Mango-GS: Enhancing Spatio-Temporal Consistency in Dynamic Scenes Reconstruction using Multi-Frame Node-Guided 4D Gaussian Splatting
Authors: Tingxuan Huang
Haowei Zhu
Jun-Hai Yong
Hao Pan
Bin Wang
Venue: ICLR 2026 Poster (2026)
Keywords: 3DGS
Dynamic Reconstruction
Multi-frame
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Mango-GS improves spatio-temporal consistency in dynamic scene reconstruction by modeling multi-frame control-node dynamics within a 4D Gaussian splatting framework.

Abstract: Reconstructing dynamic 3D scenes with photorealistic detail and temporal coherence remains a significant challenge. Existing Gaussian splatting approaches modeling scenes rely on per-frame optimization, causing them to overfit to instantaneous states rather than learning true motion dynamics. To address this, we present Mango-GS, a multi-frame, node-guided framework for high-fidelity 4D reconstruction. Our approach leverages a temporal Transformer to learn complex motion dependencies across a window of frames, ensuring the generation of plausible trajectories. For efficiency, this temporal modeling is confined to a sparse set of control nodes. These nodes are uniquely designed with  decoupled position and latent codes, which provide a stable semantic anchor for motion influence and prevents correspondence errors for large movements. Our framework is trained end-to-end, enhanced by a input masking strategy and two multi-frame loss to ensure robustness. Extensive experiments demonstrate that Mango-GS achieves state-of-the-art quality and fast rendering speed, enabling high-fidelity reconstruction and real-time rendering of dynamic scenes.

Published as a conference paper at ICLR 2026
MANGO-GS:
ENHANCING SPATIO-TEMPORAL CON- SISTENCY IN DYNAMIC SCENES RECONSTRUCTION USING MULTI-FRAME NODE-GUIDED 4D GAUSSIAN SPLATTING
Tingxuan Huang Haowei Zhu 1 Jun-hai Yong', ' Hao Pan Bin Wang 1.2+
School of Software, Tsinghua University 2 BNRist (htx25, zhuhw23) weaigssttiinguaa.... edu. en, [yongih, haopan, wangbins itw.biinnnstsinnnnuu..ee...
ABSTRACT
Reconstructing dynamic 3D scenes with photorealistic detail and strong temporal coherence remains significant challenge. Existing Gaussian splatting approaches for dynamic scene modeling often rely on per-frame optimization, which can over- fit to instantaneous states instead of capturing underlying motion dynamics. To address this, we present Mango-GS, multi-frame, node-guided framework for high-fidelity 4D reconstruction. Mango-GS leverages temporal Transformer model motion dependencies within short window of frames, producing tempo- rally consistent deformations. For efficiency, temporal modeling is confined to a sparse set of control nodes. Each node is represented by decoupled canonical position and a latent code, providing stable semantic anchor for motion propa- gation and preventing correspondence drift under large motion. Our framework is trained end-to-end, enhanced by an input masking strategy and two multi-frame losses improve robustness. Extensive experiments demonstrate that Mango- GS achieves ofte--of-the-ar reconstruction quality and real- fil-timme rendering speed, enabling high-fidelity reconstruction and interactive rendering of dynamic scenes.
INTRODUCTION
Reconstructing photorcalistic and dynamic 3D scenes from casually captured videos is a signifi- cant goal in computer vision and graphics. Success in this area enables a wide range of applica- tions, including virtual reality, digital twins, and advanced visual effects Tang et al. (2023); Wu
et al. (2025b). Early methods for this task, particularly those based Neural Radiance Fields
(NeRF), have demonstrated compelling results in capturing scene appearance and geometry Attal al. (2023); Li al. (2022a): Park al. (2021b): Pumarola et al. (2021): 1); Wang al. (2023).
However, -eRe- approaches such as D-NeRF, Nerfies, and HyperNeRI, often require exten- sive computational resources and suffer from slow rendering speeds. A more recent method. 3D Gaussian Splatting (3DGS), brought major breakthrough for static scenes Kerbl et al. (2023). It achieves high quality rendering in real time by the direct rasterization of 3D Gaussians.
Inspired by its success, researchers have begun extending 3DGS to model dynamic scenes. Many current approaches learn the motion of Gaussians by introducing time-dependent parameters or us- ing deformation network, typically a Multi-Layer Perceptron (MLP), to predict changes for each frame Wu et al. (2024); Park et al. (2025). While effective for simple movements, we observe fun- damental limitation in this fer-frame optimization strategy. By processing each frame in isolation, these models tend to memorize the specific state of the scene at each moment rather than learning the underlying principles of motion. This makes it difficult to maintain temporal consistency, often leading to visual artifacts or blur, especially when dealing with fast or complex movements.
A natural way to improve temporal consistency is consider multiple frames once, allowing the model to understand motion trends Blattmann et al. (2023). Sequence models like Transformers
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Vaswani et al. (0117) are exceptionally well suited for this, as they can capture complex relationships across a temporal window. However, significant obstacle arises when applying this idea to 3DGS. A typical scene contains often millions of individual Gaussians. A straightforward application of temporal Transformer to all Gaussians would lead to prohibitive computational and memory costs, directly contradicting the core advantage of 3DGS, which is its efficiency and speed.
To overcome this challenge, we can simplify the complex motion of the entire scene by modeling the movement of sparse set of control points. This principle has been explored by methods like SC- GS Huang et al. (2024). which uses a k-Nearest Neighbors (k-NN) approach to propagate motion from control nodes the full set of Gaussians. This is promising direction, yet faces its own difficulties. When motion becomes rapid, the spatial neighborhood defined by k-NN in the initial frame may no longer be meaningful. For example, points that start close together can end up far apart, causing unrelated parts of the scene to be incorrectly influenced by the same control points. This weakens the model's 's ability to reconstruct challenging regions of large movements.
Our work addresses these limitations through two core insights. First, to prevent neighborhood drift, we propose a decoupled control node representation. Instead of treating a node as just a 3D position, we separate its spatial location from persistent latent feature code. This decoupling allows the model to establish semantic neighborhoods. Second, on top of this representation, we introduce multi-frame frame temporal attention mechanism. We use temporal Transformer learn coherent, physically plausible motion patterns, rather than memorizing individual frames.
To sum up, we introduce Mango-GS: Multi-frame Node-Guided Optimization framework for Gaussian Splatting. Our main contributions are:
1. We introduce decoupled representation for control nodes, which separates their spatial position from a latent feature code. This design effectively stabilizes the motion influence throughout the sequence and prevents neighborhood drift in scenes with large movements.
2. We apply a multi-frame temporal Transformer to model the dynamics of control nodes for Gaussian Splatting. This allows our model to capture complex, long-range temporal dependencies and generate highly coherent motion.
3. The resulting framework, Mango-GS, sets new state-of-the-art in dynamic scene recon- struction, demonstrating superior performance in rendering quality and speed, particularly in challenging scenes with fast and intricate motion.
2 RELATED WORKS
Dynamic Neural Rendering, Reconstructing dynamic 3D scenes is fundamental problem in computer vision. Following the breakthrough of NeRF Mildenhall et al.] (202T) for high-quality neural rendering, substantial effort has gone into adapting NeRF to dynamics. Some approaches Li et al. 20226 202T) attach time-conditioned latent codes to represent scene evolution, while another line of work Park et al. (2021a) b): |Pumarola et al. (202T) introduces explicit deformation fields that warp rays into a canonical space where a static NeRF is optimized. Building on efficient NeRF variants Song et al. (003); y Müller al. (2022), the other methods CCo & Johnson (2023); Fridovich-Keil et al. 2023); ; Shao et al. (2023) further factorize the 4D space-time domain into sets of 2D feature planes reduce model size and speed up training. Despite these advances, their inference speed still falls short of practical, real-time rendering requirements.
Dynamic 3D Gaussian Splatting. 3D Gaussian Splatting (3DGS) enables real-time, high-fidelity rendering for static scenes and has motivated deformable extensions that animate a canonical Gaus- sian set over time. Representative methods include D-3DGS Luiten et al. (2024) and Deformable 3DGS Yang et al. (2024), which regress per- Gar-Gausiinn caanslation/roaaioonscaall with lightweight MLPs. This paradigm has been strengthened by richer inputs, such as the learnable embeddings in E-D3DGS Bae et al. (2024) and DN-4DGS Lu et (2024a). Eyent-boosted Deformable 3DGS fur- ther combines event streams with deformable Gaussians to recover high-speed motion that is hard to capture with RGB-only inputs Xu et al. 2025)). To improve temporal modeling, 4DGaussians encodings, while GaGS Lu et al. (2024b)
Wu al. (2024) augments Gaussians with explicitly extracts 3D geometry features and injects them into the deformation learner to enforce geometric coherence. In parallel, MotionGS Zhu et al. (2024b) introduces flow-based motion guid- ance to decouple camera motion from object motion, and TimeFormer Jiang al. (2025) applies
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Decoupled Control Nodes
Time n
MLP
Tempural attention
Ourput heads
Code
Poxition
Nodes
Nodes
positions .)
deformp.
ANV
Mango-Ga GS
KNN
Temporat input masking Top-K hard-frame
Gaussians deformo m
Ground truth framesy
Motion-avare loss
3D Gaussians
An overview of the Mango-GS framework. Our method is driven by set of decoupled control nodes, each comprising a canonical position and a feature code. A dense 3D Gaussian cloud is associated with these nodes via learned k-NN relationship based on both position and features. A temporal attention network takes the canonical node positions and a time window [0. T] as input, processing them through MLP layers and temporal attention blocks to predict the nodes' deformations over the entire time window. This learned motion is then propagated back to the Gaussian cloud to produce final dynamic scene representation for each frame. The entire model is optimized end-to-end with temporal input masking scheme and composite loss, which includes a top-k hard-frame photometric loss, and a motion-awan aewrre temporal loss.
Figure 1:
cross-temporal Transformers to deformable Gaussians, explicitly modeling temporal relationships between frames. For long sequences and efficiency, SWinGS Shaw al. (2024) trains sliding poral windows guided by 2D optical flow, and Swift4D Wu et al. (2025a) leverages a 4D hash grid focus computation on dynamic regions.
Beyond per-Gaussian prediction, structure priors and explicit parameterizations have been explored. SC-GS Huang et al. (2024) uses sparse set of control nodes to drive dense Gaussians via spatial k-NN. Our method follows this node-guided paradigm, but differs by (i) decoupling each node into canonical position and a latent code, and (ii) performing multi-frame temporal modeling directly in the sparse node space rather than dense Gaussians. Another family directly parameterizes time. Duan et al. (2024) Li et al. (2024); Yang et al. (2023) represent dynamics with true 4D Gaussians, increasing expressiveness the expense of higher storage and training overhead. Fully explicit 4D Gaussian variants Lee et al. (2024) and decomposed 4D hash encodings such as Grid4D Xu et al. (2224) further improve fidelity and compactness, while MEGA Zhang al. (2025) study memory- efficient color parameterizations and adaptive allocation of 3D versus 4D Gaussians. Beyond view synthesis, Splatter Video (Snn et al. (2024) represents generic videos with 3D Gaussians and associ- ated motions to support downstream video tasks such as tracking and editing. Our work, Mango-GS adopts sparse gode-guided formulation with multi-frame modeling to enhance temporal coherence while retaining the efficiency benefits of Gaussian rasterization.
3 PRELIMINARY
3D Gaussian Splatting. 3DGS is an explicit point based representation in which each scene element a 3D Gaussian parameterized by its mean E R², positive definite covariance E R3x3 per- point opacity (0, 1], and view dependent color via spherical harmonics (SH). The Gaussian density is defined as:
G(x) = e - -1 x-1 (x -4)
To ensure Σ > 0, 3DGS factorizes it into rotation R E SO(3) and a diagonal scaling S diag(s) with s E R Σ = R RT. For novel view synthesis, each 3D Gaussian splatted onto the image plane. Let W be the world to camera transform and J the local affine or Jacobian at the W W JT. which defines a 2DD Gaussian footprint used for rasterization. After projecting all Gaussians and sorting them along
projected mean. The covariance in image coordinates is E'
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the viewing ray from back front, the per pixel color C' is computed by alpha compositing with effective opacities af and SH shaded colors C;:
C
Σ G II1-a)).
where q is evaluated from the Gaussian's ' SH coefficients under the current view direction, and af is the splatted opacity derived from and the projected footprint. The full pipeline is differentiable and supports end to end optimization of the Gaussian parameters for high-fidelity real time rendering.
METHOD
This section presents a high-level overview the pipeline, followed by the two core components and the training strategy. We first summarize how sparse control signals are mapped to dense, time- varying representation; we then describe the decoupled control node representation that stabilizes motion propagation; and we introduce the multi-frame temporal attention network that learns node dynamics; finally, we outline the overall optimization procedure. In addition, the structure of the training data provided in Appendix A.
Our goal is to reconstruct dynamic 3D scenes from monocular or multi-view videos with both high- fidelity appearance and temporally coherent motion. To this end, we introduce Mango-GS (Fig. 33. which animates dense 3D Gaussian representation using a sparse set of learnable control nodes. This sparse-to-denss design enables expressive temporal modeling with a compact attention network while avoiding the cost of operating directly millions of Gaussians.
4.1 OVERALL PIPELINE
Our method represents a dynamic scene as a deformation canonical 3D Gaussian point cloud. The entire process, as depieted in Fig. [ can be understood unified pipeline that maps sparse set of control instructions to a dense, dynamic scene representation.
The foundation of our scene set 3D Gaussians, control nodes, N = J where M < N. Each control node n, = (p,, f() is decoupled entity composed of canonical position E I and learnable feature code fi E D.
{g and corresponding sparse
The influence of these control nodes on the dense Gaussian cloud is established through a learned k-Nearest Neighbor (k-NN) relationship. For each Gaussian Jj. we identify its & most influential control nodes. The influence weight, Way. of node n; on Gaussian gj is computed based on their proximity in a joint position-feature space, ensuring motion-aware neighborhood:
exp(-D(g), n2)) Wiy Σve K(() exp( e ny))
Vi K()).
where IC(j) is the set of nearest nodes to Gaussian gj. and measures distance in a joint space of Gaussian canonical parameters and node features.
To animate the scene, we model the trajectory of all control nodes over a time window of T frames. As shown in the central part of Fig. [ our temporal attention network & takes the canonical node positions p: (tiled across the T timestamps) and timestamps to ....... tr- as input. The network then predicts sequence of deformations for each node all at once:
(t): (t,,),, As,())== = @1pr (t)T=) θ),
(4)
where Ap, Aq. and As represent the predicted translation, rotation, and scaling for each node, respectively, and θ are the learnalle parameters of the network.
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Finally, the node deformations propagated to all Gaussians using the pre-computed k-NN weights. The deformation of each Gaussian 93 at time t is a weighted blend of its semantic neigh- boring control nodes. For instance, its positional deformation (t) is calculated as:
(A())))))=
Σ (C()
wi (Api(t))
(5)
Similar blending is applied compute the final rotation and scaling for each Gaussian. The result- ing deformed Gaussian cloud (t(t) can then be rendered from any viewpoint. The entire framework trained end-to-end with a temporal input masking scheme and a composite loss function that eval- uates the quality of rendered frame groups against ground truth data, ensuring photometric accuracy, robustness to challenging frames, and high temporal fidelity.
4.2 DECOUPLED CONTROL NODE REPRESENTATION
simiaaty
w/ semantic similarity
(a) k-NN pairs with position
(b) k-NN pairs with code
Figure 2: Position-only nodes versus decoupled nodes with position and code. We visualize 24 Gaussians (red) in high motion region and its three corresponding nodes (white). With the decou- pled design, Gaussians attach to semantically consistent nodes rather than merely following spatial neighbors, which struggle under large motion.
central challenge in modeling dynamic scenes lies in establishing a robust and meaningful cor- respondence between the sparse nodes and the dense Gaussian cloud. Purely using spatial k-NN in canonical space, as in SC-GS Huang et al. (2024), fragile. Specifically, under large non-rigid motion, points that start nearby can end up on different, independently moving parts of the same or different objects. Relying on a static spatial neighborhood can therefore lead to erroncous motion propagation, causing blurring deformations and visual artifacts.
We address this by decoupling each control node into a position and a code, and by learning affinity rather than relying on Euclidean distance alone. The relationship is inferred from the full canonical parameters of each Gaussian, yielding neighborhoods that reflect shape, orientation, and location rather than proximity only.
Formally, let a Gaussian 9j have canonical parameters Φ, = (Iy Qj, 8j), where xj, Gj. and repre- sent its canonical position, rotation, and scaling factors, respectively. We compute learned affinity score between 9j and node n, using a lightweight MLP, and convert the top-k scores into weights via softmax. The MLP maps Gaussian canonical parameters to an embedding that is compared with the node code to produce the affinity score. The final influence weights Wij for the & most influential nodes are then derived by applying a softmax function these scores.
This design brings two benefits. First, the learned affinity produces semantics semantics-aware aware and robust neighborhoods, as evidenced in Fig. (b), the longer KNN links indicate non-local yet consistent matches that remain valid under large displacement, unlike the short, purely local links in Fig. 2 (a). Second, it is parameter efficient by reusing existing Gaussian attributes, avoiding per-Gaussian latent features and the associated memory overhead.
4.3 TEMPORAL ATTENTION FOR NODE DYNAMICS
To generate realistic and temporally coherent motion, the model has to reason across time rather than memorize frame-wise states. Per-frame deformation, which treats frames in isolation, often fails to
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MLP
Resize
Temporal Tramsformer
Node Positions
[N, T, Cirl
N,
Resize
Timep-
Cd
1N, T, W/
Hidden
Output heads
The Architecture of the Temporal Deformation Network. For each of the N control nodes, features over a window of I' frames are processed by an MLP backbone interleaved with temporal self-attention blocks. Attention operates along the time axis and is fused by a lightweight gate, then decoded to per-node translation, rotation, and scale for all frames.
Figure 3:
capture complex trajectories. We therefore design multi-frame deformation network & that models the dynamics of the control nodes across time window of T frames. As illustrated in Fig. deep MLP architecture is interleaved with temporal self-attention blocks that operate along the time axis, enabling the network to capture ranger non-linear dependencies and produce coherent motion for all nodes within the window.
The network processes all N control nodes simultaneously. For each node, its canonical position P; is first encoded into feature Temb- Similarly, timestamps to: ...,, -r- are embedded to temb E R.'xC. These are concatenated and shaped into an initial feature tensor (0) E x2xxXxx X which summarizes all nodes over the window. Oin the input feature dimension. (0) is then processed through series of L layers with the hidden size of W. Most layers are standard MLP blocks with ReLU activations. However, at specific layers within the network, we introduce the temporal attention block.
Given the input features (!)) E R NxTxW the block first projects them into a temporal attention
space (() E NxWXT. We then apply multi-head self-attention (MHA) along the temporal axis. For each node, this operation computes with all time steps within the window [0, 1]:
Hattn = .HA(H
H in H
(6)
We then lightweight gating module Chen al. (2023); Huang al. (2025) to generate (Waate Whans) ) from Haun and fuse temporal information back into the main stream:
H(1+1) H() (W(gate)) + Whias-
This allows the network to adaptively control how much of the temporal information is incorporated. Here, is the sigmoid function, Wgate E R. N xT and Whins E N W xT are output attention signals, and & denotes element-wise multiplication. This gated update rule is more expressive than simple residual connection. After the final layer, the output tensor (4) is passed through separate linear heads to decode the final deformations for each node at each the time steps.
4.4 TRAINING AND OPTIMIZATION STRATEGY
The Mango-GS framework trained end-to-end with two key strategies: temporal input mask- ing scheme to strengthen motion reasoning, and composite loss function that focuses on difficult frames while enforcing cross-frame consistency.
Temporal input masking. To prevent the temporal attention network from trivially memorizing timestamps and frame-specific appearance, we employ an input masking strategy. This can be viewed as lightweight form of training-time augmentation that improves robustness Zhu et al. (2024a). During training, we randomly select and mask out a subset of the input time embeddings. The network is then tasked with predicting the deformations for the entire window using only the visible temporal context.
The overall loss function is:
0.8 x Ltaae + 0.2 X Lmotiou:
(8)
where Lirame is top-k hard-frame photometric loss, and Lmotion is motion-aware loss.

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Top-k hard-frame loss. It is the primary supervision signal, which is based photometric loss, calculated on per-frame basis as a weighted combination of the L1 loss and DSSIM. Instead of averaging this loss actoss all frames, reconstruction loss, Ltrame, is the mean the individual loss from only the k frames with the highest error in the batch. We set K fixed ratio of 0.6 batch size and recompute the top-k frames at every iteration, so gradients are reweighted toward the currently hard frames. This focuses optimization on the most challenging moments in the sequence.
apply top-k furd-frame mining strategy. The final image
Motion-aware loss. To explicitly supervise temporal coherence, the model uses a motion-aware loss. Lmotion. that operates on the temporal differences. Let 81, = I, 411 and 81, 4 4-1 denote the temporal differences between consecutive rendered and ground truth frames, respectively. We treat these differences as a compact description of how the scene evolves between neighboring timestamps. The motion-aware loss is defined as a weighted sum of three terms:
Cmotion
Aaitt Ldift + Xamp Camp Adir Edir-
(9)
The three components are given by
Lai
Σ [18T((z) - 51((3)11
(10)
1,a
Cump
Σ max(0, [161(())|1 11)1()))11,
(11)
Ldit (o)) ST(())))
(12)
Here cos(..-) denotes the cosine similarity between temporal gradients (i.e., the dot product of b-- normalized vectors), and we set (Aair, Aump; Adiz) = (0.7, 0.2, 0.1) in all experiments.
The first term, Lair, encourages the rendered sequence exhibit similar frame changes the real sequence, so that events such as object motion or appearance/disappeaaane occur with the correct spatial support. The second term, Lamp prevents the network from underestimating motion magni- tude: even the direction is correct, overly small temporal differences would lead to over-smoothed dynamics. The third term, Ldir- focuses on the direction of change and penalizes cases where the rendered motion points in a different direction from the ground truth. Together, these terms ask the model not only to reconstruct individual frames, but also to match how they change over time, which substantially reduces flickering and yields more coherent motion.
EXPERIMENTS
5.1 EXPERIMENTS SETTINGS
Datasets. We evaluate our method on two representative real-world dynamic scene datasets: 1) Hy- PerNeRF dataset Park al. (2021b) is captured with 1-2 cameras, following straightforward camera motion. It contains complex dynamic variations, such as human movements and object deforma- tions. 2) Neural 3D Video dataset Li al. (2022b) contains real-world scenes. These scenes feature relatively long durations and diverse motions, some containing multiple moving objects. Each scene has approximately 20 synchronized videos. Except for the flame salmon scene, which consists of 1200 frames, all other scenes comprise 300 frames. For each scene, we select one camera view for testing while using the remaining views for training.
Metrics. To evaluate reconstruction quality, we employ seak-signal-to-nois ratio (PSNR), struc- tural similarity index (SSIM), multi-scale SSIM (MS-SSIM), and perceptual quality measure LPIPS with an AlexNet Backbone assess the rendered images. Additionally, we evaluate storage effi- ciency by calculating the output file size as storage (MB). We also measured the rendering speed Chh
(FPS). On HyperNeRR dataset, we further report temporal LPIPS (tLPIPS) Chu et al. (2018) met- ric computed on frame-to to---t--fffe to-frame differences to quantify temporal perceptual quality and temporal coherence.
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Flame steak
Flame salmon
4DGS
GaGS
Ours
Ground truth
Figure 4: Visualization comparison between baselines and our methods from the Neural 3D Video dataset. The main differences are highlighted and zoomed in with boxes.
Implementation details. We adhere to the settings of the original 3DGS paper for the static at- tributes our Gaussian representation. Our dynamic model operates on time window T frames and utilizes 2048 control nodes at the beginning guide the scene deformation. The train- ing process follows a two-stage optimization: node initialization and warm-up stage spanning 5,000 iterations, followed by main dynamic training stage of 35,000 iterations where all param- eters, including the temporal attention network and the canonical Gaussians, are jointly optimized. For our composite loss function, the DSSIM weight Adssim is set 0.2, and the weights for our motion-awe -wwrre aware loss components are determined through preliminary experiments. All experiments conducted on a single NVIDIA RTX 3090 GPU.
5.2 COMPARISON WITH STATE-OF-THE-ART
Table 1: Quantitative comparison datasets. Best is bold and second-best is underlined.
Neural 3D Video
HyperNeRF-vrig
Method
PSNR
SSIM1
LPIPS
PSNR
MS-SSIMI
tLPIPS
FPS
Storage
D-3DGS
31.15
0.941
0.078
25.0
0.70
0.0234
14.2
172 MB
E-D3DGS
30.86
0.938
0.048
25.4
0.70
0.0257
45.2
64 MB
4DGS
31.58
0.942
0.055
25.2
0.68
0.0248
45.0
59 MB
SC-GS
30.20
0.935
0.067
23.6
0.66
0.0236
24.5
85 MB
GaGS
31.10
0.944
0.060
24.3
0.65
0.0233
12.0
48 MB
MotionGS
24.6
0.71
0.0229
39.9
69 MB
TomeFrrmer
31.84
0.941
24.3
0.68
0.0265
40.9
46 MB
Ours
31.89
0.942
0.049
26.2
0.78
0.0196
149.5
60 MB
We compare Mango-Go GS with recent dynamic-seee scene methods, including Deformable 3DGS (D- 3DGS) Yang et al. (2024), E-D3DGS [ae et al. (2024), 4DGS Wu et al. (2024). SC-GS Huang al. (2024), GaGS Lu et al. (2024b), MotionGS Zhu al. (2024b) and TimeFormer (w/ 4DGS) Jiang et al. 2025). Unless otherwise noted, numbers averaged over all test views of each dataset.
Neural 3D video. Mango-GS achieves the highest PSNR and competitive SSIM and LPIPS, as shown in Tab. [ Deformable 3DGS reaches the second-best PSNR and SSIM but with higher LPIPS, and E-D3DGS improves perceptual quality at the cost of PSNR. 4D-GS and GaGS are competitive but fall short overall. We note that MotionGS do not report metries the Neural 3D Video benchmark in Tab. [ so we primarily compare against the others. For visual assessment, qualitative comparisons in Fig. show that method produces superior results; as highlighted in
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the boxed areas, Mango-GS renders sharper details in both the dynamic foreground subjects and the static background.
Peel-hanana
Chicken
3DPrinter
Ctu-lemon
SC-GS
4DGS
GaGS
Ours
Ground truth
Figure 5: Qualitative comparison on HyperNeRf. Our method offers sharp results. Differences are highlighted with boxes.
HyperNeRE. Mango-Gi GS achieves the best accuracy HyperNeRF, shown in Tab. Our method attains the highest PSNR and MS-SSIM as well as the lowest temporal LPIPS (tLPIPS), outper- forming strong baselines such MotionGS and TimeFormer in both spatial and temporal quality. Thanks to our multi-frame inference framework, Mango-GS runs at 149.5 FPS, well above the other methods, while keeping its storage size 60 MB, which is lower than Deformable 3DGS and SC- GS, and comparable to the others. In particular, Mango-GS is over 3x faster than MotionGS and TimeFormer while achieving superior tLPIPS and MS SSIM, indicating better trade-off between efficiency and reconstruction fidelity. Furthermore. a per-scene breakdown on the HyperNeRF-vrig sequences provided in the supplementary material (Tab. (). To further assess performance, we provide qualitative comparisons in Fig. []] Existing methods often struggle with fast intricate motion, introducing noticeable blurriness and ghosting artifacts. contrast, Mango-GS generates sharp and temporally coherent results, accurately capturing fine details in both static and dynamic regions.
5.3 ABLATION STUDIES
To validate design choices and analyze the impact of key hyperparameters, conduct series of comprehensive ablation studies on the HyperNeRF dataset. We investigate both the parameters of our model and the contributions of its core architectural components.
Time window and K-neighbors. We study the impact of the temporal window size T and the number of nearest neighbors K. The results are summarized in Tab. 2 For the time window T. observe that performance peaks at T 6. A smaller window is insufficient to capture long-range motion dependencies, while larger window introduces slight performance drop and significantly

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Table 2: Ablation on time window size T and number of neighbors K.
(a) Temporal Window Size (7)
(b) Number of Neighbors (K)
PSNR
SSIM 1
tLPIPS 4
FPS 1
PSNR
SSIM f
tLPIPS +
27.53
0.925
0.0225
87.9
27.41
0,920
0.0205
28.19
0.937
0,0203
117.8
28.39
0.942
0.0196
6
28.35
0,942
0.0196
149.5
4
28.26
0.938
0,0199
28.24
0.940
0.0197
156.2
27.90
0.931
0.0203
Table 3: Ablation the core components of Mango-GS. We report results from progressively adding each component to a baseline.
Step
Method
PSNR
SSIM f
LPIPS 4
tLPIPS
Baseline (single frame)
25.15
0,875
0.139
0.0250
Nodes (w/o learned affinity)
24.52
0.868
0.142
0.0235
3
+ Decoupled nodes (with learned affinity)
25.31
0.892
0.118
0.0223
+ Multi-frame (w/o temporal attention)
27.30
0.928
0.096
0.0225
5
Multi-frame (with temporal attention)
27.78
0.937
0.084
0.0196
+ Top-k loss
28.05
0.941
0.077
0,0202
Motion-awee aware loss
28.32
0.942
0.071
0,0192
reduces rendering speed, as it increases the sequence length for the attention mechanism. Our default setting of T 6 provides the best balance of quality and performance, achieving 149.5 FPS. For yields the best results. A smaller K may not provide enough information for stable deformation, while larger K tends to over- over-smooth the motion, losing fine- grained details. Consistent with these trends, tLPIPS improves as T increases from 2 to but slightly degrades T = suggesting that overly large temporal windows over-smooth the motion and hurt temporal perceptual quality; similarly. tLPIPS is minimized at K = 3, while larger K values again lead to over smoothing and worse temporal coherence.
number neighbors K,
Analysis of model components. As shown in Tab. 3 starting from a single frame baseline, intro- ducing nodes without learned affinity harms stability due to purely spatial propagation. Decoupling node position and code with a learned affinity restores reliable correspondences and improves de- tail. Moving to multi-frame setting with simple temporal MLP layers provides clear boost by leveraging temporal context, and inserting temporal self self-attention further benefits the performance. Adding the top-k hard-frame loss sharpens difficutt regions and improves perceptual quality. Finally, the motion-aware loss yields the most coherent temporal transitions, completing the full model. Nu- merically, iLPIPS exhibits clear drops when introducing decoupled nodes, temporal attention multi-frame setting, and the motion-aware loss, indicating progressively better temporal coherence. Although adding the top-k loss alone may slightly increase tLPIPS while noticeably improving PSNR. combining with the motion-aware loss yields the lowest tLPIPS, showing that focusing hard frames and enforcing global motion consistency are complementary and jointly improve both spatial quality and temporal stability.
CONCLUSION
We introduced Mango-GS, node-guided approach to dynamic scene reconstruction with Gaussian splatting. By decoupling control nodes into position and latent code, the method forms stable, semantics-aware neighborhoods; multi-frame temporal attention module predicts coherent node trajectories that propagate to the dense Gaussians. The resulting system improves visual quality and temporal stability while maintaining high rendering speed. Future work includes extending the temporal horizon with lightweight memory, adapting online to long vidcos, and integrating stronger geometry flow priors for extreme motions.
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A APPENDIX
A.1 MULTI-FRAME GROUP SAMPLING FOR TEMPORAL. LEARNING
The effectiveness of the temporal attention network depends heavily on the quality and diversity of the training data. Thus we introduce two multi-frame group sampling strategies designed to teach model about diverse dynamics and the continuous nature time.
Datasets
Spurse Stride Sampler
Interpolation Sampler
3
4
0
2
3
5
1
3 7 11
F ...
Figure 6: Overview of multi-frame group sampling strategies. Colored blocks with number denote real frames, and gray blocks with F' indicate placeholder timestamps. A row of blocks represents the frame training window.
Sparse Stride Sampler. From a view-specific sequence {[I,, t)}, we pick real frames with fixed stride. This approach creates training groups that span a longer duration, encouraging the network to learn a more robust understanding of dynamics.
Interpolation Sampler. To train model understand continuous motion rather than discrete states, this strategy mixes real frames with placeholder timestamps {č} obtained by linear interpola- tion between nearby real times. Placeholders pass through the network but without any supervision. They act as temporal anchors to regularize the temporal attention, encouraging the model to learn smooth function of time.
We slide the T'frame window over each timeline and alternate the two samplers with fixed ratio (7:3), achieving long-range temporal coverage and local smoothness without increasing runtime.
Table 4: Ablation the ratio between sparse stride and interpolation samplers.
Sparse ratio
Interpolation ratio
PSNR*
MS-SSIM
tLPIPS.
1.0
0.0
25.4
0.74
0.0206
0.9
0.1
25.9
0.76
0.0199
0.7
0.3
26.2
0.78
0.0196
0.5
0.5
25.6
0.72
0.0210
We further study how the ratio between sparse stride and interpolation samplers affects learning. Intuitively, the sparse stride sampler exposes the network to larger temporal offsets and dynamics, while the interpolation sampler focuses on intermediate timestamps and encourages locally smooth motion. As shown in Table 4 PSNR and MS-SSIM remain relatively stable when the sparse sam- pler dominates, and the 0.7:0.3 setting used in Mango-GS achieves the best trade-off, giving the lowest tLPIPS. When the interpolation sampler dominates, both reconstruction quality and temporal consistency degrade, suggesting that strong long-rau rong-range supervision important for robust temporal modeling.
A.2 ABLATION OF THE CONTROL NODES NUMBER
Number of Control Nodes. We analyze the effect of the initial number of control nodes, N. The nodes are dynamically densified and pruned during training, counts. As shown in Tab. S performance improves as the number nodes increases from 512 to 2048, more nodes provide greater capacity to model complex deformations. However, further increasing the count to 4096 leads to a slight decline in performance, likely due to overfitting on the training views. Based this, use 2048 initial nodes default setting.
report both the initial and final
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Table 5: Ablation the number of control nodes N. We report initial and final node counts, along with reconstruction quality metrics.
Initial Nodes
Final Nodes
PSNR
SSIM
LPIPS 4
512
~~10
27.81
0.931
0.088
1024
~960
28.14
0.938
0.079
2048
~~420
28.32
0.942
0.071
4096
~2250
28.25
0.941
0.073
Table 6: Per-scene reconstruction quality of Mango-GS on the HyperNoRF-vrig dataset
Scene
PSNRT
MS-SSIM
tLPIPS.
Broom
23.3
0.72
0.0253
Teel-Banana
27.1
0.76
0.0213
3DPrinter
26.3
0.79
0.0178
Chicken
27.7
0.85
0.0139
Mean
26.2
0.78
0.0196
A.3 PER-SCENE RESULTS ON HYPERNERF-VRIG
As shown in Tab. 6 Mango- GS maintains consistently strong performance across all HyperNeRF. vrig scenes, and the per-scene scores aggregate to the same Mean values reported the main com- parison table.
A.4 CONSECUTIVE QUALITATIVE RESULTS
Finally, to visually demonstrate the temporal coherence of our method, we present uncurated, con- secutive rendering results Figure 7 The sequences exhibit exceptional continuity, with seamless and natural transitions from one moment to the next. Our method successfully avoids flickering ar- tifacts and maintains sharp, clear object boundaries. This high level of temporal stability is direct result of our temporal attention network, which learns continuous representation of motion rather than memorizing discrete frames. Furthermore, these high-fidelity results are rendered in real time, achieving 155.6 FPS at resolution of 544 x 960, underscoring the efficiency of our framework.
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Tg
T.
T
T
T,
T
Tg
T,
T,
T,
Ti
Tn
Figure 7: Sequence of 12 consecutive inference results from Mango-GS (T 6). Each group displays complete inference output specific timestamps.
16


Reviews:
[{"review_id": "OUAOLtH18P", "reviewer": "Reviewer_6RAS", "summary": "This paper presents Mango-GS, a multi-frame node-guided framework for dynamic 3D scene reconstruction using Gaussian splatting. The key innovation lies in decoupling control nodes into canonical positions and learnable feature codes, combined with a temporal transformer that processes multiple frames simultaneously to learn motion dynamics. The sparse control nodes guide dense 3D Gaussians through learned k-NN relationships, enabling efficient temporal modeling while avoiding per-frame overfitting. Experiments on HyperNeRF and Neural 3D Video datasets demonstrate state-of-the-art reconstruction quality with real-time rendering speeds of 149.5 FPS and reasonable storage requirements of 60 MB.", "questions": "* How does the method handle scenarios where objects enter or leave the scene mid-sequence? Does the k-NN relationship remain stable, or do you need special handling for appearance/disappearance events? Please provide examples or discuss this limitation.\n* You claim T=6 provides the best balance (Table 2a), but the performance difference between T=6 and T=8 is marginal (28.35 vs 28.24 PSNR) while T=8 achieves higher FPS (156.2). Can you provide more analysis on this trade-off? Are there specific motion types that benefit from larger T?\n* The method uses 2048 initial control nodes which are dynamically densified/pruned. What are the densification and pruning criteria?\n* In Figure 5, your method shows sharper results than baselines. However, could you provide temporal consistency metrics? Static frame comparisons don't fully validate the claimed temporal coherence improvements. Have you conducted user studies comparing temporal quality?\n* The top-k hard-frame loss selects the k frames with highest error per batch. What is k as a fraction of batch size? If k is too small, does this ignore most training signals? If k is too large, does this reduce to average loss? In Table 3, adding top-k loss improves LPIPS from 0.084 to 0.077, but this could simply be due to focusing on high-frequency details in difficult frames rather than improving overall consistency.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "* The decoupled node representation is a well-motivated design that elegantly addresses the neighborhood drift problem in large motion scenarios.\n* The multi-frame temporal attention mechanism represents a significant departure from per-frame optimization strategies prevalent in prior work. This design enables the model to learn motion patterns rather than memorize instantaneous states, leading to improved temporal coherence as evidenced by both quantitative metrics and qualitative visualizations.\n* The ablation studies systematically validate each component's contribution, and the method achieves excellent rendering speed while maintaining competitive storage efficiency.", "weaknesses": "* The theoretical justification for why the decoupled representation prevents neighborhood drift is primarily empirical. While Figure 2 provides visual evidence, a more rigorous analysis of the learned feature space and how it maintains semantic consistency under large deformations would strengthen the claims.\n*  The motion-aware loss components ($L_{diff}, L_{dir}$) are mentioned but never formally defined in the main paper.\n* The evaluation focuses heavily on PSNR/SSIM metrics, but temporal consistency evaluation is limited. While motion-aware loss is used during training, there are no temporal metrics in the evaluation (e.g., temporal LPIPS, or frame-to-frame stability measures). This is a significant omission for a method claiming temporal coherence as a primary contribution.\n* The comparison with SC-GS shows lower performance (Table 1: 30.20 vs 31.89 PSNR), but SC-GS also uses control nodes. The paper doesn't adequately explain why their approach differs beyond adding temporal attention. A more detailed comparison highlighting the specific differences in node design and their impact would be valuable.\n* The multi-frame sampling strategy uses an arbitrary 7:3 ratio between sparse stride and interpolation samplers with no justification or ablation. How sensitive is training to this ratio? Why not 5:5 or 9:1?", "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": "Equ6t3zY6O", "reviewer": "Reviewer_ZZyk", "summary": "This paper presents a 4D scene reconstruction approach that ensures both photorealistic detail and temporal coherence. It leverages a temporal transformer to model complex motion dependencies across multiple frames, guided by a set of sparse control nodes with decoupled position and latent codes. This design enables stable motion representation and prevents correspondence errors during large movements. Trained end-to-end with multi-frame objectives, Mango-GS achieves state-of-the-art reconstruction quality and real-time rendering performance.", "questions": "1. Please include the cites in Table 1. \n2. Please add the discussions with other approaches that focus on the temporal modeling of Gaussians. e.g. A, B.\n\n[A] MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting\n[B] TimeFormer: Capturing Temporal Relationships of Deformable 3D Gaussians for Robust Reconstruction", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Decoupling the 4DGS makes sense.\n2. This paper presents a good FPS performance.", "weaknesses": "1. For the quantitative comparison in Table 1. The improvement in PSNR is limited.\n2. The visualized comparison is weak. It is better to provide video comparison in supplementarials.", "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": "mcTfQLSEfg", "reviewer": "Reviewer_ytGT", "summary": "The paper first points out that existing Gaussian Splatting methods rely on per-frame optimization, causing them to memorize frame-specific states rather than learning true motion dynamics. To address this, it proposes Mango-GS, a multi-frame, node-guided 4D Gaussian Splatting framework that introduces a decoupled control node representation and a temporal attention network. The method aims to enable efficient, temporally consistent, and high-fidelity dynamic scene reconstruction.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper clearly identifies a core weakness in existing dynamic 3D Gaussian Splatting methods, namely their reliance on per-frame optimization, which causes temporal inconsistency and overfitting to instantaneous states. The motivation for introducing a multi-frame modeling framework is well justified and directly addresses this limitation.\n\n* The proposed multi-frame temporal deformation network interleaves MLP layers with temporal self-attention blocks and a gated fusion mechanism. This hybrid design effectively captures long-range motion dependencies while remaining computationally efficient. It represents a well-balanced approach between expressive modeling and real-time rendering.\n\n* The paper is well organized, with clear reasoning and smooth transitions between sections, making the overall presentation easy to follow.", "weaknesses": "* My main concern regarding the design of Mango-GS lies in the insufficient justification for using a Transformer to address temporal inconsistency. First, the advantage of Transformer is its ability to capture long-range dependencies, but Table 2 shows that the optimal temporal window is only six frames. Is using a Transformer to model such a short sequence truly necessary, and does the computational cost justify the potential performance gain? Furthermore, several prior works have also explored multi-frame motion modeling to improve temporal coherence. The paper should include an ablation study comparing the Transformer with other mechanisms that leverage multi-frame motion information, in order to clearly demonstrate the advantages of using a Transformer in this context.\n\n* In Figure 4, the highlighted regions should be enlarged for better visual inspection.\n\n* In Figure 5, the visual differences among compared methods are not very clear. For example, in the *Peel-Banana* scene, the results of 4DGS and Mango-GS appear quite similar, and in the *Cut-lemon* scene, differences among all methods are subtle. The paper should select frames with more noticeable differences or to use highlighted and zoomed-in regions to better demonstrate the superiority of Mango-GS. Based on the current visualizations, I do not observe a significant advantage of the proposed method.\n\n* In the experimental section, all compared methods are from 2023-2024. The paper should include comparisons with the latest approaches and should report detailed per-scene quantitative results.\n\n* The paper should also evaluate the method on more challenging datasets, such as the iPhone dataset, to further validate its robustness and generalization ability.\n* The discussion of related work should be expanded to include more recent studies, as the current review is not sufficiently comprehensive.\n* If possible, I hope to see video results to more intuitively understand the advantage of Mango-GS in motion consistency.", "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": "xwzAXupT3G", "reviewer": "Reviewer_NRrk", "summary": "The paper tackles real-time dynamic scene reconstruction from monocular videos based on SC-GS, aiming to model long-range, non-linear motion with temporal coherence. The authors propose Mango-GS, which couples 3D Gaussian Splatting with a node-guided multi-frame network:  (1) an MLP backbone interleaves temporal self-attention and gated fusion to propagate dependencies across a window of $T$ frames and predict per-node deformations; (2) training employs temporal input masking plus a composite loss with frame loss and motion loss. Experiments on Neural 3D Video, and HyperNeRF-vrig report consistent rendering quality improvement and stability while remaining real-time rendering speed.", "questions": "1. What is the approximate training time? How much overhead is there relative to SC-GS?\n2. Could you clarify how the temporal input masking is constructed (random or structured across time)? What masking ratio do you use, and is it fixed or scheduled during training?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "- This paper is well-written and easy to understand.\n- I appreciate the paper’s attempt to process multiple frames jointly. Handling T frames simultaneously and introducing self-attention across different timestamps make sense, and this idea could inspire future work on monocular (multi-view) dynamic reconstruction.”\n- The ablation is appreciated to show the important contribution of each part.\n- Across different real-world datasets, the results are consistently strong, with visualizations aligning well with the quantitative metrics.", "weaknesses": "- Missing references for some important dynamic reconstruction works:\n  - [NeurIPS 2024] Grid4D: 4D Decomposed Hash Encoding for High-Fidelity Dynamic Gaussian Splatting, by Jiawei Xu et al.\n  - [NeurIPS 2024] Splatter a Video: Video Gaussian Representation for Versatile Processing, by Yang-Tian Sun et al.\n- Using a lightweight MLP to compute the implicit distance between control points and Gaussians is insightful. However, I don’t find the benefit of this design immediately clear from Figure 2. Have the authors explored alternative formulations, such as a codebook approach similar to VQ-VAE?\n- A minor suggestion: Figure 3 does not clearly convey self-attention across time. Also, if my understanding is correct, position and timestamp should be provided jointly as input to the MLP?\n- Minor typo errors:\n  - L23: `which provides` -> `which provide`\n  - L24: `prevents correspondence error` -> `prevents correspondence errors`\n  - L695-696: `Each groups displays` -> `Each group displays`", "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)