1000+ FPS 4D Gaussian Splatting for Dynamic Scene Rendering
Authors: Yuheng Yuan
Qiuhong Shen
Xingyi Yang
Xinchao Wang
Venue: NeurIPS 2025 poster ()
Keywords: 4d gaussian splatting
efficient dynamic scene representation
Primary area: applications

Abstract: 4D Gaussian Splatting (4DGS) has recently gained considerable attention as a method for reconstructing dynamic scenes. Despite achieving superior quality, 4DGS typically requires substantial storage and suffers from slow rendering speed. In this work, we delve into these issues and identify two key sources of temporal redundancy. (Q1) \textbf{Short-Lifespan Gaussians}: 4DGS uses a large portion of Gaussians with short temporal span to represent scene dynamics, leading to an excessive number of Gaussians. (Q2) \textbf{Inactive Gaussians}: When rendering, only a small subset of Gaussians contributes to each frame. Despite this, all Gaussians are processed during rasterization, resulting in redundant computation overhead. To address these redundancies, we present \textbf{4DGS-1K}, which runs at over 1000 FPS on modern GPUs. For Q1, we introduce the Spatial-Temporal Variation Score, a new pruning criterion that effectively removes short-lifespan Gaussians while encouraging 4DGS to capture scene dynamics using Gaussians with longer temporal spans. For Q2, we store a mask for active Gaussians across consecutive frames, significantly reducing redundant computations. Compared to vanilla 4DGS, our method achieves a $41\times$ reduction in storage and $9\times$ faster rasterization on complex dynamic scenes, while maintaining comparable visual quality.

1000+ FPS 4D Gaussian Splatting for Dynamic Scene Rendering
Yuheng Yuan
Qiuhong Shen 1.+ Xingyi Yang2.1 Xinchao Wangh
National University of Singapore "The Hong Kong Polytechnic University
(yuhengyuan, qiuhong. shen ou nus edu, xingyi. yanggoolu edu hk, xinchao@nus.cn edu, 5g
Project page: https: //d::////dgg--aa 1k. github.iiin
4DGS
Ours
Mem: 2.16GB, Raster FPS: 103
PSNR: 33.16dB
Mem: 53MB, Raster FPS: 1088
PSNR: 33.06dB
32.0
STG
Ours
PSNR(OB)
31.5
-E-D3DGS
31.0
4DGussians
30.5
Dynamic
30.0
600 800
Figure 1: Compressibility and Rendering Speed. We introduce 4DGS-1K, novel compact representation with high rendering speed. contrast to 4D Gaussian Splatting (4DGS) [ we can achieve rasterization 1000+ FPS while maintaining comparable photorealistic quality with only 2% of the original storage size. The right figure is the result tested on the N3V 2 datasets, where the radius of the dot corresponds to the storage size.
Abstract
4D Gaussian Splatting (4DGS) has recently gained considerable attention as a method for reconstructing dynamic scenes. Despite achieving superior quality, 4DGS typically requires substantial storage and suffers from slow rendering speed. In this work, we delve into these issues and identify two key sources of temporal redundancy. (Q1) Short-Lifespan Gaussians: 4DGS uses a large portion dynamics, leading (Q2) Inactive Gaussians: When rendering, only a small subset of Gaussians contributes each frame. Despite this, all Gaussians are processed during rasterization, resulting in redundant computation overhead. present 4DGS-1K, which FPS on modern GPUs. For QI, we introduce the Score, new pruning criterion that effectively removes short-lifespan Gaussians while encouraging 4DGS to capture scene dynamics using Gaussians with longer temporal spans. For Q2, we store mask for active Gaussians across consecutive frames, significantly reducing redundant computations. Compared to vanilla 4DGS, our method achieves 41x reduction in storage and 9x faster rasterization on complex dynamic scenes, while maintaining comparable visual quality.
Gaussians with short temporal span represent
excessive number Gaussians.
To address these redundancies,
1000 Variation
Introduction
Novel view synthesis for dynamic scenes allows for the creation of realistic representations of 4D environments, which is essential in fields like computer vision, virtual reality, and augmented reality. Traditionally, this area has been led by neural radiance fields (NeRF) (26), which model opacity and
"Equal contribation. "oorespoondig Author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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color over time to depict dynamic scenes. While effective, these NeRF-based methods come with high training and rendering costs, limiting their practicality, especially in real-time applications and on devices with limited resources.
Recently, point-based representations like 4D Gaussian Splatting (4DGS) 1 have emerged strong alternatives. 4DGS models dynamic scene using a set of 4D Gaussian primitives, each with a 4-dimensional mean and a covariance matrix. At any given timestamp, 4D Gaussian is decomposed into set of conditional 3D Gaussians and marginal ID Gaussian, the latter controlling the opacity at that moment. This mechanism allows 4DGS to effectively capture both static and dynamic features of scene, enabling high-fidelity dynamic scene reconstruction.
However, representing dynamic scenes with 4DGS is both storage storage-intensiee intensive and slow. Specifically, 4DGS often requires millions of Gaussians, leading to significant storage demands (averaging 2GB for each scene the N3V (2) dataset) and suboptimal rendering speed. In comparison, mainstream deformation field methods the storage size of 4DGS [ and improving rendering speed are essential for efficiently representing complex dynamic scenes.
require only about 90MB for the same dataset. Therefore, reducing
We look into the cause of such an explosive number of Gaussian and place specific emphasis two key issues. (Q1) A large portion of Gaussians exhibit a short temporal span. In empirical experiments, 4DGS tends to favor "flicking" Gaussians to fit complex dynamic scenes, which just influence short portion of the temporal domain. This necessitates that 4DGS relies on a large number of Gaussians to reconstruct high-fidelity scene. As result, substantial storage needed to record the attributes of these Gaussians. (Q2) Inactive Gaussians lead to redundant computation. During rendering, 4DGS needs to process all Gaussians. However, only very small portion of Gaussians are active at that moment. Therefore, most of the computation time is spent on inactive Gaussians. This phenomenon greatly hampers the rendering speed. In this paper, framework that significantly reduces the number of Gaussians to minimize storage requirements and speedup rendering while maintaining high-quality reconstruction. To address these issues, 4DGS-IK introduces two-step pruning approach:
introduce 4DGS-1K,
Pruning Short-Lifespan Gaussians. We propose a novel pruning criterion called the spatial- temporal variation score, which evaluates the temporal impact of each Gaussian. Gaussians with minimal influence are identified and pruned, resulting in a more compact scene representation with fewer Gaussians with short temporal span.
Filtering Inactive Gaussians. To further reduce redundant computations during rendering. we use a key-frame temporal filter that selects the Gaussians needed for each frame. On top of this, we share the masks for adjacent frames. This is based on our observation that Gaussians active in adjacent frames often overlap significantly.
Besides, the pruning in step enhances the masking process in step By pruning Gaussians, we increase the temporal influence of each Gaussian, which allows us select sparser key frames and further reduce storage requirements.
We have extensively tested our proposed model on various dynamic scene datasets including real and synthetic scenes. As shown in Figure 4DGS-1K reduces storage costs by 41 x on the Neural 3D Video datasets (2) while maintaining equivalent scene representation quality. Crucially, it enables real- time rasterization speeds exceeding 1,000 FPS. These advancements collectively position 4DGS-IK as practical solution for high-fidelity dynamic scene modeling without compromising efficiency.
In summary, our contributions are three-fold:
We delve into the temporal redundancy of 4D Gaussian Splatting, and explain the main reason for the storage pressure and suboptimal rendering speed.
We introduce 4DGS-1K, compact and memory-efficient framework address these issues. consists of two key components, a spatial-temporal variation score-based pruning strategy and temporal filter.
Extensive experiments demonstrate that 4DGS- 1K not only achieves a substantial storage reduction of approximately 41 but also accelerates rasterization to 1000+ FPS while maintaining high- quality reconstruction.

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2 Related Work
2.1 Novel view synthesis for static scenes
Recently, neural radiance fields(NeRF) [ have achieved encouraging results in novel view synthesis. NeRF [3] represents the scene by mapping 3D coordinates and view dependency to color and opacity. Since NeRF [3] requires sampling each ray by querying the MLP for hundreds of points, this significantly limits the training and rendering speed. Subsequent studies [8H15] have attempted to speed up the rendering by introducing specialized designs. However, these designs also constrain the widespread application of these models. In contrast, 3D Gaussian Splatting(3DGG) gained significant attention, which utilizes anisotropic 3D Gaussians to represent scenes. It achieves high-quality results with intricate details, while maintaining real-time rendering performance.
[66 has
2.2 Novel view synthesis for dynamic scenes
Dynamic NVS poses new challenges due to the temporal variations in the input images. Previous NeRF-based dynamic scene representation methods [2] 4 17 handle dynamic scenes by learning a mapping from spatiotemporal coordinates to color and density. Unfortunately. these NeRF-based models constrained in their applications due to low rendering speeds. Recently, 3D Gaussians Splatting [16] has emerged as a novel explicit representation, with many studies (3) attempting to model the dynamic scenes based on it. 4D Gaussian Splatting(4DGS) [I is one of the representatives. It utilizes a set of 4D Gaussian primitives. However, 4DGS often requires a huge redundant number of Gaussians for dynamic scenes. These Gaussians lead tremendous storage and suboptimal rendering speed. To this end, we focus on analyzing the temporal redundancy of 4DGS [ hopes of developing novel framework to achieve lower storage requirements and higher rendering speeds.
2.3 Gaussian Splatting Compression
3D Gaussian- based large-scale scene reconstruction typically requires millions of Gaussians, resulting in the requirement of up several gigabytes of storage. Therefore, subsequent studies have attempted to tackle these issues. Specifically, Compgs 28 and Compact31) (29 employ vector quantization to store Gaussians within codebooks. Concurrently, inspired by model pruning,
studies 30
have proposed criterion to prune Gaussians by a specified ratio. However, compared to 3DGS 16),
4DGS [ introduces an extra temporal dimension enable dynamic representation. Previous 3DGS- based methods may therefore be unsuitable for 4DGS. Consequently, we first identify key limitation leading to this problem, referred as temporal redundancy. Furthermore, we propose a novel pruning criterion leveraging spatial-tent temporal variation, and temporal filter to achieve more efficient storage requirements and higher rendering speed.
3 Preliminary of 4D Gaussian Splatting
Our framework builds on 4D Gaussian Splatting (4DGS) D. which reconstructs dynamic scenes by optimizing collection of anisotropic 4D Gaussian primitives. For each Gaussian, it is characterized by a 4D mean μ = (Mas My Pas Ub) E R coupled with a covariance matrix V E g4x4
By treating time and space dimensions equally, the 4D covariance matrix Σ can be decomposed into a scaling matrix SAD = (S2, Sy, $2, 8t) E I and rotation matrix RAD E 4x4, RAD is represented by a pair of left quaternion q E IR4 and right quaternion 4- IR..
During rendering, each 4D Gaussian is decomposed into a conditional 3D Gaussian and a ID Gaussian at specific time t. Morcover, the conditional 3D Gaussian can be derived from the properties of the multivariate Gaussian with:
Pzyzlt = P1: E1:3, (4)
Eggsit = E13,13 - Σ1.3, - 12,1:3
5- -
Here, /43 E 3 and 3:3,1 E 3x3 denote the spatial mean and covariance, while μe and Σ4,4 are scalars representing the temporal components. To perform rasterization, given pixel under view I
3

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1.0
Ourse:: Fille) Oursina Filter)
4005
4005 Ourswo
Percentage
Active Ratio
Oursivo Fiterr
Activation lol
0.6
0.4
0.4
0.2
0.2
0.2
0.0
0.5
1.0
1.5
0.0
0
100
200
300
0.0
50
100
150
the Value of 2;
Timestamp
Timestamp
(a)
Figure 2: Temporal redundancy Study. (a) The E, distribution of 4DGS. The red line shows the result of vanilla 4DGS. The other two lines represent our model has effectively reduced the number of transient Gaussians with small Σg. (b) The active ratio during rendering at different timestamps. demonstrates that most of the computation time is spent on inactive Gaussians in vanilla 4DGS. However, 4DGS- IK can significantly reduce the occurrence of inactive Gaussians during rendering to avoid unnecessary computations. in the first frame and frame t. It proves that active Gaussians tend to overlap significantly across adjacent frames.
(c) This figure shows the loU between the set active Gaussians
and timestamp I, its color I(u, U, () can be computed by blending visible Gaussians that are sorted by their depth:
u(,,utt) Σ (d)) II ag)
with
0; - (u, o|))o (t) ~ Ntt; Σ,,)
(3)
where c; (d)) is the color of each Gaussian, and a; is given by evaluating 2D Gaussian with covariance EnD multiplied with learned per-point opacity and temporal Gaussian distribution (t).. In the following discussion, we denote Σ,,4 as Σ, for simplicity.
Temporal Redundancy. Despite achieving high quality, 4DGS requires a huge number of Gaussians to model dynamic scenes. We identify key limitation leading this problem: 4DGS represents scenes through temporally independent Gaussians that lack explicit correlation across time. This means that, even static objects are redundantly represented by hundreds of Gaussians, which incon- sistently appear or vanish across timesteps. We refer to this phenomenon as temporal redundancy. As result, scenes end up needing more Gaussians than they should, leading excessive storage demands and suboptimal rendering speeds. In Section we analyze the root causes of this issue and propose a of solutions to reduce the count of Gaussians.
4 Methodology
Our goal is to compress 4DGS by reducing the number of Gaussians while preserving rendering quality. To achieve this, we first analyze the redundancies present in 4DGS, as detailed in Section 4.1 Building on this analysis, we introduce 4DGS-IK in Section 4.2] which incorporates a set of compression techniques designed for 4DGS. 4DGS-1K enables rendering speeds of over 1,000 FPS on modern GPUs.
4.1 Understanding Redundancy 4DGS
This section investigates why 4DGS requires excessive number of Gaussians to represent dynamic scenes. In particular, we identify two key factors. First, 4DGS models object motion using a large number of transient Gaussians that inconsistently appear and disappear across timesteps, leading to redundant temporal representations. Second, for each frame, only a small fraction of Gaussians actually contribute the rendering. We discuss those problems below.
Massive Lhfrt-Lifespan Gaussians. We observe that 4DGS tends to store numerous Gaussians that flicker in time. We refer to these Short-Lifespan Gaussians. To investigate this property,

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Testing Training View
Inactvve Gaussian
Filler
foode
X
ft
(a) Transient Gaussian Pruning
(b) Temporal Filter
Figure 3: Overview of 4DGS-1K, (a) We first calculate the spatial-temporal variation score for each 4D Gaussian on training views, to prune Gaussians with short lifespan (The Red Gaussian). (b) The temporal filter is introduced to filter out inactive Gaussians before the rendering process alleviate suboptimal rendering speed. At given timestamp t, the set of Gaussians participating in rendering is derived from the two adjacent key- key-frames, to and fo+
we analyze the Gaussians' opacity, which controls visibility. Intuitively, Short-Liffspan Gaussians exhibit opacity pattern that rapidly increases and then suddenly decreases. In 4DGS, this behavior typically reflected the time variance parameter E, E,-small E, values indicate short lifespan.
Observations. Specifically, we plot the distribution of > for all Gaussians in the Sear Steak scene. As shown in Figure 2a most of Gaussians has small M, values (e.g. 70% have E, < 0.25).
Therefore, in 4DGS, nearly all Gaussians have a short lifespan. This property leads to high storage needs and slower rendering.
Inactive Gaussians. Another finding is that, during the forward rendering, actually, only small fraction of Gaussians are contributing. Interestingly, active ones tend to overlap significantly across adjacent frames. To quantify this, we introduce two metrics: (1) Active ratio. This ratio defined as the proportion of the total number of active Gaussians across all views at any moment relative to the total number of Gaussians. (2) Activation Intersection oovrronn-vnn-nnon (IoU). This is computed as IoU between the set of active Gaussians in the first frame and in frame t.
Observations. Again, we plot the two metrics from Sear Steak scene. As shown in Figure 2b nearly 85% of Gaussians are inactive at each frame, even though all Gaussians are processed during rendering. Moreover, Figure demonstrates that the active Gaussians remain quite consistent over time, with an loU above 80% over 20-frame window.
The inactive gaussians bring significant issue in 4DGS, because each 4D Gaussian must be decomposed into a 3D Gaussian and a ID Gaussian before rasterization (see eq. (T). Therefore, a large portion of computational resources wasted inactive Gaussians.
In summary, redundancy in 4DGS comes from massive Short-Lifespan Gaussians and inactive Gaussians. These insights motivate our compression strategy to eliminate redundant computations while preserving rendering quality.
4.2 4DGS-1K for Fast Dynamic Scene Rendering
Building on the analysis above, we introduce 4DGS-IK, a suite of compression techniques specifically designed for 4DGS eliminate redundant Gaussians. shown in Figure this process involves two key steps. First, we identify and globally prune unimportant Gaussians with low Spatial- Temporal Variation Score Section 4.2. Second, we apply local pruning using a temporal filter to inactive Gaussians that are not needed at each timestep in Section 4.2.2
4.2.1 Pruning with Spatial-Temporal Variation Score
We first prune unimportant 4D Gaussians improve efficiency. Like 3DGS, we remove those that have a low impact on rendered pixels. Besides, we additionally remove short-lifespan Gaus-
5

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sians those that persist only briefly over time. To achieve this, we introduce a novel spatial-temporal variation score as the pruning criterion for 4DGS, composed of two parts, spatial score that measures the Gaussians contributions to the pixels in rendering, and temporal score considering the lifespan of Gaussians.
Spatial score. Inspired by the previous method [30 31 and a-blending in 3DGS [16]. we define the spatial score by aggregating the ray contribution of Gaussian 9i along all rays r across all input images at a given timestamp. It can accurately capture the contribution of each Gaussian to one pixel. Consequently, the spatial contribution score S obtained by traversing all pixels:
NHW Σ 0 1
8
where N denotes the number of training views, W denote the height and width of the images, and o; [T; az) reflects the contribution of jth Gaussian the final color of all pixels according to the alpha composition in eq. 2
Temporal score. It is expected to assign higher temporal score to Gaussians with a longer lifespan. To quantify this, we compute the second derivative of temporal opacity function (t( defined in
eq. (3. The second derivative p; (t) is computed as
(2)
2 (1) = (1 11 )2
(t(()
(5)
Σ
Intuitively, large second derivative magnitude corresponds unstable, short-lived Gaussians, while low second derivative indicates smooth, persistent ones.
Moreover, since the second derivative spans the real number domain R, apply tanh function to map it to the interval (0, 1). Consequently, the score for opacity variation, S! of each Gaussian It. is expressed as:
1
STV
t=0 0.5 tanh( p; (2) (t) 0.5
In addition to the opacity range rate. the volume of 4D Gaussians is necessary to be considered, as described in eq. (I). The volume should be normalized following the method in (30), denoted as 7(54)= Norm(V(S**)
Therefore, the final temporal score S = S (S,"))
Finally, by aggregating both spatial and temporal score, the spatial-temporal variation score S can be written as:
S
Pruning. All 4D Gaussians are ranked based on their spatial-temporal variation score S, and
Gaussians with lower scores are pruned to reduce the storage burden of 4DGS []. The remaining Gaussians are optimized over a number of iterations compensate for minor losses resulting from pruning.
4.2.2 Fast rendering with temporal filtering
Our analysis reveals that inactive Gaussians induces unnecessary computations in 4DGS, significantly slowing down rendering. To address this issue, we introduce a temporal filter that dynamically selects active Gaussians. We observed that active Gaussians in adjacent frames overlap considerably (as detailed in Section 4.1 which allows us to share their corresponding masks across window of frames.
Key-frame based Temporal Filtering. Based on this observation, we design a key-frame based temporal filtering for active Gaussians. We select sparse key-frames even intervals and share their masks with surrounding frames.
Specifically, we select list of key-frame timestamps (4, A, For each 4, we render the images from all training views at current timestamp and calculate the visibility list {m, J. 1 where mijj is the visibility mask obtained by eq. (2) from the jth training
(4) where T depends on the chosen interval

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Table 1: Quantitative comparisons the Neural 3D Video Dataset.
Method
PSNRT
SSIMT
LPIPS
Storage(MB]
FPST
Raster FPST
#Gauss,
Neural Volume
22.80
0.295
DyNeRF
29.58
0.083
0.015
SireamRF
28.26
5310
10.90
HyperReel 5
31.10
0.927
0.096
360
2.00
K-Planes
31.63
0.018
4K4D| 36
21.29
2519
290
Dynamic 3DGS R7
30.67
0.930
0.099
2764
4DGaussian
31.15
0,940
0.049
90
E-D3DDG 26
31.31
0.945
0.037
35
74
Swift4D
32.23
120
Grid4D 39
31.49
146
116
STG
32.05
0.946
0.044
200
140
4D-RotorGS
31.62
0,940
0.140
MEGA|42
31.49
0,056
25
77
Compact3D 29
31.69
0.945
0.054
T
32.01
0.055
114
4DGS
31.91
0.946
0.052
2085
3333160
Ours
31.88
0.946
0.052
418
805
1092
666632
Ours-PP 31.87
0.944
0.053
1092
666632
The metrics of the model are tested without "coffee martini" and the resolution set to 1024
The retrained model from the official implementation.
Table 2: Quantitative comparisons on the D-NeRF Dataset.
Method
PSNRT
SSIM
LPIPS.
MBy)
FPST
Raster FPST
#Gauss
DNERT
29.67
0.95
0.08
0.1
Vox 43
32.67
0.97
0.04
K-Planes 0
31.07
0.97
0.02
1.2
4DGaussian
32.99
0.97
0.05
18
104
Deformble3DGSS Px
40.43
0.99
0.01
27
70
131428
SC-GS[44
40.65
28
126
Grid4D
39.91
166
DD-RotorSS 41
34.26
0.97
0.03
112
1257
4DGS
34.09
0.98
0.02
4DGS'O
32.99
0.97
0.03
278
1232
445076
Ours
33.34
0.97
0.03
42
1462
2482
66460
Ours-PP
33.37
0.97
0.03
1462
2482
66460
The retrained model from the official implementation.
viewpoint at timestamp I and N the number of training views at current timestamp. The final set
of active Gaussian masks is given by {U," ma.j i==
Filter based Rendering, To render the images from any viewpoint at a given timestamp teest, we consider its nearest key-frames, denoted and ty. Then, we perform rasterization while only considering the Gaussians marked by mask {
ma ==,,"" This method explicitly filters out
inactive Gaussians speed up rendering.
Note that using long intervals may overlook some Gaussians, reducing rendering quality. Therefore, we fine-tune Gaussians recorded by the masks to compensate for losses.
Experiment
5.1 Experimental Settings
Datasets. We utilize two dynamic scene datasets to demonstrate the effectiveness of our method: (1) Neural 3D Video Dataset (N3V) 22] This dataset consists of six dynamic scenes, and the resolution 2704 2028. For a fair comparison, we align with previous work [ [4 by conducting evaluations at half-resolution of 300 frames. (2) D-NeRF Dataset [19]. This dataset is monocular video dataset comprising eight videos of synthetic scenes. We choose standard test views that originate from novel camera positions not encountered during the training process.
Evaluation Metrics. To evaluate the quality of rendering dynamic scenes, we employ several commonly used image quality assessment metrics: Peak Signal-to-Noies Ratio (PSNR). Structural
7

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Implementation Details. Our method is tested in a single RTX 3090 GPU. We train our model following the experiment setting in 4DGS [I]. After training, we perform the pruning and filtering strategy. Then, we fine-tune 4DGS-IK for 5,000 iterations while disabling additional clone/split Ground Truth
4DGS 1167 MB / 152 FPS
Ours
Ours-PP 30 MB / /955 FPS
235 MB / 935 FPS
(a) Results on Sear Steak Scene.
Ground Truth
4DGS
Qurs
Ours- PPrs-PP
1361 FPS
MB 1361 FPS
(b) Results on Trex Scene.
Figure 4: Qualitative comparisons of 4DGS and our method.
Table 3: Ablation study of per-component contribution.
ID
Datsett
PSNR1
SSIM
LPIPS
Mtorage(MB))
FPS
Ruster FPS
#Gauss]
Pruning
vanilla 4DGS'
31,91
0.9458
0,0518
2085
90
118
3333160
31,51
0.9446
0.0539
209
242
561
3333160
29.56
0.9354
0.0605
2091
300
561
3333160
31,92
0.9462
0.0513
417
312
600
666632
31.88
0.9457
0.0524
418
805
1092
666632
31.63
0.9452
0,0524
418
789
1080
666632
31.87
0.9444
0.0532
50
805
1092
666632
The result with environment map.
The result without finetuning.
Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) 45
Following the previous work, LPIPS 45) computed using AlexNet [46] and VGGNet 447 on N3V dataset and the D-NeRF dataset, respectively. Moreover, report the number of Gaussians and storage. To demonstrate the improvement in rendering speed, we report two types of FPS: (I) FPS. It considers the entire rendering function. Due to interference from other operations, it can't 't effectively demonstrate the acceleration achieved by our method.
(2) Raster FPS. It only considers the rasterization, the most computationally intensive component during rendering.
Baselines.
Our primary baseline for comparison is 4DGS M. which serves as the foundation of our model. Moreover, we compare 4DGS- IK with two concurrent works on 4D compression, MEGA [42 and Compact3D 29). Certainly, we conduct comparisons with 4D-RotorGS 41 which is another form of representation for 4D Gaussian Splatting with the capability for real-time rendering speed and high-fidelity rendering results. addition, we also compare our work against NeRF-based methods, like Neural Volume (4), DyNeRF (2). StreamRF [18), HyperReel (5), DNeRF [1). K- K-Planes [6]
and 4K4D [36]. Furthermore, other recent competitive Gaussian- methods are also considered in comparison, including Dynamic 3DGS (37) STG [40], 4DGaussian 7 E-D3DGS [26].
Swift4D 38), Grid4D
[9] and SC-GS
operations. For pruning strategy, the pruning ratio set to 80% on the N3V Dataset, and 85% on the D-NeRF Dataset. For the temporal filtering, we set the interval A, between framees to 20 frames
8

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on the N3V Dataset. Considering the varying capture speeds on the D-NeRF dataset, we select 6 key-frames rather than a specific frame interval. Additionally, to further compress the storage of 4DGS [ we implement post-processing techniques in our model, denoted as Ours-PP. It includes vector quantization
28 on SH of Gaussians and compressing the mask of filter into bits.
Note that we don't apply environment maps implemented by 4DGS on Coffee Martini and Flame Salmon scenes, which significantly affects the rendering speed. Subsequent results indicate that removing it for 4DGS-IK does not significantly degrade the rendering quality.
5.2 Results and Comparisons
Comparisons on real-world dataset. Table presents quantitative evaluation on the N3V dataset. 4DGS-1K IK achieves rendering quality comparable the current baseline. Compared to 4DGS M. we achieve 41x compression and 9x faster in rendering speed at the cost of a 0.04d reduction in PSNR. In addition, compared to MEGA 42 and Compact3D (2],. two concurrent works on 4D compression, the rendering speeds are 10x and 4x faster respectively while maintaining comparable storage requirement and high quality reconstruction. Moreover, the FPS of 4DGS- far exceeds the current pfate-of-the-and levels. It is nearly twice as fast as the current fastest model, Dynamic 3DGS 37 while requiring only 1 1% of the storage size. Additionally, 4DGS- IK achieves better visual quality than that of Dynamic 3DGS [37], with an increase of about 1.2dB in PSNR. Compared to the storage-efficient model, E-D3DGS [26 and DyNeRF [2] we achieve an increase of over 0.5dB in PSNR and fast rendering speed. Figure 4 Joffers qualitative comparisons for the Sear Steak, demonstrating that our results contain more vivid details.
Comparisons on synthetic dataset. In our experiments, we benchmarked 4DGS- IK against several baselines using the monocular synthetic dataset introduced by D-NeRF 19. The result is shown in Table 2 Compared to 4DGS D. our method achieves up to 40x compression and 4x faster worth noting that the rendering quality of our model even surpasses that of the original 4DGS, with an increase of about 0.38d in PSNR. Furthermore, our approach exhibits higher rendering quality and smaller storage overhead compared to most Gaussian-basse methods. We provide qualitative results in Figure for more visual assessment.
rendering speed.
5.3 Ablation Study
To evaluate the contribution of each component, conducted ablation experiments the N3V dataset (2]. More ablations are provided in the supplement(See appendix BB.
Pruning. As shown in Table 3 our pruning strategy achieves 5x compression ratio and 5x faster rasterization speed while slightly improving rendering quality. As shown in Figure 2a our pruning strategy also reduces the presence of Gaussians with short lifespan. As such, 4DGS- 1k processes far fewer unnecessary Gaussians (See Figure 26) during rendering. Moreover, as shown in Figure [2c] the pruning process expands the range of adjacent frames. It allows larger intervals for the temporal filter.
Temporal Filtering. As illustrated in Table 3 the results of and с are obtained by directly applying the filter without fine-tuning. It proves that this component can enhance the rendering speed of 4DGS. However, as mentioned in Section 4.1 the 4DGS contains a huge number short lifespan Gaussians. It results in some Gaussians being overlooked in the filter, causing slight decrease in rendering quality. However, through pruning, most Gaussians are ensured to have long lifespan, making them visible even at large intervals. Therefore, it alleviates the issue of Gaussians being overlooked (See f). Furthermore, appropriate fine-tuning allows the Gaussians the active Gaussians list to relearn the scene features to compensate for the loss incurred by the temporal filter (See and f).
Conclusion
In this paper, we present 4DGS-1K, compact and memory-efficient dynamic scene representation capable of running at over 1000 FPS on modern GPUs. We introduce novel pruning criterion called the spatial-temporaa variation score, which eliminates a significant number of redundant Gaussian points in 4DGS, drastically reducing storage requirements. Additionally, we propose a temporal filter that selectively activates only subset of Gaussians during each frame's rendering. This approach enables our rendering speed to far surpass that of existing basclines. Compared to vanilla 4DGS,

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4DGS-IK achieves a 41x reduction in storage and 9x faster rasterization speed while maintaining high-quality reconstruction.
Acknowledgement
This project is supported by the National Research Foundation, Singapore, under its Medium Sized Center for Advanced Robotics Technology Innovation.
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Reviews:
[{"review_id": "JswpYwDyta", "reviewer": "Reviewer_B6dT", "summary": "Drawing insight from short-lifespan Gaussians and inactive Gaussians, this paper proposes 4DGS-1K, a novel post-processing method that prunes 4D Gaussian Splatting representations. It achieves a 41× reduction in storage and a 9× increase in rasterization speed on complex dynamic scenes, while maintaining comparable visual quality.", "questions": "Please refer to Cons/Questions.", "limitations": "The limitations are discussed in the supplementary.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Pros:**\n1. The paper is well-written and provides clear insights into 4D Gaussian Splatting representations. The authors identify that short-lifespan and inactive Gaussians significantly increase storage and rendering costs while having minimal impact on visual quality.\n2. 4DGS-1K consists of two key components: a spatio-temporal variation score-based pruning strategy and a temporal filter.\n3. Using 4DGS-1K, the method achieves substantial storage reduction (~41×) and accelerates rasterization to over 1000 FPS, all while maintaining high-quality rendering.\n\n**Cons/Questions:**\n1. Overall, since this is a post-processing method, it may have some negative impact on rendering quality. Has it been considered whether this method could be applied during training to mitigate quality loss?\n2. What does NHW mean in Equation 4?\n3. Regarding the temporal score, the reviewer is curious about how transient objects in the scene are handled. Could these dynamic elements be mistakenly filtered out?\n4. What does “pp” stand for in Table 3? The reviewer recommends that the authors provide an ablation study on the fine-tuning process.\n5. The reviewer recommends that the authors add a discussion about implementing the methods on the recent SOTA 4D Gaussian Representations, e.g., Representing Long Volumetric Video with Temporal Gaussian Hierarchy.", "was_revised": true, "final_justification": "Thanks for the authors' insightful comments. Most problems have been solved. After carefully reading other reviewers’ comments, I recognize the importance of implementing this method on the 4DGS’s family, which could demonstrate the generalization of the proposed method. Therefore, I would like to maintain my initial positive score and recommend integrating this method on more baselines.", "rebuttal": "We thank the reviewer for the constructive comments. We provide our feedback as follows.\n\n> **`>>> Q1:`**\n> **If this method could be applied during training to mitigate quality loss?**\n---\n**`>>> A1:`** **Yes.** This is feasible. For example, we can periodically prune and update the visibility mask of Gaussians and continue training. In our experiments, fine-tuning the remaining Gaussians can be considered as a part of this process, and it demonstrates a significant alleviation of the rendering quality loss.\n\n> **`>>> Q2:`**\n> **What does NHW mean in Equation 4?**\n---\n\n**`>>> A2:`** $N$ denotes the number of training views, while $H$ and $W$ denote the height and width of the images, respectively. By $NHW$, we mean all pixels in the training views. In Equation 4, this indicates that aggregating the contributions of each Gaussian to all pixels across all training views. We appreciate your reminder and will revise this in the main paper.\n\n> **`>>> Q3:`**\n> **How are transient objects in the scene handled? Could these dynamic elements be mistakenly filtered out?**\n---\n\n**`>>> A3:`** Thanks for your interest. \n* We treat static and dynamic objects, including transient objects, within a unified framework. All Gaussians are evaluated together to estimate their scores and record their visibility masks. It is consistent with the insight on 4DGS for modeling dynamic scenes.\n* In fact, the dynamic elements will **not** be filtered out. We provide a visualization of the pruned Gaussians in the *Sear Steak* Scene in the Appendix(See Sec.A.4 and Fig.7). Our method prunes only redundant parts in vanilla 4DGS, while the remaining Gaussians still accurately render dynamic content such as moving hands and pets. This is because dynamic objects are not represented by a single long-lifespan Gaussian but instead by an aggregation of numerous redundant short‑lifespan Gaussians, as we mentioned in Lines 127 to 134. Our pruning strategy only removes redundant and outlier Gaussians, while preserving legitimate dynamic elements like moving objects.\n\n\n> **`>>> Q4:`**\n> **What does \"pp\" stand for in Table 3? Provide an ablation study on the fine-tuning process.**\n---\n**`>>> A4:`**\n* The abbreviation “PP” refers to the Post-Processing techniques mentioned in Lines 259–261. \n* We conducted an ablation study on fine-tuning provided in Tab.3. The fine-tuning process helps mitigate the degradation in rendering quality introduced by pruning and filtering. It achieves an improvement in PSNR from 31.63 dB to 31.88 dB. A comprehensive comparison can be found in Table 3, particularly elements (b) and (c), as well as elements (e) and (f).\n\n> **`>>> Q5:`**\n> **Discussion about implementing the methods on the recent SOTA 4D Gaussian Representations, e.g., Representing Long Volumetric Video with Temporal Gaussian Hierarchy.**\n---\n\n**`>>> A5:`** Thanks for sharing this impressive work and offering this valuable suggestion. We will discuss two current SOTA 4D Gaussian representations, Temporal Gaussian Hierarchy[1] and FreeTimeGS[2]. Meanwhile, it helps demonstrate the generalizability of our method. It will be appended to our paper.\n\nTemporal Gaussian Hierarchy[1] provides a hierarchical organization of 4DGS[3] to represent Gaussians with varying speeds. Therefore, for the pruning strategy, while considering the $\\Sigma_t$ of each Gaussian, the hierarchical depth also needs to be taken into account. For filtering, this hierarchical organization provides additional information for this process to exploit. Different intervals can then be assigned to segments at different levels. For instance, smaller intervals can be assigned to the higher-level segments to capture some short‑lifespan Gaussians. This helps us record Gaussians at a finer granularity, thereby mitigating the loss in rendering quality caused by filtering.\n\nFor FreeTimeGS, it models the lifespan of Gaussians by defining a unimodal function with a scaling parameter. \n\\begin{equation}\n\\sigma(t)=exp(-\\frac{1}{2}(\\frac{t-\\mu_t}{s})^2)\n\\end{equation}\nTherefore, for the pruning strategy, the temporal score can still be derived from the second derivative of $\\sigma(t)$, whereas the spatial scores follow the original processing procedure. As for the filter, FreeTimeGS is likely to share similar properties that active Gaussians in adjacent frames overlap considerably. Therefore, our filtering strategy can still be applied to this method.\n\n[1] Representing Long Volumetric Video with Temporal Gaussian Hierarchy.\n\n[2] FreeTimeGS: Free Gaussian Primitives at Anytime and Anywhere for Dynamic Scene Reconstruction.\n\n[3] Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting."}, {"review_id": "2Qj0KwCbmD", "reviewer": "Reviewer_nmKh", "summary": "This paper introduces 4DGS-1K, a framework for compressing dynamic 4D Gaussian scene representations. It leverages a spatio-temporal variation score to prune redundant Gaussians and employs keyframe visibility mask sharing to efficiently skip inactive Gaussians during rendering. The proposed method achieves a high compression ratio and fast rendering speeds across both real-world and synthetic datasets.", "questions": "1. It remains unclear how inactive Gaussians are actually identified in practice, even considering the brief explanations on lines 156–161 and 217–219. My current understanding is that they are selected via the temporal filtering process, but the details of computing the visibility mask for temporal filtering are not sufficiently explained (Equation 2 appears to be the standard Gaussian rendering equation, with no information about the visibility mask). \n2. I would suggest to reorganizing Section 5.3. For example,  element e, and element f mentioned on lines 293–300 have no clear references or connections for the contents in the main paper. If these contain important results or illustrations, the authors should consider moving them to the main paper to improve readability. \n3. Do the proposed strategies also improve training speed?\n\nI am willing to increase my score if my concerns and questions are addressed.", "limitations": "Missing social impact discussion.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**: \n1. the major contribution, i.e., the improvement in FPS, of this paper is noticeable compared to the listed baselines.\n2. the authors conduct many experiments to illustrate the advantages of the proposed methods.\n\n**Weakness**:\n1. It seems that most of the baseline methods used for comparison are drawn from 2024 conferences. I suggest that the authors include comparisons to more recent and competitive works to better demonstrate the advantages of their method. At a minimum (but not limited to), this should include relevant methods such as 4K4D [A], Swift4D [B], and other state-of-the-art video compression techniques.\n2. The reported performance improvements appear inconsistent, while the authors do not sufficient explain such discrepancies. In Section C, the authors present gains on selected D-NeRF scenes with reduced floaters; however, the average performance actually drops. Furthermore, while the floaters are attributed to limited training views, it is not clear how the proposed strategies directly address or improve performance specifically in such under-constrained regions.\n3. If raster FPS and the number of Gaussians are included as evaluation metrics, could the authors clarify why most of the baselines do not report values for these two metrics?\n4. The method is positioned primarily as an incremental improvement over 4DGS, which may limit its generalizability to other dynamic Gaussian frameworks, even though the authors acknowledge this in the limitations. This constraint could reduce the broader significance and impact of the work.\n\n[A] 4K4D: Real-Time 4D View Synthesis at 4K Resolution\n\n[B] Swift4D: Adaptive divide-and-conquer Gaussian Splatting for compact and efficient reconstruction of dynamic scene", "was_revised": true, "final_justification": "The authors have addressed most of my concerns in the rebuttal. I recommend incorporating the additional experiments presented during the discussion period to better demonstrate the method’s generalization capability and revising the manuscript to clarify previously confusing points.", "rebuttal": "We would like to thank the reviewer for their insightful feedback and interesting observations. We address the reviewer’s comments below and will include all the feedback in the revised version of the manuscript.\n\n> **`>>> W1:`**\n> **Comparisons with more recent and competitive works such as 4K4D, Swift4D.**\n---\n\n**`>>> A1:`** Thanks for mentioning these excellent works! As suggested, we compared our method with 4K4D[1], Swift4D[2], Grid4D[3] and DynMF[4] on the N3V dataset. As shown in the table below, the rendering quality achieved by our model exceeds that of the majority of models, except for Swift4D[2]. Furthermore, our model demonstrates substantially superior rendering speed and storage performance. This table will be appended to Tab.1 in the main paper, and will cite these impressive works.\n\n|            Method            | PSNR&nbsp;$\\uparrow$ | FPS&nbsp;$\\uparrow$ | Storage(MB)&nbsp;$\\downarrow$ |\n|:---------------------------:|:--------------------:|:-------------------:|:--------------------------:|\n| 4K4D[1]                         |     21.29       |        `290`             |           2519                 |\n| Swift4D[2]                   |      **32.23**                |    125                 |             `120`              |\n| Grid4D[3]                     |   31.49          |      116               |          146                  |\n| DynMF [4]                     |   31.49          |      197               |          176                  |\n| Ours                     |      `31.87`                |    **805**                 |          **50**                 |\n\n[1] 4K4D: Real-Time 4D View Synthesis at 4K Resolution.\n\n[2] Swift4D: Adaptive divide-and-conquer Gaussian Splatting for compact and efficient reconstruction of dynamic scene.\n\n[3] Grid4D: 4D Decomposed Hash Encoding for High-Fidelity Dynamic Gaussian Splatting.\n\n[4] DynMF: Neural Motion Factorization for Real-time Dynamic View Synthesis with 3D Gaussian Splatting.\n\n> **`>>> W2:`**\n> **Inconsistent performance between Sec.C and average performance. How do the proposed strategies directly improve performance via floaters pruning?**\n---\n\n**`>>> A2:`** \n* Sorry for any confusion! There is **no inconsistency** between the results. While the results from the original 4DGS [1] are higher than ours, those were obtained under different devices and settings. For a fair comparison, we retrained the official 4DGS code on the D-NeRF dataset using the same protocol. As shown in Table 2, retrained 4DGS achieves 32.99 dB, while our method reaches 33.37 dB. Thus, we claim the improvement.\n* For floaters pruning, floaters are typically characterized by limited spatial extent and short lifespan. Therefore, floaters tend to receive lower scores relative to other Gaussians. They are pruned directly during this stage, which leads to enhanced rendering quality.\n\n[1] Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting\n\n>**`>>> W3:`**\n> **Why do most of the baselines not report values for raster FPS and the number of Gaussians?**\n---\n\n**`>>> A3:`** We sincerely apologize for any confusion and wish to emphasize that these two evaluation metrics are meaningful within our context. However, they are not applicable when making comparisons with other baselines.\n\nMost baselines represent the motion of Gaussians through deformation fields or trajectory functions. Before rasterization, they need to obtain the Gaussian attributes at the current moment through an MLP or trajectory function $f$. Therefore, for rendering speed, rasterization is not the only factor affecting FPS and cannot be considered in isolation. For the number of Gaussians, we wish to emphasize that each method defines Gaussian attributes differently, resulting in varying storage per Gaussian. Therefore, comparing the number of Gaussians across these baselines is both unfair and not applicable. The general evaluation metric should be the storage footprint of these models.\n\n**Why do we report these two metrics?** These metrics serve primarily as indicators to demonstrate the improvements our method brings to 4DGS. Raster FPS is reported to avoid the influence of other operations within the rendering module, as discussed in Lines 864 to 868. The number of Gaussians is reported to demonstrate the high pruning rate of our method.\n\nOverall, these two metrics are applied and reported solely to 4DGS and 4DGS-1K, and are therefore not reported for the other baselines.\n\n>**`>>> W4:`**\n> **A limited generalizability which could reduce the broader significance and impact of the work.**\n---\n**`>>> A4:`** Thanks for the question! While our work builds upon 4DGS, we argue it is a significant leap forward, not incremental at all. \n\nIn terms of the task, our method introduces **order-of-magnitude improvements** over the existing 4DGS. Our method is the first to achieve over 1000 FPS for dynamic scenes. We deliver an $8×$ rendering speedup and a $41×$ storage reduction over the baseline.\n\nRegarding generalization, dynamic Gaussian methods fall broadly into two families: deformation-based methods and direct 4D Gaussians.\nWhile our method is not directly applicable to deformation-based family, it is designed to be **compatible with the entire 4DGS family**. For example, our method is compatible with other works like Temporal Gaussian Hierarchy[1] and FreeTimeGS[2].\n\n[1] Representing Long Volumetric Video with Temporal Gaussian Hierarchy.\n\n[2] FreeTimeGS: Free Gaussian Primitives at Anytime and Anywhere for Dynamic Scene Reconstruction.\n\n> **`>>> Q1:`**\n> **How are inactive Gaussians and the visibility mask actually identified?**\n---\n\n**`>>> A1:`** The visibility mask $m_{i,j}$ from $j^{th}$ training viewpoint at a given timestamp $t_i$ is naturally obtained through the **rasterization**, which has already been implemented in 3DGS[1]. Furthermore, we define inactive Gaussians as the **complement** of the visibility mask, denoted as $\\neg~ m_{i,j}$. We apologize for any confusion and will include the relevant description in the main paper.\n\n[1] 3D Gaussian Splatting for Real-Time Radiance Field Rendering.\n\n> **`>>> Q2:`**\n> **Reorganize Section 5.3 to improve readability.**\n---\n**`>>> A2:`** We appreciate your suggestion. Sec.5.3 discusses the effects of pruning, filtering, and fine-tuning, which may appear somewhat disjointed. We plan to reorganize this section and revise the table layout to improve clarity and readability. Additionally, we would like to clarify that the results in elements (e) and (f) are referred to in Line 300 to demonstrate the effect of fine-tuning on filtering.\n\n> **`>>> Q3:`**\n> **Do the proposed strategies also improve training speed?**\n---\n**`>>> A3:`** No. Our method does not demonstrate a significant improvement in training speed."}, {"review_id": "HA0sUmGZgN", "reviewer": "Reviewer_thVU", "summary": "This paper proposes a method to compress 4DGS to maintain rendering quality while improving rendering speed. 4DGS has many inactive and short-lifespan Gaussians, resulting in massive waste of computing resources. To address this problem, the authors design a ranking mechanism combining with temporal and spatial scores to evaluate the contribution of each Gaussian. The Gaussian with low score will be pruned. The experiments demonstrate the effectiveness of proposed methods.", "questions": "+ I think SOTA methods, SC-GS and Grid4D, should be included as a baseline in the experiments on the D-NeRF dataset.\n+ Lack of training speed evaluation. Will the pruning and filtering processes take a lot of time?\n+ A tiny question about the temporal score design. Why not directly use the $|\\Sigma_t|$ rather than the second derivative for the temporal score? If possible, could the authors provide some theoretical explanation?", "limitations": "Yes, the limitation has been mentioned in the supplementary.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n+ The problem of inactive and short-lifespan Gaussians are clearly analyzed.\n+ The proposed methods are very effective in maintaining rendering quality while improving rendering speed.\n\n**Weakness**\n\n+ The proposed methods are carefully designed for 4DGS[1]. This issue severely restricts their application in much higher-performance models, such as Deformable-GS[2], SC-GS[3] and Grid4D[4].\n+ When reconstructing scenes with sparse per-frame training views, such as D-NeRF, the proposed methods might lead to obvious rendering quality degradation.\n\n[1] Yang et.al. Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting, ICLR2024.\n\n[2] Yang et.al. Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction, CVPR2024.\n\n[3] Huang et.al. SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes, CVPR2024.\n\n[4] Xu et.al. Grid4D: 4D Decomposed Hash Encoding for High-Fidelity Dynamic Gaussian Splatting, NeurIPS 2024.", "was_revised": true, "final_justification": "My main concern is the generalizability of the proposed methods which are carefully designed for 4DGS family models. According to the addtional experiments conducted by the authors, the proposed methods are very effective on this models. Although the generalizability might still be an unignorable limitation, the proposed methods are probably useful for the development of 4DGS family models. In general, I decide to raise my rating to boardline accept.", "rebuttal": "Thank you for your positive feedback and insightful comments! Below, we respond to address your concern. We welcome further discussion to enhance the clarity and effectiveness of our work.\n> **`>>> W1:`**\n> **Limitations on the application in much higher-performance models, such as Deformable-GS, SC-GS and Grid4D.**\n---\n\n**`>>> A1:`** We acknowledge that our method was initially designed for 4DGS; however, we argue that our model still demonstrates strong generalizability. While our method is not directly applicable to deformation-based family, it is designed to be **compatible with the entire 4DGS family**. For example, our method is compatible with other works like Temporal Gaussian Hierarchy[1] and FreeTimeGS[2].\n\n[1] Representing Long Volumetric Video with Temporal Gaussian Hierarchy.\n\n[2] FreeTimeGS: Free Gaussian Primitives at Anytime and Anywhere for Dynamic Scene Reconstruction.\n\n>**`>>> W2:`**\n> **Rendering quality degradation on the D-NeRF dataset.**\n---\n\n**A2:** There is **no degradation** in rendering quality on the D-NeRF datasets. While the results from the original 4DGS [1] are higher than ours, those were obtained under different devices and settings. For a fair comparison, we retrained the official 4DGS code on the D-NeRF dataset using the same protocol. As shown in Table 2, retrained 4DGS achieves 32.99 dB, while our method reaches 33.37 dB. Thus, there is no degradation. \n\n> **`>>> Q1:`**\n> **Baselines such as SC-GS and Grid4D on the D-NeRF dataset.**\n---\n\n**`>>> A3:`** Thanks for mentioning these excellent works. We compare our method with these methods on the D-NeRF dataset. This table will be appended to Tab.2 in the main paper to provide a more comprehensive comparison. While our method exhibits a reduction in rendering quality compared with these models, it achieves significantly accelerated rendering performance and minimized storage requirements, underscoring its efficiency advantages. \n\n|            Method            | PSNR&nbsp;$\\uparrow$ | FPS&nbsp;$\\uparrow$ | Storage(MB)&nbsp;$\\downarrow$ |\n|:---------------------------:|:--------------------:|:-------------------:|:--------------------------:|\n| SC-GS[1]                     |    **40.65**        |     126                |              28              |\n| Grid4D[3]                     |        39.91     |      166               |                    93        |\n| 4DGS                     |      32.99                |    376                 |          278                 |\n| Ours                     |      33.37                |    **1462**                 |          **7**                 |\n\n\n> **`>>> Q2:`**\n> **Lack of training speed evaluation. Will the pruning and filtering processes take a lot of time?**\n---\n**`>>> A2:`** No. On the N3V dataset, pruning and filtering processes take nearly 10 minutes. Moreover, 4DGS-1K only takes approximately 30 minutes to fine-tune. This result is provided in Line 773 in the Appendices.\n\n> **`>>> Q3:`**\n> **Why not use the $\\left|\\Sigma_t\\right|$ rather than the second derivative for the temporal score?**\n---\n\n**`>>> A3:`** We appreciate your valuable insight. Directly using  $\\left|\\Sigma_t\\right|$ for pruning results in a reduction in rendering quality. This has been discussed in the Appendix(See Tab.4, panels (e)). Using $\\left|\\Sigma_t\\right|$ yields a PSNR of 33.47 dB on the *Sear Steak* scene, compared to 33.60 dB for vanilla 4DGS. The PSNR achieved by our method is 33.76 dB. This is because $\\left|\\Sigma_t\\right|$ follows a long-tailed distribution, with its values extending over the entire set of real numbers. As a result, this score disproportionately highlights a minority of large $\\left|\\Sigma_t\\right|$, and diminishes the discriminative capacity of Gaussians with smaller $\\left|\\Sigma_t\\right|$."}, {"review_id": "wNdffgI5Ej", "reviewer": "Reviewer_CKCJ", "summary": "The paper introduces 4DGS-1K, a method to address the inefficiencies of 4D Gaussian Splatting (4DGS) in dynamic scene rendering. The authors identify two key redundancies in 4DGS: (1) Short-Lifespan Gaussians, which are transient and require excessive storage, and (2) Inactive Gaussians, which are processed during rendering but contribute little to the output.", "questions": "1. The 1000 FPS claim is striking but requires scrutiny: The \"raster FPS\" (1092) measures only the rasterization step, while the end-to-end FPS (805) is lower. This discrepancy should be clarified. Is the 1000 FPS achievable in practice, or is it a theoretical peak?\n2. The experiments use an RTX 3090, a high-end GPU. Performance on mid-range hardware or edge devices is untested, which could limit real-world applicability.\n3. The Spatial-Temporal Variation Score (Eq. 7) combines spatial and temporal terms heuristically. While intuitive, the paper lacks theoretical justification for the aggregation method (e.g., why multiplicative instead of additive?).\n4. The pruning threshold (80–85%) is empirical. A sensitivity analysis (e.g., how performance degrades with stricter pruning) would strengthen the method’s robustness.", "limitations": "Yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths：\n1. The Spatial-Temporal Variation Score is a novel criterion for pruning Gaussians, combining spatial contribution and temporal stability. \n2. The Temporal Filter leverages the observation that active Gaussians overlap across adjacent frames, enabling efficient masking.\n\nWeakness：\nThe paper introduces meaningful innovations and demonstrates empirical results, but the overstated FPS claim and minor theoretical gaps warrant revisions.", "was_revised": true, "final_justification": "Thank you for addressing my concerns. While I have no additional issues to raise, I agree with the other reviewer that the generalizability requires stronger justification. I therefore maintain my score.", "rebuttal": "We sincerely appreciate the thorough review and insightful feedback provided by each reviewer. The reviewers asked perceptive questions and comments, which are answered in detail in individual responses and have improved our submission.\n> **`>>> Q1:`**\n> **The discrepancy between FPS and raster FPS should be clarified.**\n---\n**`>>> A1:`** We truly appreciate the reviewer for this excellent question, as it, in fact, touches upon a key aspect of our results. \n\nThe two metrics, \"raster FPS\" and \"end-to-end FPS\", are presented to **serve complementary purposes**.\n\n- The \"raster FPS\" (1092 FPS) is reported because it *most directly measures our contribution*: pruning and filtering the number of Gaussians. By isolating the rasterization step, this metric clearly demonstrates the efficiency of our method.\n- The \"end-to-end FPS\" (805 FPS) represents the *practical rendering speed*. It provides a holistic view by including all stages of the rendering module.\nBoth are important, as we aim to demonstrate that we improve the rasterization speed, and consequently, the end-to-end (E2E) speed is also enhanced.\n\n> **`>>> Q2:`**\n> **Performance on mid-range hardware or edge devices.**\n---\n\n**`>>> A2:`** Thanks for your interest. Our method maintains high rendering speed even on devices with limited computational resources. We have discussed this issue in the Appendix (Section A.2, Lines 778–780). We implement our model on the NVIDIA TITAN X GPU(released in 2015). It still achieves over **200 FPS** on the N3V dataset, significantly outperforming the vanilla 4DGS (20 FPS). It is worth noting that this rendering speed is nearly on par with the performance of other baselines running on an RTX 3090.\n\n> **`>>> Q3:`**\n> **A theoretical justification for the aggregation method (e.g., why multiplicative instead of additive?).**\n---\n**`>>> A3:`** Thanks for your insightful suggestions. Each 4D Gaussian can be decomposed into a conditional 3D spatial Gaussian and a marginal 1D temporal Gaussian, as demonstrated in 4DGS[1], where $p(x, y, z, t) = p(x, y, z|t)p(t)$. This perspective inspires us to model the spatial and temporal terms as a product. Furthermore, we also report the 'additive' results on the N3V dataset following the default experiment setting. As shown in this table, the product formulation yields higher PSNR (31.92 vs. 31.55) and SSIM (0.9462 vs. 0.9416), and achieves a lower LPIPS score (0.0513 vs. 0.0611). This experiment will be appended to the main paper to support our design of the score.\n\n|   Method   | PSNR$\\uparrow$ | SSIM$\\uparrow$ | LPIPS$\\downarrow$ |\n|:----------:|:--------------:|:--------------:|:------------------:|\n|   Add      |     31.55      |     0.9416     |      0.0611        |\n| Product    |  **31.92**     |  **0.9462**    |   **0.0513**       |\n\n\n[1]Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting\n\n> **`>>> Q4:`**\n> **A sensitivity analysis of pruning threshold.**\n---\n**`>>> A4:`** The sensitivity analysis is provided in Fig.9 in the Appendices. We analyze the impact of varying pruning ratios on rendering quality. It demonstrates that our spatial-temporal variation score-based pruning can even improve scene rendering quality when the pruning ratio is relatively low. Moreover, it can maintain results comparable to the vanilla 4DGS at higher thresholds."}]

Author rebuttal:

Meta-review: In this work, the authors focus on 4DGS, introducing two major elements helping in the rendering speed enhancement: a pruning criterion that removes short-lifespan Gaussians, and a mask for active Gaussians across consecutive frames. The approach is overall simple and, in the tested benchmark, very effective.

Although some of the reviewers have raised concerns about the generalizability of the proposed approach (which should be indicated more explicitly in the paper), the consensus is positive towards the paper. The authors need to incorporate all the extra elements provided in the discussion with the reviewers into the paper.
Decision: Accept (poster)