From Tokens to Nodes: Semantic-Guided Motion Control for Dynamic 3D Gaussian Splatting
Authors: Jianing Chen
Zehao Li
Yujun Cai
Hao Jiang
Shuqin Gao
Honglong Zhao
Tianlu Mao
Yucheng Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: 3d reconstruction
computer vision
monocular dynamic reconstruction
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We present a motion-adaptive 3D Gaussian Splatting framework that reallocates control points with semantic and motion priors, achieving smoother trajectories and superior dynamic reconstruction.

Abstract: Dynamic 3D reconstruction from monocular videos remains difficult due to the ambiguity  inferring 3D motion from limited views and computational demands of modeling temporally varying scenes. While recent sparse control methods alleviate computation by reducing millions of Gaussians to thousands of control points, they suffer from a critical limitation: they allocate points purely by geometry, leading to static redundancy and dynamic insufficiency. We propose a motion-adaptive framework that aligns control density with motion complexity. Leveraging semantic and motion priors from vision foundation models, we establish patch-token-node correspondences and apply motion-adaptive compression to concentrate control points in dynamic regions while suppressing redundancy in static backgrounds. Our approach achieves flexible representational density adaptation through iterative voxelization and motion tendency scoring, directly addressing the fundamental mismatch between control point allocation and motion complexity. To capture temporal evolution, we introduce spline-based trajectory parameterization initialized by 2D tracklets, replacing MLP-based deformation fields to achieve smoother motion representation and more stable optimization. Extensive experiments demonstrate significant improvements in reconstruction quality and efficiency over existing state-of-the-art methods.

Published as a conference paper at ICLR 2026
FROM TOKENS TO NODES: SEMANTIC-GUIDED Mo- TION CONTROL FOR DYNAMIC 3D GAUSSIAN SPLAT- TING
Jianing Chen 1:; Zehao Li Yujun Cai², Hao Shuqin Gaok Honglong Zhao', Tianlu Mao Yucheng Zhang" 1.21
'Institute of Computing Technology, Chinese Academy of Sciences, ICT University Chinese Academy of Sciences, UCAS
The University of Queensland [chenjianingg.33 aning,3s,
acc..c.ccm
ABSTRACT
Dynamic 3D reconstruction from monocular videos remains difficult due to the ambiguity inferring 3D motion from limited views and computational demands of modeling temporally varying scenes. While recent sparse control methods al- leviate computation by reducing millions of Gaussians to thousands of control points, they suffer from critical limitation: they allocate points purely by ge- ometry, leading to static redundancy and dynamic insufficiency. We propose a motion-adaptive framework that aligns control density with motion complexity, Leveraging semantic and motion priors from vision foundation models, we estab- lish patch-token- -ode correspondences and apply motion adaptive compression to concentrate control points in dynamic regions while suppressing redundancy in static backgrounds. Our approach achieves flexible representational density adap- tation through iterative voxelization and motion tendency scoring, directly ad- dressing the fundamental mismatch between control point allocation and motion complexity. To capture temporal evolution, introduce spline-based trajectory parameterization initialized by 2D tracklets, replacing MLP-based deformation fields to achieve smoother motion representation and more stable optimization. Extensive experiments demonstrate significant improvements in reconstruction quality and efficiency existing state-of-the-art methods.
motion-adaptiv
INTRODUCTION
Dynamic 3D reconstruction from monocular videos is critical for virtual reality, autonomous sys- tems, and content creation. The task requires capturing complex object motions and deformations from limited viewpoints while maintaining real-time rendering performance. This remains chal- lenging due to the fundamental ambiguity of inferring 3D motion from 2D observations and the computational demands of modeling temporally varying scenes.
Recent ad advances in 3D Gaussian Splatting (Kerbl et al. (2023)) have enabled efficient static scene re- construction through explicit primitive representations and last rasterization. Extensions dynamic scenes follow two approaches: dense methods that parameterize each Gaussian's 's temporal evolu- tion, achieving high quality but poor scalability, and sparse control methods that use small set of control points to govern scene deformation. Sparse approaches like SC-GS (Huang al. (2023), SP-GS Wan (2024)) and 4D-Scaffold (Cho et al. (2025) ) offer significant computational sav- ings by reducing the optimization space from hundreds of thousands of Gaussians to thousands of control points.
However, existing sparse methods suffer from fundamental limitation: they allocate control points based purely on geometric considerations. Methods typically use Farthest Point Sampling (FPS)
"Equal contribution
Corresponning authors

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Published as a conference paper at ICLR 2026
(Huang al. (2023) Diwen Wan (2024): Chen et al. (2023) voxel centers (Cho al. (2025) Kong et al. (2025) to ensure uniform spatial coverage, but this geometric uniformity does not align with motion complexity. Real scenes exhibit highly non-uniform motion where static backgrounds dominate spatial extent while dynamic objects occupy smaller regions but require detailed motion modeling. This mismatch leads to static redundancy yet dynamic insufficiency, where control points are wasted on static regions while dynamic areas remain under-represented.
We address this through motion-adaptive control point allocation guided by vision foundation mod- els. Our approach is built on the insight that semantic understanding can predict motion patterns: certain object categories exhibit predictable motion behaviors that can be learned from large-scale video datasets. We leverage pre-trained vision foundation models to extract semantic tokens from image patches and establish patch-token-nowt correspondence, enabling direct transfer of 2D se- mantic priors to 3D control point placement.
Our method operates in three stages. First, we generate candidate nodes by back-pre nack-projecting image patches into 3D space using estimated depth and camera poses, with each node retaining its se- mantic token as a descriptor. Second, we apply motion-adaptive compression that iteratively merges nodes based on semantic similarity and motion tendency scores derived from vision foundation mod- els. This compression concentrates control points in dynamic regions while reducing redundancy in static areas, directly addressing the static-dynamic resource allocation mismatch. Third, pa- rameterize node trajectories using cubic splines rather than MIPs, initialized from 2D tracklets to provide stable motion guidance during optimization. This spline formulation offers several advan- tages. It ensures temporal smoothness, reduces optimization complexity by decoupling trajectory learning from other parameters, and provides a compact representation that scales better than dense deformation fields.
In summary, our main contributions are:
We propose a mntion--daaptive node initialization method using semantic and motion priors from vision foundation models to align control density with motion complexity.
We introduce a spline-based parameterization of node trajectories, which provides com- pact, smooth, and differentiable motion basis for the entire dynamic scene.
We present a complete optimization framework demonstrating superior reconstruction quality and efficiency over existing methods.
RELATED WORK
Dynamic NeRF Neural Radiance Fields (NeRF) (Mildenhall et al. (2020)) pioneered static view synthesis via implicit volumetric MLPs. Subsequent works (Guo et al. (2023); Gafni et al. (2021); Park et al. (202 a by; Pumarola al. (202T: Fang et al. (2022) Wang et al. (2023) extended NeRF to dynamic scenes with temporal structures such as deformation fields and canonical mappings, but remain inefficient due to dense ray sampling and costly volume rendering. To improve efficiency, recent methods introduce grid-based representations (Liu et al. (2022)) and multí-view supervi- sion (Lin et al. (2022) 2023 ). while explicit representations such multi-plane (Chen et al. (2022); Fridovich-Kaa Keil et al. (2023b); b. [Shoo et al. (2023)) and grid-plam hybrids (Song et al. (2023) further accelerate training. Nonetheless, their rendering speed is still insufficient for real-time applications.
Dynamic Gaussian Splatting. 3D Gaussian Splatting (3DGS) (Kerbl al. (2023) enables real- time rendering with explicit point-based representations and shows potential for broader 3D tasks (Li al. (2024); Qu al. (2024); ; Cai al. (2019) 2020)). Recent works have extended 3DGS to dy- namic scenes by learning time-varying Gaussian transformations. Several approaches (Yang et al. (2024b): [i et al. (2025) ) adopt per-Gaussian deformation fields, but such designs often incur redun- dant computation and slow training. Later methods adopt compact structural representations, such as plane encodings or hash-based schemes (Wu et al. (2024); ); Xu et al. (0244). to improve defor- mation efficiency. Alternatively, sparse control points have been introduced (Huang al. (2023);
Diwen Wan 2024); Kong et al. (2025): Lei et al. (022)); Chen et al. 2025): Liang et al. (2025) as lightweight mechanism to govern Gaussian motion via interpolation, supporting both high-quality rendering and motion editing. Existing approaches differ how control points are initialized: SC-
GS, SP-GS, and HAIF-GS (Huang al. (2023): Diwen Wan (2024); Chen al. (2025) adopt FPS
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sampling to ensure uniform spatial coverage, while 4D-Scaffold and EDGS (Cho al. (2055) ) Kong et al. (2025) use voxelization, which proves suboptimal in real-world scenes dominated by static backgrounds. More recent methods, such MoSca and HiMoR (Lei et al. (023); Liang et al. (2055), ) leverage 2D tracklets from vision foundation models, but they remain sensitive to tracking errors and struggle with large topological variations. Despite these advances, sparse control meth- ods still fail to adapt control density to motion complexity, often resulting in static redundancy and dynamic insufficiency. To address this, we propose motion-adaptive 3DGS framework that re- allocates control points according to motion cues and further stabilizes trajectory learning through spline parameterization.
PRELIMINARY: 3D GAUSSIAN SPLATTING
3D Gaussian Splatting (3DGS) (Kerbl et al. 2023 models static scene anisotropic 3D Gaus- sians, each parameterized by center μ E R. covariance Σ E 33x3 opacity a E (0, 1), and spherical harmonics (SH) coefficients с e 33(++1)² for view-dependent color, denoted as G(J,, Σ, a, c).
Each Gaussian is projected the image plane through the camera projection, forming 2D Gaussian that contributes to pixel colors. The 2D Gaussians are sorted by depth and rendered via an a- blending scheme. The color pixel p is obtained by compositing the contributions N ordered Gaussians overlapping the pixel:
C(p) = - Cio; II og), IEN
(1)
j=1
where C; the color of the i-th Gaussian and a, its image-space density determined by the projected covariance. The parameters are optimized with photometric reconstruction loss, and adaptive density control dynamically prunes or spawns Gaussians to improve efficiency and fidelity.
Extending 3DGS to dynamic scenes is commonly formalized by endowing the representation with explicit temporal parameterization instead of a purely canonical configuration. Following prior work (Liang et al. (2025); Wang et al.) (2024)), we introduce a temporal transformation that maps each Gaussian from the canonical space its state at frame written as T, [R, t,] SF(3). Applying T, to a canonical Gaussian G(Jo: Eo, c) yields its time-varying form G, = G(T,Po, R, a, c), which provides compact parameterization dynamic scenes.
METHOD
4.1 OVERVIEW
Given a monocular image sequence (T+). our goal is to reconstruct a dynamic 3DGS representation that enables temporally consistent, photorealistic novel-view renderings. The central challenge lies in the spatially non-uniform motion complexity and the need for smooth, stable trajectories under sparse supervision. To address this, we adopt sparse node-based deformation representation that controls canonical Gaussians (Sec. 4.2) through motion motion-adaptive allocation. we first initialize nodes from image patches and leverage semantic and motion cues from vision foundation models com- press redundant nodes in static regions while preserving those in dynamic regions (Sec. 4.3). We then parameterize node trajectories with spline to provide a compact, smooth, and differentiable motion basis, initialized from 2D tracklets for stable early-stage optimization (Sec. [.4). Finally, we propagate node transforms to Gaussians through dual quaternion blending and jointly optimize geometry, appearance, and motion with multi-view photometric and motion-consistency constraints (Sec. 4.5). Figure summarizes our pipeline, which integrates motion-adee aadiionad compression with iterative voxelization flexibly adapt representational density according to motion complexity.
4.2 NODE-BASED DEFORMATION REPRESENTATION
Modeling deformations in dynamic Gaussian scenes requires balancing expressiveness with tractability. Direct per-primitive formulations are prohibitively high-dimensional, while real-world motion often exhibits low-rank structure dominated by rigid and smooth patterns. This motivates a compact node-based representation. where each node carries SE(3) trajectory and RBF kernel defining its spatial influence. Gaussians inherit motion from their K nearest nodes through weighted aggregation, forming an efficient basis for subsequent initialization and trajectory modeling.
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Adtist- Nede Inttolizztion
Bock- ansjectce
Gedlerty
Fatckes
(1 lnity Node Linling
2 Toperhi Node
131 Compeesiien soct
Mon en Adephee
(4) Fsundation Shoge
(c) Sp Sgire--Paramtteriid Node Trojactories
(3) Gouzzion- Node Deformation
( Optinization
Zmg Marsoler Video
Space
Pust
Xt Motier Collitecy
Mi
Smu
Raac struchal Cirtia
Ex Time
Key friees
Traskitt
Mosk
Trajetiry of Node Mi
Oene and Boets
Deforee Erers
Enwdered
Brown
Figure 1. The overview of our method. (A) Given a monocular video, we extract semantic and motion priors from pre-trained vision foundation models. (B) These priors guide motion-adaptive node initialization, yielding compact distributions aligned with dynamic regions. (C) The initialized nodes assigned spline- parameterized trajectories provide a motion basis. (D) Node motions are propagated Gaussians through deformation, transforming the canonical representation. (E) The deformed model is rendered and optimized for consistent reconstruction.
Node Representation. We introduce sparse set of nodes N = ( to capture the dominant smooth motion patterns of the scene, where the number of nodes N, is significantly smaller than the number of Gaussian primitives Ng. Each node is formally defined as
N (t).)).
(2)
where T((() E SE(3) denotes the trajectory of NG across time, and P. E R+ specifies the radius of its radial basis function (RBF), which determines the spatial extent of its influence. Thus, (t) governs rigid motion over time, while p; determines the spatial scope of influence. This node for- mulation further supports motion-adaptive initialization, allowing dynamic regions to be modeled with higher fidelity (Sec. 4.3). h.. To ensure smooth and compact temporal modeling, each trajectory is parameterized by splines (Sec. 4.4).
Gaussian-to-Node Binding and Deformation. We derive the rigid transformation of each Gaus- sian primitive Gj at any query time by leveraging the trajectories of its neighboring nodes. Given the node set A (N i- each Gaussian Gj is associated with neighborhood of K nodes, de- noted V(G,) C N. The binding weight of node N; to Gaussian Gj is defined as
|x; c:
exp
2pp
Wij Σ EkEV(G,) exp
x,
2nh
where X; is the canonical center of Gaussian Gj, C₁ the canonical center of node Ng. These normalized weights interpolation coefficients in the blending stage.
To propagate node motion to Gaussians, we construct dense deformation field that interpolates per- ver-Gaussian rigid motions from sparse node trajectories. Following prior work (Lei et al. (2025 ).
we instantiate this field with Dual Quaternion Blending (DQB) (Kavan et al. (2007). which provides better interpolation quality. Concretely, for node N,, its SE transform at time written as
T,(t)= = (t) (t)). Its dual quatermion representation Q (t) E DQ is constructed as (1) p(()) (r),
(1) (1) + (a,)()),
(4)
where (t) is the unit quaternion corresponding to Rt(t), (t()) is the pure quaternion of the translation vector t, (t), and is the dual unit with ?2
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The blended transformation for Gaussian Gj is obtained by normalizing the weighted sum of neigh- boring nodes' dual quaternions and mapping the result back to SE(3):
(t) ΣiEvia,) Wiy Qi(1) Eievic,) mhy Q:00
(t(t) DQ2SE3 (Q)
(5)
Here normalization guarantees that (t) remains a unit dual quatemiio, while DQ2SE3()) denotes the standard conversion from a unit dual quaternion to rigid transform. This formulation enables Gaussian motion to be obtained through weighted blending of neighboring node trajectories, ensur- ing physical consistency and temporal smoothness.
4.3
MOTION-ADAPTIV NODE INITIALIZATION
Building upon the node representation in Sec. 4.2 we now address how to initialize nodes in way that adapts to motion complexity. Uniform sampling tends to oversample static backgrounds while failing to capture sufficient detail in dynamic regions, resulting in biased motion modeling. To overcome this imbalance, we introduce a semantic-guided, motion-adaptive initialization that allocates more nodes to dynamic areas while reducing redundancy elsewhere. Given calibrated keyframes with depth and semantics, this procedure generates a compact node set in canonical space that serves as the starting point for subsequent deformation modeling.
Patch-to-Node Generation. To better integrate semantic cues with geometry, we generate candi- date nodes directly from image patches rather than uniformly sampling point clouds or voxelizing 3D space. Specifically, we select a set keyframes {4, and divide each image into fixed-size patches {p}. A frozen vision foundation model provides token embedding 2t.p for each patch at frame 1, along with estimated depth maps. Each patch center Ut.p is back-projected into 3D space to obtain its coordinate Xt.-- The resulting collection { ((xtp; =t,p)} forms the initial candidate node set, where each node is anchored at the patch center and retains the semantic token as its descriptor. This preserves a patch-token-node correspondence that can be exploited during subsequent compression.
Dynamic Addtiin-Adapiive Node Compression. The candidate node set is still excessively large for direct modeling. necessitating a principled compression strategy. A naive voxelization with fixed resolution is insufficiently adaptive across regions and often mixes features of distinct objects. We therefore propose an iterative motion-adaptive compression that iteratively merges nodes while preserving fidelity in dynamic areas. Starting from small initial voxel size Unit. the voxel resolution is progressively enlarged during compression. In each iteration, bipartite soft matching (Huang et al.) (2025) is applied within every voxel. For each node in A, we connect to the most similar node in B, and the top r% pairs with the highest similarity are merged by retaining one representative node. After completing all voxels in the current iteration, the voxel size is enlarged by fixed step Av, and the process is repeated until the node count falls below target threshold.
To ensure that merging respects both appearance and geometry, we define a joint similarity between nodes N₁ E A and N₁ E B as
sim(N,,, = cos(z,, (z),,)))))))) 17 Mag (No,N)),
(6)
where cos(2;,2;) measures the token-based appearance similarity, and Ms,(N., E [J de- notes a foreground prior predicted by frozen VFM. Tokens from VFMs encode both semantic context and local appearance. Static regions yield consistent tokens across views, whereas motion causes variations that lower their similarity. Thus, cosine similarity serves as an effective cue dis- tinguish dynamic from static areas. The mask prior provides coarse localization of dynamic areas, discouraging premature merging regions with high dynamic likelihood.
However, simply applying a uniform compression ratio across all voxels fails to leverage this motion-aware similarity information effectively. Such uniform treatment leads to an unfavorable trade-off: a high ratio prematurely merges dynamic nodes during early fine-voxel stages, while low ratio fails to sufficiently reduce redundancy in static regions. To address this limitation, we propose an adaptive compression strategy that adjusts the compression ratio according to the motion tendency of each voxel cluster. Concretely, we define a dynamic tendency score Payn (C') for a cluster C by combining the mean foreground prior with the pairwise similarity within the cluster:
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Payn (C) =
Σ m(Ne) - -B. [Mel
E
simm Simm(N..N/))
Ucl
NzEue
(N..G))EMc
where Uc denotes the of nodes in cluster C, and MM the set of their matched pairs. This score is then used to modulate the compression ratio of each cluster:
r%(C) = mmin + (1 Paya (C)) (Fmmx Tminn,
(8)
so that static voxels with low Payn are merged aggressively with high 7%, while dynamic voxels with high Payn preserved with low r%.
In this way, compression reduces redundancy in static regions while maintaining sufficient node density in dynamic areas, striking balance between efficiency and temporal modeling fidelity.
4.4
NODE TRAJECTORIES
Given the motion-adaptive node set in the canonical space, the next challenge is to represent their temporal evolution. Directly optimizing node positions at every frame is unstable and computa- tionally expensive, as it lacks temporal regularization and entangles motion learning with Gaussian attribute updates. To achieve sparse yet stable control, parameterize each node trajectory with small set of keyframes connected by cubic splines. This spline-based formulation enforces smooth and differentiable trajectories, alleviates early-stage optimization difficulty, and provides reliable motion guidance for the associated Gaussian primitives.
Spline-Based Based Formulation. To obtain the motion each Node at arbitrary time steps, we repre- sent its trajectory with cubic Hermite spline (Park al. (2025); Ahlberg et al. (2016); Goodfellow et al. (2016)). Concretely, we select a set of keyframes [Tx k= along the timeline and assign learn- able positions {Pk 1K ===1 to the Node at these frames. The trajectory E(t) between two neighboring
keyframes (lx, (x+1) is then interpolated as
{(L) = hoo (7) Pk + h10(T) (4++1 (x) P& + hol(T) Pa+1 1 + +/m(7)) (l++1 - 1x) PA+1,
(9)
where T = E0+1 トー -
and the Hermite basis functions are
hoo(T) = 2T - 37 + 1, I(T((T = 27 + T, hol = 27 + +r?, hn1(F) = -2
(10)
This spline-based construction ensures temporal continuity by keeping both positions and first-order derivatives consistent across time. More importantly, it provides compact and differentiable repre- sentation that avoids the instability and heavy joint optimization associated with MLP-baser defor- mation fields, thereby offering stable guidance for the Gaussian primitives bound to these nodes.
Trajectory Initialization. To provide stable guidance at the early stage, we initialize the spline- parameterized node trajectories from scometry-consistengy instead of using random parameters. Concretely, we extract long-term 2D tracklets (Doersch al. (2023)) from sequence of frames, and unproject them into world coordinates using estimated depth (Piccinelli et al. (2024)) and camera poses. Formally, given a pixel coordinate u, on the 2D track at time with depth Di(,.), its world- space position is computed
Fz
R - T D.(u.)) T.T.. Tr
(11)
where T (.) denotes the back projeccoootionn from image to camera space with intrinsic K, and (R,, T,) are the estimated extrinsics. We then initialize the translational spline by fitting Hermite trajectory {(), over keyframes (f& 1K
k= to the 3D tracklets (Iz) } via least-squares optimization:
N,
min (P.)
Σ
ll x E(1)((())) 1l
(12)
A=
where {Pk} C R denote the learnable node positions the keyframes, and {(t) between (tx, tx+1) follows the cubic Hermite basis described previously. For the rotational component, we initialize Rode (t) Ia for all I, and defer its refinement to the joint optimization stage.

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Table
Quantitative comparison on Hyper-NeRF(vrig) dataset per-scene.
We highlight the best
second best and the third best results in each scene.
Method
129P5]
3D-Pricter
Hyper bustn
0.307
0.225
0.65
06668
D 30000S710 S7 Suctin
19.50
0.269
0.7000
20.71
0.655
0.277
22.77
0.640
0.363
25.05
0.533
0.155
22.36
0.655
0.374
4D0s W Maniiii 2i14
22.01
03365
01557
71.66
0.201
0.337
78.09
0.806
0.207
77.75
0.817
0.704
25.05
0.81
0.346
Motints 2irrett 2024
22:30
0.280
21.50
0.710
26.50
0.790
28.25
0.900
24.78
0643
0.195
MoDle
0.173
Crosi
50053
21:18
0.414
0.221
29.27
0.199
0.178
25.26
0.715
0.361
SC-68 Nisng 41 M35
18:66
0.269
0.505
15.79
0.613
0
21.85
0.666
0.289
25.40
0.806
0.215
21:30
0.576
0.311
S0-GPFMAN
19.95
0.284
11
61
0.657
0
13:20
0.684
0.230
76
0.811
0.307
27.66
0.611
0.296
Oars
32.17
0.221
0
23.55
0.723
0,233
29.66
0,863
0.161
78.55
0,019
28.25
0733
0.342
Table 2. Quantitative comparison on N3DV dataset per-scene. We highlight the best second best and the
third best results in each scene.
Method
Coffee Mantni PSNR
SSIM
PSNRT
Cook Spinach
SSIM
Cat Beef PSNIRT
SSIM-
Flae Salmon PSNR
SSIM
PSNRT
Hore Strok
SSIM)
PSNIRT
Sear Stook
SSIMI
PSNR
Meon
SSIMI
HecPlang Cao.8 Inbnun
2003
13:76
0.815
16.95
0.739
16.76
0.558
11:16
0.342
16.91
0.753
16.89
0.588
15:35
0.559
30G8 Fiozeria
19.25
17.20
0.739
18.48
0.752
0.510
19.55
400% Wa me Kidd
20.95
0.761
22.64
0.779
25.18
0.913
31.64
0.75%
21:01
0.787
25.38
0.328
22.10
0.765
SC Go Hunn d 2023
19.02
0.712
16.20
0.737
20.59
0.741
17.65
0.653
17.31
0.753
21.23
0.767
18.77
0.736
McDG
3025
21.37
22.40
0.782
21:35
0.878
23.63
Xu d.
21.32
22.58
0.788
23.51
21.04
0.815
23.14
0.306
22.51
Ours
22.55
0384
32.03
0.705
34.16
0.896
21.07
0.835
23.80
0.821
24.13
0.829
23.31
0.811
This geometry-driven initialization strategy grounds the spline trajectories in observed motion pat- terns, producing stable translational paths while preserving rotational flexibility, which facilitates more robust convergence during optimization.
4.5
OPTIMIZATION
To stabilize optimization under the monocular setting, we design a composite loss that integrates photometric, geometric, and motion-related constraints:
Liaill Augh Logh + Amass&Cmace + AdepthhSeephh + ArrackeLrrck + Jarap-Crapp
(13)
The photometric loss Cigh follows the standard practice in 3DGS (Kerbl al. (2023) ). encouraging rendered views to be consistent with the input images. The mask loss Laakk employs foreground masks predicted by an off-the-shelff segmentation model (Yang et al. (2023) as supervision sig- nals. The depth loss Ldepth leverages relative depth maps estimated from a monocular depth predic- tion model (Hu al. (2025)). aligned with sparse geometric priors improve structural accuracy. For motion supervision, the tracking loss Limck enforces temporal consistency by constraining the projected motion of rendered points against trajectories obtained from pre-trained 2D tracking model (Doersch et al. (2023)). Finally, the ARAP loss Camp (Huang et al. (2024); ); Lei et al. (2025) regularizes control point motion by penalizing non-rigid distortions in local neighborhoods, thereby ensuring locally rigid deformations and preventing unrealistic stretching. Detailed formulations of the above loss terms are provided in Appendix. A.2
5 EXPERIMENTS
5.1 EXPERIMENTAL SETUP
Datasets and Metrics. We evaluate our method on two real-world datasets: Hyper-NeRF (Park et al. (2021b) and Neural 3D Video (N3DV) (Li al. (2022). Hyper-NeRF dataset was captured using handheld rig equipped with two Pixel 3 cameras. We utilize data from one camera and conduct evaluations on the held-out views captured by the other. N3DV dataset consists of 18-20 synchronized cameras per scene, recording 10-30 second sequences. To conduct monocular exper- iments, we follow the experimental protocol of MoDGS (Qingming et al. (2025),, using cam0 for training and reporting evaluations cam5 and cam6. For quantitative evaluation, we employ three standard metrics: Peak Signal-to-Non to-ise Ratio (PSNR), Structural Similarity Index (SSIM) (Wang et al. (2004)), and Learned Perceptual Image Patch Similarity (IPIISS (Zhang et al. 2018)).
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Booom
Pesl
Primer
Chicken
Ours
GirdD
SC-G8
SC-GS-MANI
Grid41)
SC-GSHMANI
Figure 2. Qualitative comparison the Hyper-NeRF(vrigg dataset Park et al. other SOTA muu method reconstructs finer details of the moving objects.
2021b) ). Compared with
Cut Reef
Cook Spirach
GI
Ours
GridAD
4DGG
D-3DGS
Figure 3. Qualitative comparison the N3DV dataset Li al. 2022
Baselines and Implementation. We compare our method with state-of-the-art methods in dynamic scene reconstruction, including NeRF-based methods (TiNeu Vox (Fang et al. (2022) ) Hyper-NeRF (Park et al. (202Ib)). HexPlanes (Cao Johnson (2033)) and
methods (D-3DGS (Yang et al. (2024b)), 4DGS (Wu et al. (2024)), ED3DGS (Bae et al. (2024),Mola) MoDec- GS (Kwak et al. (2025)). ). Grid4D (Xu et al. (2024)), SC-GS (Huang al. (2023). MoDGS (Qing- ming et al. (2025) )) All implementations are based on Porch framework and trained on single
V100 GPU with 32 GB of VRAM. For more implementation details, please refer to Appendix A.2
5.2
COMPARISONS
Results on Hyper-NeRF. As shown in Table ! our method outperforms state-of-the-ee basclines across all scenes and evaluation metries. The qualitative results in Figure further illustrate that our approach captures scene dynamics with higher fidelity, producing more complete and detailed reconstructions moving objects. In addition, we augment SC-GS (Huang et al. (2023) with our Node Initialization (MANI), denoted as SC-GS+MANI. The last three rows of Table show that SC-GS+MANI achieves clear improvements over the original SC-GS, and this advantage is also visible in Figure for instance, in the Broom and Chicken scenes, SC-GS+MAN reconstructs dynamic regions more thoroughly with richer details, benefiting from the notion-aware initialization of control nodes. More results are available in Appendix A.4
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(a) Key components
(b) Node Init.
(c) Node Traj.
Method
PSNRI
SSIM1
PSNRT
SSIMT
LPIPS.
Method
PSNR1
SSIMT
LPIPS)
boseline
22.35
0.613
0.335
HPS
24.49
0.678
0,250
MI.P
23.95
0.317
-MANI
23.89
0.635
0.315
Vosel
24.06
0.652
0.271
Grid
24.28
-MS
34.51
0.688
0:278
Tracklet
24.83
0.681
0.253
Trackiet
24,59
0.67]
0.263
-MS twio
24.15
0.639
MANT
Lincar
23.15
0.500
0.384
25.78
(ours)
Table 3. Ablation studies on the Hyper-NeRR Park et al.
2021b dataset.
(a) PCD (b) Ours (before comp.) (c) Ours (wlo (ay)(C)) (d) (MANI)
(FPS)
(Voxel)
Figure 4. Visualization of different Node init. meth. on Chicken scene of Hyper-NeRF data (Pak al. 2021b).
Results on N3DV. Table the per-scene results the N3DV dataset. Under the monocular setting, our method achieves state-of-the-an ahtt performance with a mean PSNR of 23.31 dB. Figure 3 provides qualitative comparisons, where the highlighted red boxes show sharper and more coher- ent motion with fewer artifacts. For example, in fast hand motions, our method produces clearer contours and structures, while others yield blurry reconstructions. These improvements arise from placing more control points in notion-dominen areas and modeling their trajectories with spline parameterization, offering robust alternative implicit MLP deformation fields.
5.3 ABLATION STUDY
We conduct ablation studies on our method using the Hyper-NeRF (Park al. (202Tb) dataset, and summarize the results in Table 3 Figure 4 and Figure [) Our baseline follows a design similar to SC-GS (Huang et al. (2023) ). with more details provided in Appendix A.3
Motion-Adaptive Node Initialization (MANI). As shown in Table 3a introducing MANI on top of the baseline yields clear performance gains. Table 3bb further compares MANI with al- ternative initialization strategies (FPS (Huang et al. (2023)). voxel-based (Kong et al. (2025). tracklet-based (Liang et al. (2025)), confirming the superiority of our motion-adaptive design. Fig- ure visualizes the initialization. (a) shows the raw point cloud provided by the dataset, where COLMAP (Schonberger & Frahm (2066) fails to recover dynamic regions due to view inconsis- tency, causing static sampling to poorly cover moving areas.(b) shows our patch-to-node strategy yields better distribution, with red region indicating dynamic area in Chicken scene. (c,d) shows adding the dynamic tendency score Payn (C) (Eq. 7 further merges static redundancy and preserves dynamic details. (e.f) shows replacing our strategy with FPS or voxel-based initialization results in inferior performance.
Apline-Parameterizee Node Trajectories (MS). As shown in Table Ba] adding MS the baseline (row 3) yields significant performance gain, and initializing node splines with 2D tracklets from VFM models (row 4) further boosts the results. To validate its effective- ness, we replace MS with alternative deformation meth- ods, including an MLP (Yang et al. (2024b)), grid- based method (Wu et al. (2024)). and a tracklet-based method (Liang al. (2025)) Table (3cee3cch] reports the quanti- tative results. MLP and gridd approaches suffer from entangled optimization with large param- eter spaces, leading to suboptimal performance under sparse control nodes. Tracklet-based defor- mation benefits from motion priors and achieves better reconstruction, but its reliance on predicted trajectories and clustering introduces noise, resulting in less stable optimization. In addition, qual- itative results on the N3DV dataset (Figure (5) show that our method produces clearer and more complete reconstructions of dynamic regions.
MLP
Tracklet
Figure 5. Qualitative results of ablation.

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CONCLUSION
In this work, we introduced a aotion-adaptive framework for dynamic 3D Gaussian Splatting that addresses the imbalance between static redundancy and dynamic insufficiency in existing sparse control methods. By leveraging vision foundation model priors for node initialization, applying motion-aware compression adapt representational density, and employing spline-based based trajec- tory formulation for stable optimization, our approach achieves substantial improvements in recon- struction quality. Extensive experiments validate its superiority over prior state-of-the-ar methods, highlighting the effectiveness of aligning node allocation with motion complexity. Looking ahead, we helieve this framework opens the door incorporating stronger motion priors and handling more complex topologiaal variations in dynamic scenes.
7
ACKNOWLEDGEMENTS
This work in part supported by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No. XDA0450203, and the National Natural Science Foundation China under Grant 62172392, and the Innovation Research Program of ICT CAS (E261070).
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ETHICS STATEMENT
This work does not raise any ethical concerns. does not involve human subjects, personally identi- fiable information, sensitive data. No potentially harmful insights, methodologies, applications are introduced. The datasets and models used are publicly available and widely adopted in prior re- search. We have complied with all relevant ethical standards and the ICLR Code of Ethics.
REPRODUCIBILITY STATEMENT
We have made significant efforts to ensure the reproducibility of our work. The paper provides de- tailed descriptions of the proposed methodology in Section[4] Complete model architecture, training settings, experimental protocols, hyperparameters, and evaluation metries are documented in Sec- tion 5 and Appendix [A.2] All datasets used are publicly available and the preprocessing steps are described in the appendix.
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A APPENDIX
A.1 LLM USAGE
In this work, large language models (LL.Ms) were only used as a general- writing assist tool. Specifically, LLMss were employed for correcting grammatical errors and refining the language style of the manuseript. No part of the research ideation, methodology design, experiments, analysis, or results was generated by LLMs. The authors take full responsibility for the content of this paper.
A.2 ADDITIONAL TRAINING DETAILS
Loss functions and weights. We provide a detailed explanation for each term of the loss in Eq. [3 We employ two categories of loss functions supervise the learning of dynamic Gaussian primitives.
To ensure that rendered observations align with the input supervision signals, we impose per-frame reconstruction objectives on color, depth. and mask predictions. At each training iteration, given the camera parameters, we render an image ī, depth map D., and mask M, following Eq. 1 The RGB loss Ligh consists of weighted combination of mean squared error (MSE) between I, and Ie (weight 0.8), and a D-SSIM loss (Wang et al. (2004)) (weight 0.2). The depth loss Laepth computes the MSE between D; and the monocular depth prediction D; (Hu et al. (2025)), with a weight of 1.0. The mask loss Lmusk enforces consistency between M, and the foreground mask M, predicted by a segmentation model (Yang et al. (2023)), also weighted by 1.0.
To regularize temporal correspondences and guide the motion of Gaussians, we introduce tracking loss Lurck, composed of 2D trajectory term Linck-22d and a depth reprojection term Leach-depth- For randomly sampled query time t and target time E', the 2D trajectory loss Lunck+22 measures the MSE between the rendered trajectory ût-r"' and the tracked trajectory ut-rr' provided by pre- trained tracker (Doersch al. (2023)), under normalized pixel coordinates, with weight of 2.0. Meanwhile, the depth reprojection loss Luuth-deppth penalizes the discrepancy between the rendered reprojection depth de- and the metric-aligned depth Duu (Hu et al. (2025)), with weight of 0.1, ensuring physically plausible motion supervision.
To further constrain the motion of Gaussians and avoid degenerate deformations, we adopt an rigid-as-possible (ARAP) loss Larap inspired by physics-based shape regularization (Huang et al. 2024 Lei et al. (2025) ). Specifically, given two timesteps t and t' separated by fixed interval A,
the loss is formulated as
T N
Cara
A
IIP
())
Pt
(k)
P
[=1
AE
E(E
(14)
+ Xe
Q
(K)
P
(
Q P (
(k)
where ε() denotes the neighborhood of Gaussian 9j. P. (3) is the 3D position of Gj at time i, and (A()) is the local frame constructed around Gg. The first term encourages the pairwise distances between neighboring Gaussians to remain stable across timesteps, while the second term preserves the relative local coordinates under the corresponding local frames.
Dataset details. Hyper-NeRF (Park al. (2021b) provides dynamic scenes with continuous
viewpoints, where each timestamp exhibits complex topological deformations. We adopt four scenes from this dataset, training and rendering at a resolution of 960 x 640. In our setting, we employ the "vrig" subset, which was recorded with stereo cameras, using one camera's 's sequence for training and the other for validation. Neural 3D Video (Li et al. (2022) contains 15-20 multi-vier videos, each consisting of 300 frames. Total six scenes used to train and render at resolution of 1352 1014. For the Flame Salmon scene, we utilize the initial 300 frames from its 1000ffrmme sequence in our experiments. Following the experimental protocol of MoDGS (Qingming et al. (2025)). we
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Table 4. Additional ablation study on different Depth prior on Chiken seene of Hyper-NeRF dataset per-seene.
Method
PSNRT
SSIM
LPIPS|
DepthCrafter Hu et al (2025)
29.61
0.865
0.165
Deythnn Yang el al. 2024a)
29.43
0.848
0.164
Metric3D Yin Hu 2024
29.38
0.855
0.173
Table 5. Additional ablation study on different 2D Tracklets prior Chiken scene of Hyper-NeRF dataset per-scene.
Method
PSNR*
SSIMI
LPIPS,
TAPIR Doersch et al. (2023)
29.66
0.863
0.161
CoTracker Karacy et al. (2024)
29.20
0.839
0.182
SpatialTracker Tracker Xiao et al. 2024
29.47
0.857
0.169
camo for training and report evaluations on cam5 and cam6. We generate initial point clouds for each scene following 4DGS (Wu et al. (2024))
Implementation details. We use Adam (Kingma (2014h) to optimize our method and Gaussians in canonical space jointly. We fine-tune our optimization parameters by the configuration outlined in the 3DGS (Kerbl et al.) (2023)). Besides, the adaptive density control of Gaussians from original BDGS is also applied. The learning rates of mean, scale, rotation, opacity and color of Gaussian are 10 and 1 10 respectively.
set to 1.6 x 10 5 10
10 10:0001-00
We extract per-patch token embeddings for a set of keyframe images {T+) using VGGT (Wang et al. (225)). ) The model alternates frame-wise and global self-attention layers, enabling the tokens to encode both intra-frame semantics and inter-frame correspondence across all views. Consequently, the resulting representations accumulate temporal context: tokens in stationary background regions remain highly consistent across frames (higher inter-frame cosine similarity), whereas tokens as- sociated with moving foreground objects vary more strongly (lower similarity). We exploit this property by computing cross- cross-frame token similarities to obtain motion-awar cues that later guide initialization and regularization. To supply complementary priors and supervision signals, we adopt off-the-shed vision foundation models: Track-Anything (Yang et al. (2023) for foreground seg- mentation masks, DepthCrafter (Hu et al. (2025)) )) for temporally consistent monocular video depth, and TAPIR (Deersch et al. (2023) )) for dense 2D point trajectories. These components provide object masks, long-range consistent depth sequences, and per-point tracks, respectively, which we integrate into the training objectives and the construction of motion-aware priors.
A.3 ADDITIONAL ABLATIONS
Details of ablation setting. Our baseline implementation follows a design similar to SC- GS (Huang et al. (2023)) where Nodes are sampled from the input point cloud using farthest point sampling (FPS) and their motions are parameterized by an MLP. To model Gaussian dynamics, we replace the conventional linear blend skinning (LBS) (Sumner et al. (2007)) with deformation via quaternion-basee blending (DQB) (Kavan et al. 2007 ), which serves as the backbone deformation mechanism in all ablation settings.
Additional ablation study on VFM prior. We further evaluate the impact of different VFM- based depth estimation models on the Hyper-NeRF dataset, using the Chicken scene a repre- sentative case. Specifically, we compare DepthCrafter (Hu et al. (2025)). DepthAnything (Yang et al.] (2024a)), and Metric3D (Yin & Hu (2024)), as summarized in Table E The results indi- cate that DepthCrafter provides relatively more reliable results in our setting. Therefore, we adopt DepthCrafter as the depth prior in our framework. We further evaluate different 2D tracklet models on the same Chicken scene of the Hyper-NeRF dataset, comparing TAPIR (Doersch et al. (203 CoTracker (Karaev et al. (2024)). and SpatialTracker (Xiao et al. (2024)), as reported in Table 5 TAPIR integrates more smoothly into our pipeline and yields more reliable tracklets under the dy- namic scenes consider. Consequently, employ TAPIR our default tracking module.
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Ours
w/o init
Figure 6, Additional ablation study on rotational trajectory initialization JumpingJacks the D-NeRF dataset (Pumarola et (202T)
Table Ablation study
VFM prior loss.
Method
PSNR
SSIM
LPIPS.J
Linsck
25.46
0.691
0,259
w/o Ldepth
24.97
0.674
0,277
w/o Lunck
25.52
0.690
0.253
Ours
25.78
0.722
0.242
Additional ablation study on rotational trajectory initialization. Hyper-NeRF (Park et al. (2021b) already includes several scenes with noticeable rotational motion, such as Banana and Chicken, where our trajectory initialization applied without any modification. To further isolate articulated rotations, we additionally evaluate the proposed method D-NeRF (Pumarola et al. (202I) scene (JumpingJacks) that exhibits strong joint rotation, using exactly the same loss weights the main experiments. As illustrated in Figure 6 the variant with trajectory initialization consistently produces sharper reconstructions and improved quantitative metrics, while the model trained without initialization still converges to reasonable solution. We do not observe slower convergence or the need to increase regularization strength in any of these settings, which suggests that the proposed initialization remains stable and effective rotational motion.
Additional ablation study VFM prior loss. To assess the sensitivity to VFM based super- vision, we ablate the loss terms associated with masks, depth and tracking. As shown in Table 6 removing each term leads only moderate drops in PSNR, SSIM and LPIPS, and the optimization remains stable. These results indicate that the framework does not rely on any single VFM prior. When the depth and tracking cues are weakened, the system effectively behaves as a sparse control dynamic 3DGS that is mainly driven by RGB reconstruction and geometric regularizers rather than being dominated by potentially erroneous VFM signals.
Additional ablation study on hyperparameters. The framework involves several hyperparame- ters, but many of them are either learned or defined in an adaptive way. The RBF radius is optimized jointly with other parameters, and the per voxel compression ratio r%(C) is computed from the dynamic tendency of each voxel, so neither requires manual tuning. The MANI weights (a, 3, n) only balance semantic similarity and foreground priors and are kept fixed for all scenes. The com- pression bounds (rmin Tmax) and the spline keyframe interval N are selected once on a validation scene and reused all experiments. Table ?? report ablations on (Tmin- Tmaz), 77. (a, 3) and N on the Chicken and 3D-Printed scenes from Hyper-NeRE. NeRF. Performance varies smoothly within broad range of values and the default configuration lies close to the optimum, indicating that the method not overly sensitive and does require per-scene retuning.
18

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Published as a conference paper at ICLR 2026
Table 7. Ablation study on (rmin, ('ma)) ) combinations.
Method
PSNRT
SSIMT
LPIPS..
25, 50]
25.87
0.774
0.199
25, 75
26.10
0.796
0.196
[50, 75
26.03
0.790
0.196
Table 8. Ablation study on
Method
PSNR
SSIM
LPIPS,
0
25.15
0.713
0.236
0,25
25.81
0.768
0,215
0,5
26.10
0.796
0.196
0.75
25.95
0.785
0,209
A.4 ADDITIONAL RESULTS
Efficiency comparison. We provide detailed quantitative results the Hyper-NeRF dataset in Table reporting per-scene PSNR, training time, and rendering speed (FPS). Furthermore, Ta- ble 12 presents comprehensive comparison of our method against representative NeRF-based and 3DGS based approaches, including PSNR, training time, rendering speed (FPS), and storage size (MB) at resolution 536 x 960. Specifically, the results Nerfies, HyperNeRF, TiNeu Vox-B, D-3DGS, and 4DGS are taken from (Wu et al. (2024), measured on an NVIDIA RTX 3090 GPU, while MoDec-GS is reported (Kwak et al. (225h) using an RTX A6000 GPU. Our method eval- uated on an NVIDIA V100 GPU. Due time constraints,
have not yet conducted performance benchmarking on the same hardware. Nevertheless, it is well established that the V100 provides lower computational throughput than both RTX 3090 and RTX A6000. Therefore, the favorable comparison results demonstrate the inherent efficiency and effectiveness of our approach despite the hardware disadvantage.
Additional quantitative comparison.
To further validate the effectiveness of our approach, we conduct additional experiments on the N3DV et. (2022) dataset under the multi-view setting and compare method against several state-of-the-art baselines, as reported Table 13 The results demonstrate that our method also achieves strong performance in the multi-view scenario. To further assess performance under high motion scenarios, we additionally evaluate on the Nvidia dataset (Yoon et al. (2020)) As reported in Table 14, the proposed method achieves the best mean PSNR across all four sequences and attains the best or second best PSNR on each individual scene, outperforming representative dynamic 3D Gaussian baselines.
We conduct additional qualitative comparison on the Hyper-NeRF dataset (Park et al. (2021b)), comparing our method with 4DGS (Wu et al. (2024)), Grid4D (Xu et (2024)), and D-3DGS (Yagg al. (2024b)) as shown in Figure and Figure
Additional qualitative comparison.
19

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Published as a conference paper at ICLR 2026
Table 9. Ablation study on (a,
Method
PSNR1
SSIM
I.PPS.
0.25, 0.75
25.86
0.778
0.199
0.5, 0.5]
26.05
0.790
0.197
0.755, 0.25 [1.0]
26.10
0.796
0.196
26.03
0.788
0.197
Table 10. Ablation study on keyframe interval N.
Method
PSNRT
SSIMT
LPIPS]
FPSt
2
25.94
0.782
0.215
101
6
26.14
0.814
0.193
97
8
26.10
0.796
0.196
90
12
25.88
0.776
0.224
86
Table 11. The training times and rendering speed on Hyper-NeRF datasct per-scene.
Scene
Broom
3D-Printer
Chicken
Banana
Mean
PSNRT
22.37
22.53
29.66
28.55
25.78
Training Times (m) J
48
37
30
41
39
FPS1
61
92
88
37
69.5
Table 12.
Efficiency comparison on Hyper-NeRF dataset.
We highlight the best
second best and the
third best results in each scene.
Methods
PSNR*
Training Times
FPSt
Storage(MBB)
Nerfies Park al. (202 la)
22.2
h
<1
HyperNeRIID Park et al.
(222bb
22.4
32h
TiNeu Vox- Fang el al
2022
24.3
30m
48
D-3DGS Yang el al. (2024b)
19.7
40m
4DGS Wu el al. (024)
25.2
30m
34
MoDec-GS Kwak el a.
22055
25
1.2h
23.8
28
Ours
25.8
39m
70
25
Table 13. Additional quantitative comparison on N3DV dataset per-scene.
We highlight the best
second best and the third best results in each scene.
Coffee Mariai
Spitach
PSNR
SSIM
PSNRT
S53M
PSNR
SS1M
PSNRT
SS3M1
PSNR
SSIM
PSNR
SSIM
PSNR
SS1M
K-Mona Prabsirh-Kll
0023
29.99
0/043
31.60
0.968
31.82
0.663
30.44
0.902
11.39
0.070
31.52
0.971
31.55
0.0m0
Hyperfoc
26.37
0.592
32.30
0.941
32.92
0.945
25.36
0.882
32.20
0.949
32.57
0.962
31.10
0.927
Wa
29.10
31.95
Grdd Ku
28.34
0.938
32.44
0.911
33.25
0.974
25.59
0.947
32.20
0.990
15
0.974
31.3%
0.66
Ours
79.31
0.990
13.95
0.963
33.91
0.981
30.35
0.951
33.87
0.987
33.82
0.981
37.38
0.971
Table 14. Additional quantitative comparison Nvidia Yoon 2020 dataset.
Method
Balloon
Balloon2
Jumping
Umbrella
Mean
Deformable 3DGS Yang al. 2024b)
15.91
15.13
16.68
17.26
16.25
4DGS Wu et al. (2024)
21.89
24.85
22.37
22.36
22.87
HiMok Liang
Liang et al (2025)
23.90
23.48
20.04
24.30
22.93
MoSca
23.58
27.80
25.01
25.17
25.39
Ours
24.39
27.65
25.43
25.69
25.79
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Published as a conference paper at ICLR 2026
Peel Banana
3D Printer
GT
Ours
Gird4D
D-3DGS
4DGS
Figure Additional qualitative comparison on Peel Banana and 3D Printer scene in the HyperNeRF dataset (Park et al. (2021b)..
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Published as a conference paper at ICLR 2026
Chicken
Proon
GT
Ours
Gird4D
D-3DGS
4DGS
Figure 8. Additional qualitative comparison on Chicken and Broom scene in the HyperNeRF dataset PPark
et al.
(02
22


Reviews:
[{"review_id": "octKbRRP0R", "reviewer": "Reviewer_mnpE", "summary": "This paper proposes a motion-adaptive dynamic 3D reconstruction framework that allocates control points based on motion complexity rather than static geometry. By leveraging semantic and motion priors from vision foundation models and introducing spline-based trajectory parameterization, the proposed approach achieves smoother motion, higher reconstruction quality, and improved efficiency over prior approaches. Extensive experiments demonstrate the effectiveness.", "questions": "n/a", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Leveraging the  vision foundation models for 4D Gaussian Reconstruction seems interesting, though it is not novel.\n2. The experiments demonstrate the effectiveness based on two important datasets.\n3. Motion-Adaptive Node Initialization seems interesting.", "weaknesses": "1. The comparison is slightly old. I feel confused that the paper did not compare directly with [A] and [B].\n2. The whole pipeline is quite complex, and this paper did not provide any significant progress.\n3. These ideas all make sense, but they’re quite ordinary.\n\n[A] Mosca: Dynamic gaussian fusion from casual videos via 4d motion scaffolds\n[B] Himor: Monocular deformable gaussian reconstruction with hierarchical motion representation.", "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": "gILfDsMhU7", "reviewer": "Reviewer_bxh3", "summary": "1. This paper focuses on dynamic 3D reconstruction from monocular videos and proposes a sparse, node-based deformation framework.\n2. This paper proposes Motion-Adaptive Node Initialization and spline-parameterized node trajectories with cubic Hermite.\n3. Experiments on Hyper-NeRF and N3DV show improved quality and efficiency over previous methods.\n4. The idea seems to be novel and the objective experimental results are convincing.", "questions": "1. I am curious about the actual effectiveness of trajectory initialization. It fits splines only for translations while starting rotations from the identity. Does it show slow convergence or demand stronger regularization in cases with pronounced rotational or articulated motion?\n2. Despite incorporating multiple constraints (RGB, depth, masks,...), are there some scenarios may require stronger geometric priors or multi-view supplementation due to scale and occlusion ambiguities under monocular setups?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1. Motion-aware sparsity allocates more control nodes in motion-heavy regions via semantic/motion priors from vision foundation models, which makes sense and demonstrates a new direction for optimizing 3D/4D reconstruction.\n2. Spline-parameterized node trajectories replaces MLP deformation fields for smoother, more stable motion with cubic Hermite initialized from 2D tracklets.\n3. They provide enough ablations that isolate contributions of their framework.\n4. The objective experimental results are convincing enough to demonstrate the superiority of their method.", "weaknesses": "1. In my view, videos are necessary for papers in 4D reconstruction to show the effectiveness and superiority. If there is no videos, there is no enough subjective comparisons. Objective metrics and image/frame comparisons are not enough.\n2. This idea of 2D motion-guided deformation is not novel, and many papers have proved the effectiveness. There should be more visualizations and comparisons with such kinds of methods.\n3. The method depends heavily on VFM priors. What will happen when there is severe distortions or shifts in the VFM priors? As far as we know, VFM's priors are not that robust and stable so far.", "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": "Z7M9ba0eTe", "reviewer": "Reviewer_BzWF", "summary": "This paper addresses the problem of dynamic 3D reconstruction from monocular videos using 3D Gaussian Splatting. It identifies a key limitation in current sparse control methods: control points are often allocated based purely on geometry (e.g., uniform sampling), leading to redundancy in static regions and insufficient density in dynamic regions. To overcome this, the paper proposes a motion-adaptive framework. The core contributions are: (1) Motion-Adaptive Node Initialization (MANI), which leverages semantic and motion priors from Vision Foundation Models (VFMs) via patch-token correspondences to compress nodes adaptively, concentrating control density in dynamic areas (2) Spline-Parameterized Node Trajectories (MS), which replaces MLP-based deformation fields with cubic splines initialized by 2D tracklets, aiming for smoother motion and more stable optimization. Experiments on the Hyper-NeRF and N3DV datasets demonstrate improvements in reconstruction quality and efficiency over state-of-the-art methods.", "questions": "1.A key potential benefit of sparse control methods is enabling motion editing. Since MANI initializes nodes based on semantic tokens , do these nodes correspond to meaningful semantic parts? Have the authors explored manipulating these nodes for motion editing or control? Are there any demonstrations showing this capability?\n2.Compared to prior work like SC-GS , which primarily relied on geometric node placement, does the semantic nature of your nodes unlock more advanced or more intuitive editing possibilities? For example, could one edit the motion of specific semantic parts (like \"arm\" or \"leg\") by manipulating a group of associated nodes?\n3.Given the large number of hyperparameters identified in Weakness #1, how were they tuned for the experiments? Is there a recommended strategy for setting these parameters for new scenes? How robust is the performance to variations in key parameters like the MANI compression rates or the number of spline keyframes?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The paper accurately identifies and targets a fundamental limitation in existing sparse control methods for dynamic 3DGS – the mismatch between geometrically uniform control point allocation and non-uniform motion complexity. The concepts of static redundancy and dynamic insufficiency are well-articulated.\n2.The proposed MANI method is highly novel and intuitive. Using VFM tokens to guide an adaptive compression process that preserves nodes in dynamic regions while aggressively merging them in static ones is a strong contribution. The visualization in Figure 4 effectively demonstrates this capability.\n3.The method achieves state-of-the-art results on the Hyper-NeRF and N3DV datasets under the challenging monocular setting, outperforming numerous recent baselines quantitatively and qualitatively. The ablation studies effectively validate the contributions.", "weaknesses": "1.The proposed framework introduces a significant number of hyperparameters. These include parameters for node binding (K neighbors, RBF radius), the MANI process (patch size, initial voxel size, number of iterations, compression rates, similarity/dynamic score weights), spline parameterization (number and selection of keyframes K). Tuning such a large set of parameters can be challenging, raising concerns about the method's applicability and robustness across different types of scenes or datasets. The paper lacks an analysis of how sensitive the final performance is to the choice of these hyperparameters\n2.The method heavily relies on pre-trained VFMs for crucial inputs like semantic tokens, depth maps, segmentation masks, and 2D tracklets. Errors or inaccuracies from these upstream models (e.g., poor depth estimation in textureless regions, inaccurate tracking during occlusions, noisy segmentation) could propagate and significantly degrade the quality of node initialization (MANI) and motion supervision.", "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": "abdXDjSE2k", "reviewer": "Reviewer_JNHZ", "summary": "This paper proposes a semantic-guided motion-adaptive framework for dynamic 3D Gaussian Splatting (3DGS) reconstruction from monocular videos. The key idea is to use vision foundation models to extract semantic and motion priors that guide node allocation (“From Tokens to Nodes”) and introduce spline-based trajectory parameterization for node motion. The authors claim that this design aligns control-point density with motion complexity and yields smoother, more stable optimization. Experiments on Hyper-NeRF and N3DV datasets are presented, showing improved PSNR/SSIM compared to SC-GS, Grid4D, and other baselines.\n\nHowever, after close inspection, the technical novelty appears limited. Several core components replicate or closely follow prior works such as SC-GS, Superpoint-GS, H3D-DGS, and MoSca. The improvements demonstrated experimentally are modest and insufficiently supported by analysis or ablation. The paper is well written, but the contributions are overstated relative to their actual methodological advancement.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 1, "strengths": "1. **Clarity and organization** – The paper is clearly structured, with explicit method description, and comprehensive visuals (Figure 1–3). The pipeline is easy to follow.\n2. **Integration of semantic priors** – The idea of leveraging pretrained vision foundation models (VFM) for adaptive node placement is conceptually appealing and fits the current trend of combining large-scale priors with geometric reconstruction.\n3. **Empirical validation** – The paper reports quantitative and qualitative results on two widely used dynamic scene datasets (Hyper-NeRF, N3DV), demonstrating that the proposed pipeline functions in practice.", "weaknesses": "1. **Lack of genuine novelty** –  \n   - The *node-based deformation representation* (Sec. 4.2) is almost identical to prior works such as SC-GS [1], Superpoint-GS [2], and H3D-DGS [3]. The node weighting mechanism and dual quaternion interpolation are directly inherited from SC-GS and MoSca[4] with minimal modification.  \n   - The *image-to-space projection* in Sec. 4.3 follows the same design as H3D-DGS, where control points are back-projected from image patches using estimated depth. The only difference is the additional compression heuristic, which is conceptually similar to spatial clustering used in K-Means-based approaches (e.g., H3D-DGS achieving similar sparsity with 10% of control points).  \n\n2. **Experimental insufficiency** –  \n   - Key baselines such as 4DGS [5] are missing in several figures (e.g., Figure 2), and qualitative results appear cherry-picked. In Figure 3, for example, the Cook Spinach sequence shows that 4DGS visually outperforms the proposed method in both facial and object regions.  \n   - The claimed superiority is not consistently demonstrated: PSNR/SSIM gains over strong baselines are marginal (≤1 dB) and may not be statistically significant.  \n   - No comparison against simpler node initialization (e.g., K-Means clustering GS according to spatial position) or trajectory models (linear vs. spline) is provided to justify additional complexity.  \n   - The *spline-based trajectory* (Sec. 4.4) merely replaces existing linear or MLP deformation with cubic interpolation. Given that both N3DV [6] and Hyper-NeRF [7] are low-motion datasets, linear interpolation would likely suffice. No temporal interpolation or high-motion sequence evaluation is provided.  \n\n```\n[1] Huang, Yi-Hua, et al. \"Sc-gs: Sparse-controlled gaussian splatting for editable dynamic scenes.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024.\n\n[2] Wan, Diwen, Ruijie Lu, and Gang Zeng. \"Superpoint gaussian splatting for real-time high-fidelity dynamic scene reconstruction.\" arXiv preprint arXiv:2406.03697 (2024).\n\n[3] He, Bing, et al. \"S4d: Streaming 4d real-world reconstruction with gaussians and 3d control points.\" arXiv preprint arXiv:2408.13036 (2024).\n\n[4] Lei, Jiahui, et al. \"Mosca: Dynamic gaussian fusion from casual videos via 4d motion scaffolds.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n[5] Wu, Guanjun, et al. \"4d gaussian splatting for real-time dynamic scene rendering.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024.\n\n[6] Li, Tianye, et al. \"Neural 3d video synthesis from multi-view video.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.\n\n[7] Park, Keunhong, et al. \"Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields.\" arXiv preprint arXiv:2106.13228 (2021).\n```", "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)