowledge distillation. The nar-Gausiian responsibilities exposed by MoE-GS also provide a useful signal for future canonical-space fusion or refinement methods seeking more explicit geometric unification. Overall, MoE-GS offers a scalable and effective framework for dynamic Gaussian Splatting, improving 4D consistency while remaining compatible with evolving GS representations.
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ACKNOWLEDGMENT
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-004142300 RS-2024-00456122 and the Ministry of Education (MOE) (RS-2025-25423221) This work was also supported by the Regional Innovation System & Education (RISE) program through the institute for Regional Innovation System & Education in Busan Metropolitan City, funded by the Ministry of Education (MOE) and the Busan Metropolitan City, Republic of Korea (2025-RÍSE-02-004- 1080000-880000005 The computational resources were provided by the Cluster Server for Computational Science at Pusan National University and the National ITT' Industry Promotion Agency (NIPA) (G2025-0403).
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APPENDIX
A OVERVIEW
This appendix includes detailed implementation information in Appendix additional quantitative and qualitative results in Appendix [ Gaussian-Level Interpretation and Geometry Evaluation Appendix D analysis of expert specialization in Appendix E Best-of-N Retraining Appendix F Ablation on Distillation Weighting Strategies Appendix G
A.1 REFERENCED IN THE MAIN PAPER
The sections listed below are directly referenced in the main paper for further details:
Optimization strategy of MoE-GS (Appendix B.1
Implementation details of Expert Training (Appendix B.2
Implementation details of Router Training (Appendix
B.3)
Implementation details of Single-Pass Multi-Expert Expert Rendering (Appendix [B.4)
Quantiative Results (Appendix
Qualitative Results (Appendix
Per-Gaussial Contributions and Responsibilities (Appendix D.1
Post-hoc Gaussian Fusion using Lifting Weights (Appendix D.2
Multi-view Depth Consistency Evaluation (Appendix D.3
Analysis Expert Specialization (Appendix
Best-of-N Retraining (Appendix
Ablation Distillation Weighting Strategies (Appendix G
B IMPLEMENTATION DETAILS
This section provides implementation and training details of the proposed MoE-GS framework. We first describe the overall two-stage training strategy, which separates expert model training from router optimization to ensure stable convergence and prevent dominance by faster-converging experts. Next, we present the training setup for individual expert models, including initialization and baseline- aligned hyperparameters. Finally, we outline architectural details of the proposed Volume-aware Pixel Router, which enables spatially and temporally coherent expert blending the MoE-GS pipeline.
B.1 TWO-STAGE TRAINING STRATEGY
To ensure stable convergence and balanced optimization, adopt two-stage training strategy that decouples expert training from router optimization. Jointly training both components can lead suboptimal convergence, faster-converging experts tend dominate the gating process early on, leaving others underutilized and under-optimized. To mitigate this, we first train each expert model independently ensure that it can reconstruct the entire scene without relying on other experts. Once trained, all expert parameters are frozen. In the second stage, we optimize the routing components specifically the per- Gau-Gansi parameters W;, wdir w unfime and the MLP h to learn an adaptive gating strategy that dynamically selects and blends experts based on spatial, temporal, and siew-dependent cues.
B.2 STAGE 1: EXPERT TRAINING
In the first step of MoE-GS training, each expert model is independently optimized before integration into the MoE framework. Since MoE-GS reconstructs dynamic scenes by blending the outputs of multiple experts, is critical that each expert achieves its best possible performance. To this end, retain the original training strategies proposed in their respective works without modification. All expert models initialized using point clouds generated by COLMAP.
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Ex4DGS (Lee et al. from Moom-Motion (SfM).
2024 is initialized with sparse point clouds obtained via Structure-
4DGaussians (Wu al. 2024) and E-D3DGS [aee et al. 2024) are initialized with down-
sampled versions of these dense point clouds.
For STG (Li et al. 2024), we follow its original strategy, merging point clouds from all frames obtain globally consistent initialization that serves as a strong prior for optimizing Gaussian attributes.
Each expert is trained using its original learning rate schedule, as the hyperparameters specifically tuned to each model's architecture and deformation representation.
B.3 STAGE 2: ROUTER TRAINING
In the second stage of training. we optimize the Volume-aware Pixel Router while keeping all expert models frozen. This allows the router to focus solely on learning effective expert blending strategies without being influenced by the convergence rate of individual experts. Specifically, we optimize per- Gaussian parameters W,, w weime and the MLP Φ which collectively determine the final expert weights for blending. All three parameters are fully learnable. To avoid introducing any handcrafted directional or temporal bias, initialize w and wiime using neutral near-zero constants, allowing the router to gradually learn meaningful directional and temporal sensitivities directly from data. Joint training of experts and the router often leads to suboptimal expert utilization, as rapidly converging experts can dominate the early stages of routing. Our two-stage approach avoids this issue by ensuring that all experts are first trained to full capacity, allowing the router to later learn how to combine their outputs most effectively.
Volume-aware Pixel Router. To generate routing weights, our router begins with the Ger-Gans weights nh de- fined 3D space. illustrated in Figure [ each ex- pert's Gaussian is duplicated, and its color attribute is replaced with learnable weights ui The remaining attributes- such as position, scale, and rotation-ant are di- rectly copied from the pre-trained expert Gaussians. This enables w/ make the router aware of the expert's ' vol- umetric structure, allowing the 2D splatted weights to reflect how each expert behaves under different viewing directions and time steps. To further model view- and dimendependent variation, we also splat auxiliary features Nime and the ray direction r, which are fed into a lightweight MLP. The output of this MLP is added to W2D in residual manner to produce the final routing weights.
Resierizer
Roster izer
Rasteriaer
Recker bor
22D 220
Softmax
To promote spatial coherence, we adopt convolutional MLP architecture that leverages local pixel context. For computational efficiency, this MLP shared across all experts. We empirically set the learning rate to 0.05 for the shared MLP and whime, and to 0.5 for wi and wihtr. This modular and coherent design enables the router to learn adaptive, per-pixel expert blending strategies that generalize effectively across diverse dynamic scenes.
Figure 6: Architectural details of the Volume-aware Pixel Router.
B.4 SINGLE-PAST MULTI-EXPERT RENDERING
To efficiently deploy MoE-GS, we complement our design with an optimized rendering pipeline. In particular, independently rasterizing each expert triggers repeated kernel launches and separate memory traversal over each expert's s Gaussian buffer, significantly increasing memory IO pressure. To address this bottleneck, we adopt Single-Pass Multi-Expert Rendering strategy, which processes all Gaussians in a single batched pass and eliminates these redundant kernel launches while still preserving expert-specific outputs. For completeness, also investigated two alternative implemen- tations for rendering multiple experts: (1) sequential expert execution with CPU offloading, and (2) distributing experts across multiple GPUs.
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Sequential execution becomes prohibitively slow due to repeated PCLe transfers between the host and device. Gaussian Splatting requires high-frrequncyy random access to Gaussian attributes (positions, scales, SH coefficients, opacities) during splatting. Moving these buffers back and forth over PCIe significantly increases latency and prevents any effective kernel fusion, leading to very large slowdowns in practice.
Multi-GPU distribution also provides limited benefit. Each expert resides in a heterogeneous and non-alignable 3D deformation space, distributing experts across GPUs requires duplicating each expert's Gaussian buffer as well as synchronizing per-pixel accumulations across devices. This synchronization step introduces substantial mnterGGU communication overhead, often outweighing the parallelism benefits and resulting in negligible wall-clock speedup.
These observations motivate our choice of the Single-Pas Multi-Exper Rendering strategy as the most stable and efficient trade-off for MoE-GS workloads.
C ADDITIONAL QUANTITATIVE AND QUALITATIVE RESULTS
C.1 QUANTITATIVE RESULTS
To comprehensively evaluate MoE-GS across perceptual, structural, and pixel-wise metrics, Tables and 9 present per-scene quantitative results the N3V (Li et al. 2022 and Technicolor (Sabater et al. 2017) datasets. Table reports N3V results using a 4-expert configuration, where MoE-GS consistently outperforms individual experts across all scenes and metrics, demonstrating strong generalization. Table Sable?/show shows results on Technicolor with a 3-expert setup using E-D3DGS (Bae et al., 2024), STG (Li al. 2024), and Ex4DGS (Lee et al. (024)). MoE-GS maintains strong performance even with fewer experts, achieving top or near-top performance across most scenes and the highest overall average across all evaluation metrics.
To assess its scalability to monocular settings, Table 10 evaluates MoE-GS on the HyperNeRI dataset (Park al. 2021). Despite the limited input views, MoE-GS transfers well this setting, maintaining high fidelity and demonstrating strong adaptability beyond the multi-view reconstruction scenario.
In addition, Table Il presents an ablation study of our distillation strategies on the Technicolor dataset, reporting per-scene results and highlighting the contribution of each component final performance.
Table 8: Additional Quantitative Results on Experts and MoE-GS for the N3V Dataset (Li al.
2022). t: Models were trained on dataset split into 150 frames. We highlight best and second-best values for each metric.
Coffee Martini
Cook Spinach
Cut Roast Beef
Model
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
4DGaussians
0.923
0.066
0.955
33.15
0.954
E-D3DGS Bue
0.930
0.961
0.041
STG [1
2024
0.927
0.061
0.961
0.964
Ex4DGS (Le al. 2174
28.72
0.918
0.070
33.24
0.956
0.042
33.73
0.958
0.040
MoE-GS (Nm4)
30,43
0.940
0.054
34.24
0.966
0.031
35.08
0.968
0.030
Flame Salmon
Flame Steak
Scar Steak
Model
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
4DGaussians Wa el
2024
29.76
0.928
0.062
31.81
0.962
0.032
32.01
0.964
0.032
E E-D3DGS Bae 2024
30.49
0.936
0.054
32.77
0.960
0.037
33.70
0.964
0.033
STG Li all 2024
29.09
0.928
0.057
33.25
0.968
0.026
33.77
0.969
0.026
ExADGS Lov ot 2024
29.33
0.925
0.066
33.91
0.963
0.034
33.69
0,960
0.035
Mol- (N=4)
30.92
0.942
0.049
34.38
0.972
0.026
34.42
0.972
0.026
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Table 9: Additional Quantitative Results on Experts and MoB-GS for the Technicolor Dataset Sabater al. 2017
Birthday
Fabien
Painter
Model
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
DyNeRF eL al.
2022
29.20
NSA
0.067
32.76
NJA
0.242
35.95
NJA
0.146
HyperReel Arra
20725
29,99
0.922
0.053
34.70
0.895
0.186
35.91
0.923
0.117
4DGaussians Wa al
2024
30.87
0.904
0.087
33,56
0.854
0.186
34.36
0.884
0.136
STG LL al. 2024
31,90
0.940
0.044
35,70
0.904
0.114
37.07
0.928
0.093
Ex4DGS (Lee al.
2024
32.36
0.941
0.045
35.19
0.896
0.124
36,66
0.932
0.091
Mo (N=3)
33.26
0.947
0.049
36.26
0.908
0.121
37.63
0.939
0.083
Theater
Train
Average
Model
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
DyNeRF et. 2022
29.53
N/A
0.188
31.58
NOA
0.067
31.80
NIA
0.142
HyperReel Attal et 2023
33.32
0.895
0.115
29.74
0.895
0.072
32.73
0.906
0.109
4DGaussians
29.81
0.841
0.155
25.35
0.730
0,166
30.79
0.843
0.146
STG L al. 2024
31,08
0.879
0,140
32.32
0.937
0.045
33.61
0.918
0.087
Ex4DGS (Lee 2024
31.79
0.882
0.130
31.39
0.928
0.055
33.48
0.916
0.089
MoE-GS (N33)
32.88
0.900
0.115
32.89
0.944
0.046
34.58
0.928
0.083
Table 10: Comparison results on the HyperNeRF dataset
Park et al.
2021
PSNR (dB)
Model
3dprinter
bananu
broom
chicken
Averuge
4DGaussians Wu al. 2024]
22.16
22.90
20.88
30.12
24.02
E-D3DGS (Bác et 2024
22.41
23.38
20.07
29.11
23.74
MoE-GS (N=2)
22.84
24.75
21.26
30.37
24.81
Table 11: Ablation study distillation strategies evaluating the effect of touting-weight-base. adaptive supervision on the Technicolor dataset (Sabater et al. 2017).
Birthday
Fabien
Painter
Model
Training
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
Retrained (GT Loss)
32.05
0.936
0.050
34.7
0.878
0.171
36.26
0.931
0.089
E.DDGGS Bac al. 2024
Distilled (wlo Weight)
32.17
0.942
0.048
34.8
0.883
0.164
36.77
0.934
0.089
Distilled (Ours)
32.24
0.946
0.038
35.68
0.902
0.120
37.20
0.939
0.078
Retrained (GT Loss)
33.15
0.947
0.038
34.87
0.901
0.117
33.61
0.907
0.098
STG Li
2024
Distilled (w/ Weight)
33.27
0.948
0.040
34.99
0.901
0.188
33.66
0.907
0.099
Distilled (Ours)
33.46
0.949
0.039
35.01
0.902
0.117
33.51
0.908
0.094
Retrained (GT Loss)
32.18
0.944
0,039
35.33
0.896
0.124
36.40
0.930
0.094
Ex-DGGS Lee al.
2024
Distilled (wio Weight
32.39
0.945
0.041
35.44
0.896
0.126
36.37
0.930
0.095
Distilled (Ours)
35.88
0.903
0.115
36.87
Theater
Train
Averuge
Model
Training
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
Retrained (GT Loss)
30.49
0.871
0.148
30.92
0.896
0.097
32.88
0.902
0.111
E-D3DGS Bac
2024
Distilled (w/o Weight)
31.73
0.878
0.150
30.85
0.898
0.099
33.26
0.907
0.110
Distilled (Ours)
31.79
0.887
0.124
31.46
0.900
0.095
33.67
0.915
0.091
Retriined (GT Loss)
30.28
0.876
0.126
32.26
0.942
0.036
32.83
0.915
0.083
Li
Distilled (wlo Weight
31.23
0.883
0.124
32,30
0.943
0.039
33.09
0.917
0.084
Distilled (Ours)
31.35
0.883
0.124
32.20
0.943
0.038
33.11
0.917
0.082
Retrained (GT Loss)
31.85
0.886
0.122
32.11
0.934
0.050
33.57
0.918
0.086
Ex4DGS Lee et al.
2024
Distilled (win Weight)
31.78
0.884
0.126
32. 14
0.935
0.053
33.62
0.918
0.088
Distilled (Qurs)
0.111
32.37
0.939
0.045
16

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Published as a conference paper at ICLR 2026
C.2 QUALITATIVE RESULTS
Figures| 8 present additional qualitative comparisons of MoE-GS across different datasets. MoE-GS effectively routes scene regions the most suitable experts, resulting in high-fidelity reconstructions that outperform individual models. These results further demonstrate the model's ability to adaptively combine specialized expert outputs for diverse dynamic scenes, addition, Figure shows distillation qualitative results on the Technicolor dataset.
MoE-GS
4DGaussians
E-D3DGS
Coffee Martini
Ground Truth
EX4DGS
STG
MoE-GS
4DGaussians
E-D3DGS
Flame Salmon
Ground Truth
EX4DGS
STG
Figure 7: Additional N3V Qualitative Results. Comparison of our MoE-GS with other dynamic Gaussian splatting methods on the Neural 3D Video dataset (Li et al. 2022).
17

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Published as a conference paper at ICLR 2026
MoE-GS
Ex4DGS
STG
Fabien
E-D3DGS
MoE-GS
Ex4DGS
STG
Painter
E-D3DGS
MoE-GS
EX4DGS
STG
Theater
GT
E-D3D0S
Figure 8: Additional Qualitative Results on the Technicolor Dataset (Sabater et al. 2017). Visual
comparison of our MoE-GS with other dynamic Gaussian splatting methods.
18

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Published as a conference paper at ICLR 2026
EX4DGS
EX4DGS Ex4DGs(Distilled
(Distilled)
Fabien
Painter
GT
E-D3DGS
E-D3DGS (Dittilee)
Birthday
Theater
GT
STG
STG (Distilled)
Birthday
Fabien
Figure 9: Qualitative Results of Distillation on the Technicolor Dataset (Saaterr al.
2017). Visual comparison between retrained and distilled expert models on the Technicolor
dataset (Sabater et 2017).
19

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Published as a conference paper at ICLR 2026
D GAUSSIAN-LEVEL INTERPRETATION AND GEOMETRY EVALUATION
D.1 PER-GAUSSIAN CONTRIBUTIONS AND RESPONSIBILITTES
As described in Section 3.2 the router outputs per- per-pixel expert weights G (u), while each Gaussian j of expert & contributes pixel through its volumetric transmittace T (u) and opacity (u.)
We first define the raw volumetric contribution of Gaussian Dkjj as
(k.(u) = [u) (w),
(12)
which measures how much Gaussian j influences the ray at pixel u. To lift the pixel-level routing back the Gaussian domain, we weight this contribution by the router's pixel gate:
(z)(2)
(u)
(13)
Because different Gaussians vary in visibility, opacity, and pixel coverage, their raw gated contribu- tions Ck.j reflect both geometric scale and routing strength. To isolate the routing effect, we normalize by each Gaussian's ' total volumetric contribution:
Eneu Oks(u) Eneu (k)(
RE
(14)
where U/ denotes the set of all pixel rays from all training views. The resulting Re measures how strongly Gaussian j is utilized within expert under the router's pixel-level gates, capturing its effective contribution in the image regions where it is visible. This interpretation arises naturally from our volume-aware router: because Gaussian contributions are computed in 3D before rasterization, pixel-level gating can be consistently traced back to individual Gaussians, enabling geometry- informed rather than RGB-level interpretation of MoE-GS.
Moreover, different experts operate in heterogeneous deformation spaces, their Gaussians do not form a one-to-one correspondence in 3D. Pixel space, however, is shared observation domain across all experts: each Gaussian contributes the same set of rays through (u) The router weights G (u) therefore gate geometry-cou -ondeiii-oodi volumetric contributions, making the lifting operation well-defined and ensuring that the resulting Gaussian responsibilities preserve consistent 3D semantics rather than reflecting any form of 2D blending.
D.2 POST-HOC GAUSSIAN FUSION USING LIFTING WEIGHTS
we construct post-hoc unified Gaussian model without any retraining. For each Gaussian j in expert k, we keep all geometry attributes position, scale, rotation, and SH coefficients wefffcient--unnhangged and adjust only the opacity based on the responsibility weight:
Using the normalized responsibilities Re, from Section D.I
fused kJ
RR.j Okk.j.
(15)
The final fused model is obtained by simply concatenating all Gaussians from all experts, using the fused opacities. This unified Gaussian set is rendered with the standard 3D Gaussian Splatting volume without any 2D compositing. The result preserves each expert's geometric structure while modulating its influence according to the router-driven responsibilities, yielding a coherent volumetric representation that reflects geometry-informe routing rather than image-space blending.
D.3 MULTI-VIEW DEPTH CONSISTENCY EVALUATION
To quantitatively evaluate the geometry of our post-hoc fused Gaussian model, we compute the Multi-view Depth Consistency (MDC), defined as the mean reprojection error between depth maps across all viewpoint pairs at the same timestamp. Given rendered depth maps (D:} from viewpoints {u,} at time t, measure consistency over all ordered pairs (i.j) by reprojecting the depth from view i into view j and comparing it against Dj:
MDC = P
Σ (U.))P
IT (D.) 1//1,
(16)
20

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Published as a conference paper at ICLR 2026
Cook
0.00
04
12
080
+24
330
1.0
230
Flame Salmon
Flamn Steak
Sear Steak
Mc
0.2
200 250
230
230
Figure 10: Multi-view Depth Consistency on the N3V dataset. Comparison of our MoE-GS with other dynamic Gaussian Splatting methods (lower is better).
where P denotes the set of all ordered view pairs, and mT (D() is the reprojection of DJ into view using the known camera poses timestamp
Fig. 10 reports MDC scores across the entire N3V dataset (Li et al. 2022). comparing our post-hoc fused Gaussian model against all dynamic Gaussian Splatting baselines. Across all scenes and times- tamps, MoE-GS consistently achieves lower reprojection error (lower is better), indicating improved multi-view geometric stability. This suggests that MoE-GS effectively combines complementary geo- metric priors from heterogeneous experts, producing unified Gaussian representation that remains consistent across the sequence rather than relying on appearance-level blending.
These results provide quantitative evidence that the proposed Gaussian-level weighting leads to more coherent underlying geometry. Even though MoE-GS relies on pixel-level gates during training, the fused Gaussian model exhibits globally improved 3D coherence when evaluated using purely volu- metric metric. Averaged over all scenes and timestamps, MoE-GS attains the best MDC performance, demonstrating that our mixture formulation enhances geometric reconstruction quality rather than only photometric fidelity.
E ANALYSIS OF EXPERT-SPECIFIS MOTION BEHAVIOR
To better understand the performance variations observed across dynamic Gaussian Splatting methods, we analyze the characteristic motion trajectories produced by each expert (Fig, I1 Although all methods operate on Gaussian primitives, their deformation priors impose fundamentally different inductive biases, leading to distinct behaviors across spatial regions and temporal motion regimes. Below, we summarize the key tendencies of each expert.
4DGaussians (Wu et al., 2024) (HexPlane canonical deformation). 4DGaussians produces short, smooth, and highly regular trajectories due to its shared HexPlane canonical representation. The deformation MLP conditions on features interpolated from a global position-time grid, causing spatially adjacent Gaussians to receive nearly identical deformation signals. This results in strong spatial regularization and minimal per-Gaussian variation. While this bias enables stable performance in static or low-motion regions, it hinders the representation of high-speed or rapidly changing motion, where the canonical features cannot vary sufficiently quickly to encode fine- ine-ggained displacement.
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Published as a conference paper at ICLR 2026
4DGausstans (Hex.)
E-D3DGS (Per)
(Poty-)
Figure 11: Motion Trajectory Comparison across dynamic Gaussian Splatting methods.
Consequently, 4DGaussians trajectories often become overly smooth or collapsed in fast-motion regions.
E-D3DGS (Bae et al. 2024) (per-Gaussian volumetric deformation). E-D3DGS exhibits direc- tionally consistent and comparatively higher-velocity trajectories. This behavior arises from two architectural elements: (i) a two-branch deformation network that separates coarse and fine motion, allowing the model to represent fast and detailed dynamics; and (ii) local embedding regularizer encouraging neighboring Gaussians to share similar deformation features, yielding spatially coherent and locally aligned trajectories. These effects are further reinforced by the learned per-Gaussian embeddings Eg. which act as local motion descriptors. Because the deformation MLP conditions on these embeddings and nearby Gaussians are regularized have similar embeddings- the model nat- urally forms coherent motion clusters with consistent velocity and direction. This embedding-deee driven structure explains the tightly aligned, high-velocity flows characteristic of E-D3DGS.
Ex4DGS (Lee et al. 2024) (keyframe interpolation). Ex4DGS produces motion trajectories that are highly diverse and often "free-form," even among Gaussians that are spatially adjacent. This stems from its interpolation-basee formulation: each Gaussian updates its position independently between keyframes, without a learned deformation field or mechanism enforcing spatial coherence. As result, nearby Gaussians may move with noticeably different magnitudes or directions. This independence is particularly useful in regions with irregular or rapidly changing motion. By allowing each Gaussian to move independently, Ex4DGS can capture abrupt, that more structured deformation models tend to oversmooth. However, it also leads to less stable or coordinated trajectories in areas where motion is expected remain coherent rigid.
displacements
STG (Li et al. 2024) (polynomial trajectory model). STG produces globally smooth, low-curvature trajectories due its use of low-order polynomial motion parameterization. Unlike Ex4DGS, which relies on independent interpolation, STG learns per-Gaussian polynomial coefficients, providing moderate flexibility while maintaining strong bias toward smoothly varying motion. Because all Gaussians share the same polynomial motion form while coefficients are learned individu- ally-Sti STG occupies middle ground between interpolation- based and deformation based models. It captures moderate local variation yet enforces spatial alignment across neighboring Gaussians. These inductive biases make STG well suited for rigid or near-rigid global motion and low-frequency temporal changes, producing trajectories that are directionally consistent with limited curvature.
We emphasize that these motion tendencies are representative rather than absolute, as real-world scenes often contain mixed ambiguous motion regimes that do not align perfectly with any single deformation prior. Nonetheless, the overall patterns observed across experts explain why no existing dynamic GS method consistently dominates across all spatial temporal regions. This variability naturally motivates the MoE-GS formulation, which adaptively selects the deformation prior most compatible with the local motion behavior.
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Published as a conference paper at ICLR 2026
Table 12: Stability across repeated trainings. Variability exists across runs, but MoE-GS consistently
outperforms all single-expert variants.
PSNR (dB)
Model
Min
Max
Average
E-D3DGS [Bac al. 2024
30.78
32.33
31.19
Ex4DGS (Lee et al 2024
31.43
32.10
31.78
4DGmussims
2024
30.18
31.43
30.70
MoE-GS (N=3)
32.72
33.23
33.01
F BEST-OF-N RETRAINING VS. MOE-GS: STRUCTURAL, NOT STATISTICAL, GAINS
To evaluate stability under repeated training, we retrain each individual expert model (E-D3DGS, Ex4DGS, 4DGaussians) five times using identical settings. For MoE-GS, we also train five separate MoE-GS models, each using the corresponding expert set from that run. This ensures that every MoE- GS run paired with its own independently trained experts, and that both baselines and MoE-GS are compared under identical stochastic conditions.
(1) Single-expert performance varies across repeated trainings. As shown in Table 12 dy GS methods exhibit noticeable run-to-run variation, a behavior commonly observed in optimization- based reconstruction pipelines. However, even the strongest individual run of any expert does not reach the performance achieved by MoE-GS.
(2) Expert-specific motion behavior remains qualitatively consistent. Although absolute scores vary across runs, each expert repeatedly displays the same characteristic motion tendencies described in Appendix for example, E-D3DGS tends to produce fast, coherent motion, whereas Ex4DGS more often captures irregular, multi-directional movement. These tendencies appear consistently across repeated trainings, reflecting the deformation priors of each method rather than run-specific randomness.
Taken together, these results indicate that MoE-GS provides stable improvement beyond what can be obtained by repeated training of any single expert, and that its gains stem from integrating complementary deformation priors rather than from statistical fluctuations between runs.
G ABLATION ON DISTILLATION WEIGHTING STRATEGIES
Distillation in our framework aims to transfer the complementary reconstruction strengths identified by MoE-GS back into single expert. We explore whether assigning per-pixel weights the MoE- guided loss improves this transfer, and compare two strategies: (i) w/o weighting, and (ii) gating-based weighting derived from MoE-GS. All experiments use E-D3DGS (Bae al. (024) on the Technicolor dataset.
Table 13: Effect of
distillation.
Method
PSNR (dB) 1
SSIM
LPIPS 1
E: D3DGS (Baseline)
32.88
0.902
0.111
w/o Weight
33.26
0.907
0.110
Routing Weight (Ours)
33.67
Using MoE-derived routing weights yields clear improvements unweighted distillation (Table 13) These routing weights capture how different experts contribute to each spatial-temporal region, providing supervision signals that encode complementary deformation priors rather than relying on pixel-level error statistics. As result, the distilled expert benefits from MoE-GS's 's region-specific strengths and achieves higher reconstruction quality.
23


Reviews:
[{"review_id": "LKjSMJnx5S", "reviewer": "Reviewer_7MH6", "summary": "MoE-GS identifies that different dynamic Gaussian Splatting methods have varying image quality performance across scenes, image patches, and time, and proposes a mixture-of-experts dynamic GS method to adaptively combine the outputs from a few experts, each using a different dynamic Gaussian Splatting method. To stabilize the MoE-GS optimization, the authors propose a novel Volume-aware Pixel Router design to combine expert outputs through differentiable weight splatting, where routing decisions at the Gaussian level are projected to the pixel level to facilitate optimization. To improve efficiency, the authors propose a single-pass multi-expert rendering to reduce GPU kernel launching and memory IO overhead and a gate-aware Gaussian pruning algorithm to remove less-impactful Gaussians in the MoE setting. To further improve efficiency, the authors propose distilling single experts with the help of the MoE model, which they show is superior to training an equivalent single expert from scratch, using only ground-truth images.", "questions": "How are parameters for w_i^{dir} and w_i^{time} initialized? Are they learnable? L281 suggests they're included in the optimization.\n\nI understand that Single-Pass Multi-Expert Rendering improves speed by reducing kernel launches and memory IO, but it also sounds like it will increase peak VRAM, since we could also run the experts sequentially with CPU offloading (probably much slower), or distribute the experts across GPUs (probably almost just as fast)? Have you considered or tested these two alternative strategies?\n\nFigure 1 shows that different dynamic GS methods have differing performance across scenes, image patches, and time. I wonder if MoE-GS can be generalized to have experts that each represent a different dynamic GS method? E.g. having 2 experts, one using 4D GS and one using Ex4DGS?\n\nMinor points:\nI couldn't easily find what the abbreviations (Hex., Poly., Per., inter.) stand for in Figure 1. I know that this is explained in the main text, but it'd be nice to include what these are in the captions. Also, why is \"inter\" not capitalized while the other abbreviations are?\n\nVariables in Section 3.2 are defined and used confusingly. How do the \"2D\"-subscripted variables in eq. (7) relate to eq. (6)? w_i is defined in eq. (6) but never used or explained. I'm assuming w_{2D} is a collection of w_i's, but this is not explained.", "limitations": "", "rating": 10, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "Very good results supported by comprehensive quantitative experiments (more in the appendix!), images, and an easy-to-read demo webpage.\n\nMoE-GS runs with higher speeds and lower memory requirements (with pruning enabled) compared to only using one of the methods the experts are based on [Table 3], while offering state-of-the-art image quality.\n\nGood applicability/adaptability. Theoretically one can use any dynamic GS method as an \"expert\" so long as it returns a set of 3D Gaussians at each time step. \n\nThe paper supports the need for using different \"experts\" well, with Figure 1 showing different dynamic GS methods have varying image quality under different situations, thus it makes intuitive sense to combine different methods together.", "weaknesses": "Minor weakness: unlike in MoE LLMs where most experts are not executed, the MoE mode here (i.e. without distillation) still runs all experts at all times. The inference speed has potential to be improved further if, we can combine experts with sparsity. Is there a reason this is not done in this paper?", "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": "aofd63cUQg", "reviewer": "Reviewer_bdci", "summary": "This paper introduces MoE-GS (Mixture of Experts for Dynamic Gaussian Splatting), the first framework to apply mixture-of-experts techniques to dynamic 3D scene reconstruction using Gaussian splatting. The authors observe that existing dynamic Gaussian splatting methods show inconsistent performance across different scenes, spatial regions, and temporal frames, indicating no single approach handles all dynamic challenges effectively. To address this, MoE-GS integrates multiple specialized expert models through a novel Volume-aware Pixel Router that adaptively blends expert outputs by projecting Gaussian-level weights into pixel space via differentiable splatting. The framework also includes efficiency optimizations like single-pass multi-expert rendering and gate-aware Gaussian pruning, plus a knowledge distillation strategy to transfer MoE performance to individual experts for lightweight deployment. Experiments on the N3V and Technicolor datasets demonstrate that MoE-GS consistently outperforms state-of-the-art methods in rendering quality while maintaining practical efficiency.", "questions": "Please refer to the Weaknesses above.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper provides a solid diagnostic study (Fig. 1) showing how different dynamic Gaussian Splatting models reach performance peaks at different scenes, spatial regions, and time steps. This empirical observation is valuable to the community and highlights an important open problem of model generalization in dynamic reconstruction.\n\n2. Even if the current MoE-GS realization focuses on image-level blending, the paper raises the broader idea of combining specialized dynamic reconstruction models adaptively. This perspective could motivate subsequent studies toward true 3D-level mixtures or unified dynamic representations.", "weaknesses": "1. Has conceptual mismatch between its stated motivation and its technical realization. In 3D reconstruction, **novel view synthesis is merely a means to validate that the reconstructed geometry is correct, not the ultimate goal itself**. However, MoE-GS operates entirely at the image level, where the proposed Volume-aware Pixel Router blends rendered 2D outputs from multiple experts using per-pixel softmax weighting. Each expert maintains an independent 3D Gaussian representation, and **the router does not modify or unify these geometries**. Consequently, the method **only improves rendering appearance (e.g., PSNR, SSIM)** through image-space ensembling but does not enhance or reconcile the underlying 3D geometry. The core mechanism of MoE-GS is effectively **no different from mixing several rendered images with learned blending weights**, since the entire representation being optimized is the final rendered image rather than the underlying 3D structure, with no merged model output. Consequently, the claimed benefit for “dynamic reconstruction” is not real. \n\nWhile I understand that per-Gaussian routing may be technically challenging, I would appreciate clarification on whether the authors believe their Mixture-of-Experts design can genuinely improve geometric reconstruction quality rather than primarily enhancing rendering appearance. \n\n2. In figure 1, the paper convincingly demonstrates empirically that different dynamic Gaussian Splatting (GS) methods achieve different performance peaks across scenes, spatial regions, and time steps, but it does not provide a clear causal explanation for why these variations occur. Adding deeper theoretical or empirical insight into why existing GS methods differ in scene-, region-, and time-dependent performance will increase interpretability and scientific understanding of the proposed approach’s foundations.", "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": "Qn99JCdq6i", "reviewer": "Reviewer_AHQB", "summary": "This paper presents MoE-GS, the first approach to apply a Mixture-of-Experts (MoE) architecture to Dynamic Gaussian Representation. Using a Volume-aware Pixel Router, MoE-GS mixes the Gaussians produced by experts specialized in representing specific Gaussians within the same scene. Although this introduces runtime overhead, the authors mitigate it with single-pass multi-expert rendering and gate-aware Gaussian pruning. As a result, experiments on the N3V and Technicolor datasets show that MoE-GS achieves better view-synthesis quality and efficiency than prior state-of-the-art methods.", "questions": "\tIn Equation (6), what is the rationale for multiplying the time variable by the time-dependent per-Gaussian weights?\n\n\tIn the Theater results of Table 2, MoE-GS underperforms relative to HyperReel. I hypothesize that this is because HyperReel is not employed as an expert; would incorporating HyperReel as an expert improve performance?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 2, "contribution": 3, "strengths": "\tMoE-GS is the first work to introduce a Mixture-of-Experts architecture to dynamic Gaussian splatting, offering a new solution to the problem that a single model cannot handle scene-specific diversity.\n\n\tThe proposed MoE-GS model consistently achieves higher rendering quality than existing SOTA methods on complex dynamic scenes and demonstrated its superiority across diverse datasets (N3V, Technicolor).\n\n\tThe authors acknowledge the drawbacks of the MoE architecture, namely increased computational load and reduced speed and jointly propose techniques to mitigate these issues.", "weaknesses": "Major weaknesses are as below:\n\n\tIn LLMs, MoE is typically used at inference time to reduce the number of activated parameters [1]. By contrast, this paper uses MoE as a mechanism for mixing multiple models. Nevertheless, it suggests that as the number of models increases, computational resource demands grow, while the performance gains are unlikely (at a fine-grained level, e.g. a patch of novel view image) to surpass the best performance of any single model.\n\n[1] Dai et al. “DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.” CoRR. 2024.\n\n\tThe rationale for using the variational models (4DGaussians (Wu et al., 2024), E-D3DGS (Bae et al., 2024), Ex4DGS (Lee et al., 2024), and STG (Li et al., 2024)) is sufficient. However, the paper lacks analysis of which aspects each model is strong in and why. It also lacks an examination of whether there are synergies among the models and what performance gains such synergies bring.\n\nMinor comments or improvements are as below:\n\n\tTo emphasize that MoE-GS performs well across all aspects, including MoE-GS’s results in Figure 1 would help clarify that it achieves higher performance than all other methods.\n\n\tDo all models require training? If so, reporting the training time for each model and providing an analysis of the increased training time would be helpful.\n\nI will reconsider the score when all the concerns are handled well.", "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": "y8dwQqPjCB", "reviewer": "Reviewer_eMnv", "summary": "This paper MoE-GS employs the concept of MoE in 4D spatial reconstruction, based on the observation that multiple methods hold different individual optimal performances in multiple cases. This pipeline builds upon multiple baseline 4DGS methods and assigns volume-based per-Gaussian router score for each expert. The resulting system achieves SOTA reconstruction quality among baselines. This work also introduces acceleration and distillation techniques to improve the inference efficiency.", "questions": "1. The time cost should be justified, as the method requires significantly higher resources to maintain the SOTA visual performance.\n2. The authors should clarify on their motivations and design choices regarding the MoE structure, as listed in the weakness part. The necessity of such structure should be illustrated. A detailed specialty comparison between the expert models, and why these expert models have these properties  should be provided, which are beneficial to the overall novelty.\n3. More ablations are recommended to make the experiments solid.\n\nBased on above considerations, I would like to give a positive recommendation regarding the paper's novelty, and I will adjust my score based on the authors' rebuttal on my concerns.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. This work introduces MoE into 4D reconstruction, which is a novel solution to enhance the 4D representation capability.\n2. The Volume aware Pixel Router serves a core innovative design, providing accurate and effective router score based on the rendering process. This design tackles the difficulty in obtaining a suitable score for GS based MoE, which distinguishes this work from previous MoE structures with direct evaluation score.\n3. The visual performances are impressive. Based on the MoE structure which aggregates the strength from multiple baseline methods, the proposed method meticulously achieves significantly high visual quality.\n4. The authors cater for the inference efficiency of the MoE model. Although the rendering cost is higher than baselines, it still maintains a real-time rendering.", "weaknesses": "1. The primary concern is on the method efficiency. This method requires training multiple baseline 4DGS models along side the MoE router. This design costs significantly high time and GPU resources compared to baseline methods. Besides, the time cost used in deciding the router network is also a concern, i.e., the time spent on training the per-Gaussian dynamics property and router network.\n2. The significance of a MoE model in this 4D reconstruction scope. Although the MoE structure is applicable to 4D reconstruction problem, it is not an intuitive solution. The 4D reconstruction requires fitting to each individual scene, while the general LLM MoE requires only one training while reused for any inference. In this case, the cost of training a MoE model is efficient compared to training multiple full LLMs. However, this concept is not versatile in the proposed framework. The method requires full training of the individual method while the MoE-GS aggregates and overfits to each model's specialty. This leads to a conflict that, previous MoE for large models is used to improve the efficiency while maintaining high performance, while this framework takes costs to train MoE and improve the quality. These considerations weaken the necessity of such MoE-GS design. Besides, the 3D/4D reconstruction is random, i.e., multiple runs can yield different results. Does an MoE over multiple runs also improve the overall performance? Are the differences between different baselines large enough for a MoE structure?\n3. The hyperparameter in Eq. (11) seems an important hyperparameter for distillation. Its impact on the overall distillation performance is not clearly illustrated.", "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)