PAGE-4D: Disentangled Pose and Geometry Estimation for VGGT-4D Perception
Authors: Kaichen Zhou
Yuhan Wang
Grace Chen
Gaspard Beaudouin
Fangneng Zhan
Paul Pu Liang
Mengyu Wang
Venue: ICLR 2026 Poster (2026)
Keywords: 4D Perception
Camera Pose Estimation
Depth Estimation
Point Cloud Reconstructionn
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Recent 3D feed-forward models, such as the Visual Geometry Grounded Transformer (VGGT), have shown strong capability in inferring 3D attributes of static scenes. However, since they are typically trained on static datasets, these models often struggle in real-world scenarios involving complex dynamic elements, such as moving humans or deformable objects like umbrellas. To address this limitation, we introduce PAGE-4D, a feedforward model that extends VGGT to dynamic scenes, enabling camera pose estimation, depth prediction, point cloud reconstruction, and point tracking—all without post-processing. Training a geometry transformer for dynamic scenes from scratch, however, demands large-scale dynamic datasets and substantial computational resources, which are often impractical. To overcome this, we propose an efficient fine-tuning strategy that allows PAGE-4D to generalize to dynamic scenarios using only limited dynamic data and compute. In particular, we design a dynamics-aware aggregator that disentangles dynamic from static content for downstream scene understanding tasks: it first predicts a dynamics-aware mask, which then guides a dynamics-aware global attention mechanism. Extensive experiments show that PAGE-4D consistently outperforms the original VGGT in dynamic scenarios, achieving superior results in camera pose estimation, monocular and video depth estimation, and dense point map reconstruction. The source code and pretrained model weights are provided in the https://page4d.github.io.

Published as a conference paper at ICLR 2026
PAGE-4D: DISENTANGLED POSE AND GEOME- TRY ESTIMATION FOR VGGT-4D PERCEPTION
Kaichen Zhou 1.2
Yuhan Wang** Paul Pu Liang
Grace Chen Gaspard Beaudouin"
Fangneng Zhan
Mengyu Wang
Harvard Al and Robotics Lab, Harvard University
2 Media Lab and Electrical Engineering and Computer Science, Massachusetts Institute of Technology 3 *Department of Computing, Imperial College London
ÉCoole Nationale des Ponts et Chaussées, Institut Polytechnique de Paris Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University
VGGT
PAGE-4D (Ours)
Figure I: PAGE-4D takes a sequence of RGB images depicting dynamic scene as input and simultaneously predicts the corresponding camera parameters and 3D geometry anformation-all within a fraction of a second. Compared to VGGT, PAGE-4D produces denser and more accurate point cloud reconstructions with better depth estimation quality. (Best viewed in PDF.)
ABSTRACT
Recent 3D feed-forward models, such as the Visual Geometry Grounded Trans- former (VGGT), have shown strong capability in inferring 3D attributes of static scenes. However, since they typically trained static datasets, these mod- els often struggle in real-world scenarios involving complex dynamic elements, such moving humans deformable objects like umbrellas. To address this limitation, we introduce PAGE-4D, feedforward model that extends VGGT dynamic scenes, enabling camera pose estimation, depth prediction and point cloud reconstruction -all without post-processing. A central challenge in multi- task 4D reconstruction is the inherent conflict between tasks: accurate camera pose estimation requires suppressing dynamic regions, while geometry recon- struction requires modeling them. To resolve this tension, we propose a dynamics- aware aggregator that disentangles static and dynamic information by predicting a dynamics-awan mlask-suppressingg motion cues for pose estimation while ampli- fying them for geometry reconstruction. Extensive experiments show that PAGE- 4D consistently outperforms the original VGGT in dynamic scenarios, achieving superior results in camera pose estimation, monocular and video depth estima- tion, and dense point map reconstruction. Necessary code and additional demos are available at Link
"These authors contributed equally the second authors. These authors jointly supervised this work. The ccdnowledgments for the video used in Fig. I and Fig. provided in the appendix.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Despite recent advances in feedforward 3D estimation of static scenes from image sets ZZang et al,) 2025at Wang et al.) [025aa] 2024), extending these capabilities to dynamic environments- where objects or people undergo motion or deformation- remains a significant challenge due to the complexity of real-world motion. A common strategy for handling dynamic scenarios is decom- pose the problem into a series of sub-modules, such as depth estimation, optical flow computation, and object tracking (Luiten et al., 2020; Mustafa et al. 2016; Kopt et al. 2021: Zhang et al. (0226). While this modular approach simplifies the task by disentangling different components, it often re- sults in increased computational cost and error accumulation across sequential stages (Zhang al., 2025b). Given the limitations of modular pipelines, a unified method for dynamic geometry learn- ing that avoids sequential decomposition offers a more effective and coherent solution. However, developing such models usually requires capturing spatiotemporal relationships across frames, and demands notable computational resources as well as access large-scale dynamic datasets with ground-truth geometry (Zhang et al. 2025b, Wang al. 2025b).
Motivated by these challenges, we present PAGE-4D, a unified and efficient feed-forward model that enables the inference of key 3D attributes in dynamic scenes as shown in Fig [ To address the limited availability of labeled dynamic data, we build on the pretrained 3D foundation model VGGT (Wang et al.) (025aa) and adapt it to dynamic scenarios through targeted fine-tuning. While VGGT demonstrates strong performance in static scene understanding, its accuracy drops signif- icantly when applied to dynamic environments involving people, vehicles, deformable objects. This limitation stems from a fundamental tension: motion provides valuable cues for geometry esti- mation in dynamic scenarios, yet simultaneously introduces noise that corrupts camera pose estima- tion by violating the static epipolar constraint, as shown in Fig. (a). In other words, the very signals that enable reconstructing dynamic objects are also those that hinder reliable pose recovery (Chen et al. 2025 Zhang et al. 2025b 2022b).
This insight motivates our central idea: rather than viewing dynamics as uniformly harmful or helpful, we disentangle their effects across tasks. We introduce a dynamics- aggregator that first predicts mask to identify dynamic regions, and then applies via a cross-attention mecha- nism-filtering dynamic content for camera pose tokens while emphasizing it for geometry tokens. Together with targeted fine-tuning of layers most sensitive to dynamics, this design allows us to harness motion where it benefits geometry grounding, while suppressing its negative impact on pose estimation. With this design. our method achieves accurate pose and geometry estimation for both static and dynamic content in challenging dynamic scenarios, as illustrated in Fig. [ Through ex- tensive experiments, PAGE-4D establishes new state-of toht---rttt performance across multiple bench- marks and tasks. For instance, on the Sintel benchmark, it reduces the camera pose estimation ATE from 0.214 (VGGT) to 0.143 and improves the scale-aligned video depth Abs Rel from 0,484 to 0.357. Notably, thanks its plug-in design, PAGE-4D adds only negligible overhead in both runtime and storage compared to VGGT. This work makes the following key contributions:
We propose PAGE-4D, dynamic-aware extension of VGGT for 4D scene understanding, which achieves state-of-ther -ott----rttttttt results on dynamic geometry perception benchmarks.
We design dynamics-aware aggregator that combines (i) mask prediction module for identifying dynamic regions and (ii) a global attention mechanism that selectively leverages suppresses dynamic information across tasks.
We provide an in-depth analysis of VGGT under dynamic conditions and introduce tar- geted fine tue-tuning strategy that adapts only the layers most sensitive to dynamics, enabling efficient transfer by updating only limited subset of parameters.
2 RELATED WORK
3D Feedforward Model is learning- approach that reconstructs static 3D scene geometry from input images with temporal invariance assumption (Bochkovskii et al. 2024 Yin et al. 2023 Piccinelli et al.] 2024 Leroy et al., 2024), treating all views as capturing the same static scene.
DUS(3R (Wang et al. 2024) is the representative of this reconstruction framework, introducing
transformer- based architectures that processes image pairs from different viewpoints, learning di-
rect mappings from 2D image pixels to 3D coordinate fields. Subsequent works (Tang et al. 2024
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Yang et al. 2025 Wang et al. 2025b; Bhat et al. 2023; Tang & Tan 2018 Yao al. 2018 Chen et al. 2021 1 have explored broader scenarios. Among those, VGGT Wang et al. 2025a) presents a unified architecture using alternating attention mechanisms within each frame and across the entire sequence, responding the need of joint prediction of camera poses, depth maps, and point corre- spondences through integrated training. Despite these advances, traditional 3D methods remain temporally invariant and struggle with dynamic scenes, motivating the need for 4D feedforward approaches that explicitly capture scene dynamics.
4D Feedforward Model emerges to reconstruct dynamic scenes by capturing geometric evolution over time from image sequences (Tian et al., 2023 Van Hoorick al., 2022 Büsching al. 2024
Liang et al. 2024, Zhao et al.) (023). However, it faces sigmificant challenges in modeling tem- poral geometry changes, as moving objects violate the rigid geometry assumptions of static meth-
ods (Oliensis 2000) Ozyesil et al. 2017
1177.Co Cao et al., 2025). Given DUSt3R's success in static recon-
struction, several works (Lu et al. 2024 Wu et al. 2025 Wang Agapito 2024 Xu et al., 2024]
Yao et al. 2025) have adapted this framework for dynamic scenarios. While MONST3R (Zhang et al. 20255) fine tunes DUS(3R on video sequences, DaUSt3R (Han et al. (025) introduces explicit temporal modeling through 4D pointmap representations and cross-frame attention mechanisms, im- proving in establishment of correspondences between moving objects across frames. Other efforts include training- free methods like Easi3R (Chen et al. 2025). Despite the progress shown by these DUSedR-based approaches in dynamic content, they are all constrained by the pairwise progressing
framework in DUS(3R. Alternative approaches (Feng al. 2025 Li al. [2024] Xu et al.) [025
Jiang et al. 2025 Jin et al. 2025) Piccinelli et al. 20024 Bochkovski et al. 2024) explore different
architectural designs handle dynamic scenes for task-specific solutions, but they often sacrifice
the generalizability of feedforward approaches. More recently, the success of VGGT in sequence- based static reconstruction has inspired its extensions (Li al. 2025) to dynamic scenarios. Recent approaches such as MoVieS (Lin et al.) (025) and Stream V VGTT (Zhuo et al.) (025) focus on narrow, application-specinn scenarios rather than the broader challenge of adapting static models to dynamic domains. In contrast, we propose PAGE-4D to address this general challenge, demonstrating that carefully targeted fine-tuning of key attention components can effectively bridge the static datii-d divide without requiring major architectural changes.
METHODOLOGY
In this paper, we extend the VGGT to PAGE-4D (Disentangled Pose and Geometry Estimation for 4D Perception), dynamic-aware framework for robust 4D scene understanding. Given sequence of N RGB frames [4)," captured in a dynamic environment, our objective is to predict temporally consistent 3D outputs for each frame:
f(L))) P,, T.)
where gi E R encodes the camera intrinsics and extrinsics, D; E TR Hx is the depth map, P, the 3D point map, and T; Xxx feature representation for 2D-3D point tracking.
In this section, we begin by examining the behavior of VGGT under dynamic conditions and ana- lyzing how its transformer architecture represents spatiotemporal information. This analysis reveals fundamental limitations when directly applying VGGT to dynamic scenes. Guided by these insights, we introduce PAGE-4D, principled yet lightweight extension of VGGT that enables accurate and efficient estimation of camera pose, geometry, and tracking in challenging dynamic environments.
3.1 MOTIVATION
Although VGGT achieves state-of-the-art performance in static scene un- derstanding, its accuracy degrades markedly in the presence of dynamic objects. On the Odyssey test set (Zheng et al. 2023), which evaluates long-range point tracking and geometry understanding in dynamic scenes, we directly apply VGGT for evaluation. The results reveal clear gap between static and dynamic regions: the Absolute Depth Error in dynamic regions is 94% higher than in static regions. These results highlight the need for an architecture that achieves reliable scene un- derstanding across both static and dynamic scenarios.
Empirical Observation:
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Published as a conference paper at ICLR 2026
Refonaece
Target
Fraun
Laver
12 E-pert
Unproject
Layer
Tvint
Panomic
Poiut
Pixel
Point
Maps
(b)
Figure 2: Motivating illustration: (a) In static scenes, geometric consistency is preserved across
frames, while in dynamic scenes, moving objects violate this consistency. (b) Visualization of VGGT attention maps from the 5th, 12th, 18th, and 24th layers of global attention block with the method in Caron et al. (202T). Attention values are visualized using white-to-red color map, with white indicating low values and red indicating high values. VGGT tends to ignore dynamic content during the feed-forward process, which motivates our design of the dynamics-awe aaaar mask.
To better understand this gap, we first follow Chefer et al. (2021) on feature visualization and exam- ine key layers of VGGT (Fig. 2 (b)). We observe that dynamic regions exhibit weaker activations compared to static ones, suggesting that VGGT tends to ignore dynamic content. We then perform an ablation in which attention among dynamic tokens explicitly suppressed (see Appendix). Masking dynamic patches from the cross-frame attention mechanism improves camera pose estimation, but at the same time leads to a sharp drop in geometry. Together, these findings reveal fundamental tension in dynamic scenes: while camera pose estimation benefits from suppressing dynamic regions to maintain epipolar consistency, geometry requires exploiting their motion cues.
Static Case - Geometric Foundations: Formally, under static conditions, geometry estimation can be achieved by implicitly modeling the correspondence between a citternnee-rrane homogeneous pixel X,. and its target-frame homogeneous pixel x; (Fig. 2 (a)) (Zhang et al. 2025b Chen et al.
2025 which is fully determined by the camera intrinsics, depth, and relative pose:
Pixel (Target Frame)
Inversed Intrinsic
Translation Vector
X
Ri-r
(x+
K
X,
+
tter
(I)
Rotation Matrix
Depth
Pixel (Reference Frame)
This equation encodes the standard rigid-scene geometry assumption: once depth and camera mo- tion are known, pixel correspondences across frames can be predicted without ambiguity. Mean- while, pose estimation (Zhang al.) 20256) Chen et al., 2025), due to the concentration of relative camera motion, in VGGT often reduces to litting an essential matrix E that enforces the epipolar constraint between normalized homogeneous pixels X, and Xy:
Homogeneous Pixel (Target Frame)
x
E X,-
O,
E
R
Homogeneous Pixel (Reference Frame)
Essential Matrix
Summary: Under static conditions, both Eqn. and Eqn. 2 hold for all non-occluded pixel pairs (Xr, x.) between the reference and target frames. This makes the joint optimization camera tokens and geometry tokens over the same patches within a frame reasonable design choice.
Dynamic Case Violation and Residuals: In dynamic scenes, to realize geometry estimation, motion information needs to be taken into consideration for accurate prediction:
X; K Rie, D,(x,) K --x,, turr] KM
where Mr-r represents the displacement induced by object motion. Meanwhile, in the presence of dynamic motion, the Eqn. 2 for pose estimation longer holds. The violation manifests
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N; Layers
Adapted N₂ Layers
N₂ Layers
Global
Frame
- Dynamic-ssom global attetion
Caneea Hoa!
Ingut
Ameriion
Attestion
Amention
Carera
Mood 8
Camera
Carart
Mi
g
Regioer
Point
M
Panch
Map
M
DPT
Depth
Map
Mark Prodike
Figure 3: Fine-tuning strategy: Instead of fine-tuning the entire VGGT architecture, we adapt only the middle N₂ layers of the global attention mechanism, which are most critical for cross-frame information fusion. To further address dynamic scenes, we introduce dynamics-aware aggregator that predicts a mask to disentangle dynamic and static content.
residual:
8(x,) x EX, 2
Z, 7 = _X_ (x,),
(4)
where n(x,) is the unit normal of the epipolar line associated with X,, and AX (x,) is the com- ponent of the dynamic displacement perpendicular that line. This residual quantifies the degree to which dynamic motion "pushes" correspondences away from the epipolar geometry predicted by the camera. The larger the residual, the stronger the violation the static-scene assumption, and the greater the resulting pose estimation error. Eqn. 4 implies that in dynamic scenarios, only the static
subset of pixel pairs (x:*o, xx(e) satisfy Eqn. 2
Summary: Under dynamic conditions, Eqn. 3 for geometry estimation remains valid for all non-
occluded pixel pairs (x,, Xt) between the target and reference frames, whereas Eqn. 2 for pose estimation holds only for the static subset of non-occluded pixels (xx*a xx*a), as explained Eqn. 4
Insight: Under dynamic scenarios, camera pose estimation is brittle to dynamic motion, as small residuals corrupt essential matrix fitting, while geometry and tracking tasks can in fact benefit from modeling Mş-r. Motivated by this insight, we propose PAGE-4D, a dynamic-awan exten- sion of VGGT that disentangles the role of dynamic regions across tasks suppressnnn them for pose estimation while leveraging them for geometry and tracking.
3.2 PAGE-4D
PAGE-4D is composed of four key components: (1) pre-trained DINO-style (Zhang et al. 2022a) encoder that extracts image-level representations; (2) a dynamics-awant aggregator that integrates spatial and temporal cues through three modules Frame Attention for inter-frame patch relations, Global Attention for intra-frame patch relations, and Dynamics-Aware Global Attention for disen- tangling dynamic from static content; (3) lightweight decoders for depth, 3D point maps; and (4) larger decoder dedicated to camera pose estimation.
PAGE-4D inherits components (I), (3), and (4) directly from VGGT, while extending component (2) into three-stage dynamics-aware aggregator as Fig. 3 The first stage consists of Nj layers, each composed of one Global Attention and one Frame Attention block. Its output is fed into dy- namic mask prediction module, which produces a dynamics-award awwre mask. This mask is then applied in the second stage disentangle dynamic and static content for pose and geometry estimation. The second stage itself consists of N₂ layers, each comprising a Dynamics-Aware Global Attention block and Frame Attention block. The final stage consists of Ng layers as the first stage.
3.2.1 DYNAMICS MASK PREDICTION
A central challenge dynamic scenes is to selectively suppress the influence of moving objects for tasks such as pose estimation, while still retaining their information for geometry. To achieve this, we design a dynamic mask prediction module that learns, in a self-supervised manner, which spatial regions likely correspond to dynamic objects. This is feasible because the middle
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Published as a conference paper at ICLR 2026
Input
VGGT
Ours
Frame?
France
T'amee
Frumee
Frame
Frame
Frime
Figure 4: Qualitative Comparison of Point Cloud Estimation on the Bonn & Sintel: As shown in the figure, our method effectively captures the geometric structure in scenarios with complex motion, whereas VGGT produces fragmented and inconsistent geometry. (Best viewed in PDF.
layers of PAGE-4D already disentangle dynamic and static content, as illustrated in Fig. bb), where dynamic regions are treated distinctly. Formally, given the token features E R Bx from the aggregator, we first extract only the patch tokens Zp [X= These tokens are projected into lower-dimensional representation via linear mapping. followed by depthwise convolutional head that produces mask logits: m DeprDepth (0(z)) E (B(BS)xxxxXX) where & denotes the linear projection. To convert logits into suppression probabilities, we introduce learnable parameters T (temperature) and (scaling factor), which are optimized with the network: softplus(Togg) + = softplus + The final dynamic mask is
= a
m E BxSH-))(HW)
where a is the sigmoid function and then the final dynamic mask is padded to match the full token length P. Intuitively, regions with large positive logits correspond to patches with strong evidence of dynamic motion, and are therefore suppressed. This design allows the network to learn adap- tive, continuous suppression weights instead of binary masks, making it more robust to ambiguous motion boundaries and partial occlusions.
3.2.2 MASK ATTENTION
Once the dynamic mask M has been predicted, it can be directly incorporated into the transformer attention mechanism. Specifically, given queries Q, keys K, and values V, attention is:
QK
Attn(Q. Q K. V) = softmax
V,
(6)
Vd
where M E (Bx(S-P)x(SPP) is the broadcasted mask applied to the attention logits. Importantly, we apply this mask in a task-specific manner: Pose estimation. For queries corresponding to the camera token and registeration token, M actively suppresses attention to dynamic regions, ensuring that pose estimation remains consistent with epipolar geometry and statie scene constraints. Depth and Point Cloud. For patches concerning these tasks, the mask is not applied, allowing the network to leverage dynamic motion cues to improve point map reconstruction and 2D-3D tracking accuracy.
This asymmetric design explicitly disentangles the role of dynamic regions across tasks. Dynamic objects, which are detrimental for camera pose estimation, are ignored in that context, but their motion signals remain available for geometry and tracking tasks. By learning the mask in fully differentiable manner, the model adapts its behavior to the motion patterns present in the training data, rather than relying pre-defined heuristics.
Ceemory-Effiiien Mask Mechanism Although Eq. 6 describes a full PS+ mask, forming this matrix would require O(N2) memory and break fused Scaled Dot-Product Attention, where N = S.P. PAGE-4D instead implements an equivalent additive mask using two vectors. Given attention inputs Q K, V Rxxd the mask head predicts:
TERN
CERN
We append these to the feature dimension:
df = [avdd r Vdd.
K/ = [kj Ggl, m/= [15, 0),

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Published as a conference paper at ICLR 2026
Table 1: Video Depth Estimation on Sintel (Butler et al. 2012), Bonn (Palazzolo et al., 2019) and DyCheck (Yang et al., 2025). FPS is evaluated on KITTI using one A800 GPU. Missing entries (-) denote results not reported in the original papers cited.
Sintel
DyCheck
Method
Params
Aligu
Ahs Rel I
<. 1:25
Als Rel
<< 1,25
Abc Rel
< 1:25
FPS
DUSER
Wane al.
2024
571M
0.662
0.434
0.151
0.839
1.25
689M
0.558
0.487
Fast3R
CUT3R
12ng & BD
Wing Mig
1129
20250
793M
648M
Video
scale
0.638
0.430
0,422
0,465
0.194
0.077
0.772
0.937
0.176
0.740
6.98
65.8
VGGT
Wang er 21
4023
1.268
Depth
0.484
0.336
0,553
0.107
0.883
0.182
0.743
43.2
PAGE-IDIUUE
1,268
0.357
0.699
0.092
0.904
0.170
0.785
43.2
DUSER
Wang al 2024
571M
0.570
0.193
0.152
0.835
1.25
MASGR
His
0.517
Fast3R
CUTRR
Yang 2 mi 20225
Wibk of Mi 201200
648M
793M
scale&shift
Video
0.518
0.534
0,486
0.55%
0.196
0.075
0.768
0.943
0.228
0.635
65.8
6.9%
VGGT
Wang St 20250
1,408
1,268
Depth
0.791
0.378
0.35%
0.605
0.102
0.890
0.161
0.778
43.2
PAGESID(UBES)
126B
0.212
0.763
0.090
0.903
0.145
0.854
43.2
DUSER
Wang 2014
571M
0.488
0.532
0.139
0.832
1.25
MAS:3R
689M
0.413
0.569
0.123
0,833
1.01
MknSTáK
Zhing 2 Ruza
571M
0,402
0.525
0.069
0.954
1.27
CUT3R
Wone RTPS0
793M
Monocular Depth
0,418
0,520
0.058
0.967
0.149
0,790
6.98
Fast3R
645M
0.509
FLARE
Zhane bl.
1,40
0.606
0,402
0.130
0.836
1.75
VGGT
Waing et
1,66
0.292
0.629
0.071
0.947
0.160
0.799
43.2
PAGR-IDIUUIS))
1.268
0.242
0.742
0,053
0.970
0.141
0.840
43.2
where d' = d d+1. Then:
q. Kg
+ +r
d
Vd
but without constructing the N x N mask.
This uses only (N) memory, stays compatible with fused Scaled Dot-Product Attention.
3.3 TRAINING DETAILS
During fine-tuning, we update only the middle 10 layers while freezing the remaining aggregator and decoder layers, thereby tuning just 30% of the model instead of the full network. This design is supported by studies on transformer representations, which show that lower layers capture local structures, middle layers model regional relationships, and higher layers en- code global semantics (Raghu et al. 2021 Caron et al., [02TT. Moreover, as illustrated in Fig 2b), the middle layers of VGGT tend to suppress dynamic content, leading to degraded performance in dynamic scenarios. By selectively fine-tuning these layers, we aim to reintroduce dynamic infor- mation into the feed-forward process. Consistent with this intuition, our ablations (Please Refer to Appendix) confirm that the later middle layers contribute most significantly to accurate geometry estimation.
Fine-tuning Strategy.
Loss Functions. We adopt multi-task loss combining supervision for camera pose, depth and point-maps:
CaCamera + Ldephh Lpmap-
Following VGGT, we empirically set the loss weights to balance gradients across tasks, with A = 5. depth and point-map losses
We adopt: Huber loss for camera pose estimation, with gradient regularization. We do not include point tracking in our model, since the tracking head in VGGT is primarily designed for view registration and is not well-suited to dynamic scenarios. In addition, VGGT does not provide clear training code for the tracking head. These two factors prevent us from incorporating point tracking into our framework.
eneertainty-weigheee
4 EXPERIMENTS
To evaluate the effectiveness of PAGE-4D, we apply it to monocular video sequences and assess its performance on five tasks: video depth estimation, monocular depth estimation, camera pose esti- mation, multi-view point map reconstruction, and 4D view synthesis. We compare against several strong Daselines-DUSSE
(Wang al. 2024). MASt3R (Leroy et al. 2024), MonST3R (Zhang ct al., 20256). CUT3R (Wang et al. (025b). Fast3R (Yang et al., 2025). FLARE (Zhang et al. 2025c), and VGGT (Wang al. 2025a across each subtask.
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Published as a conference paper at ICLR 2026
4.1 VIDEO DEPTH ESTIMATION
Following the protocol of prior works (Wang et al. 2024; Zhang et al. 2025b), we evaluate our approach on the video depth estimation task using Sintel (Butler et al. 2012) and Bonn (Palazzolo et al. 2019). To assess robustness to dynamic objects, we additionally incorporate DyCheck (Gao al. 2022p. We report Absolute Relative Error (Abs Rel) and prediction accuracy at the threshold o < 1.25, under two alignment settings: (i) scale-only alignment and (ii) joint scale and 3D trans- lation alignment. Qualitative results could be found in Fig 4 and Fig 5 As summarized in Tab. I our method establishes a new state of the art across all three datasets and both alignment settings among feed-forward 3D reconstruction models. Compared to VGGT (Wang et al., 2025a), which represents the strongest prior baseline, our approach consistently reduces error and improves accu- racy. For example, on Sintel with scale-shift alignment, we improve 8 < 1.25 accuracy from 0.605 VGGT to 0.763 (+26.1%) while lowering Abs Rel from 0.378 to 0.212 (-42%). Similar trends are observed on Sintel and Bonn, where our method outperforms VGGT under both alignment regimes, without incurring noticeable increases in speed memory consumption.
4.2 MONOCULAR DEPTH ESTIMATION
In addition to video depth, we evaluate our approach on monocular depth estimation following Leroy et (022)); Zhou al. (2025). Each predicted depth map aligned independently with its ground truth, in contrast to the video setting where a single scale (and shift) is applied across the entire sequence. As summarized in Tab. I our method shows consistent improvements over existing feed-forward reconstruction methods. In particular, compared to VGGT (Wang et al., (025a) in Sintel dataset, our approach reduces Abs Rel from 0.292 0.242, and increases < 1.25 accuracy from 0.629 to 0.742. While not explicitly optimized for single-frame depth estimation, our method performs favorably against dedicated baselines such as DUSt3R, MONST3R, and FLARE. These results suggest that our model generalizes well from video sequences to single-image inputs.
4.3
CAMERA POSE ESTIMATION
We evaluate camera pose estimation the
Table 2: Camera Pose Estimation on Sintel and Tum
Sin- tel (Butler et al., (012) and Tum (Sturm et al. 2012) benchmarks. Following the protocol in (Zhang et al. 2025b), report Absolute Tra-
dynamic-scene
Sintel
Tum
Optim.
RP
RPLer:
RPG
RPE,:
MonSTIR
Zhunn et b0256
0.108
0J342
0.733
0.098
0.019
0.935
DUS0R
King
0.417
0.250
5.796
0.140
0.106
3.286
Spumn1R
Wing Apapii
0.329
CUT3R
Wang at 20:
0.213
0.065
0.621
0.065
0,015
0.475
VGGT
Waig 24 mi. 201250
0.214
0.079
D.63
00228
0.014
01371
PAGE-DI(GE))
0.538
0.011
jectory Error (ATE), Relative Pose Error in translation (RPE and rotation (RPEm)) For a fair comparison, predicted trajectories are aligned to the ground truth via Sim(3) Umeyama alignment, and we uniformly sample 10 frames per sequence for evaluation. As shown in Tab. 2 our method delivers substantial improvements Tum, reducing RPE by 21% % and RPE by 13% compared to prior feed-forward approaches, while maintaining competitive ATE, On Sintel, our approach also reduces RPF by 17% highlighting its robustness across both synthetic and real-world dynamic scenes.
4.4 POINT MAP ESTIMATION
We further evaluate our method on the DyCheck (Gao et al.) 2022) benchmark for dynamic-scene point map reconstruction. Following the protocol of (Wang et al. 2024 Zhang et | (2025)), we report Accuracy (Acc.),
Table 3: Point reconstruction DyCheck.
Dy Dhehk
Method
Optim.
Acc 1
Comp
Overall 1
Mean
Median
Mean
Median
Mean
Median
MONST3R Zhang et al.
2025b
0.851
0.689
1.734
0.958
1.292
0.823
DUSER Wang al. 2024
0.802
0.595
1.950
0.815
1.376
0.705
CUT3R Wang et al 20255
0.458
0.342
1.633
0.792
1.042
0.567
DAS3R Xu al. 2024
1.772
1.438
2.503
1.548
0.475
0.352
VGGT Wang et al. 2025a
1.051
1.016
1,594
1.393
1.322
1.204
PAGE-ID(OurS)
0.403
0.284
1.222
0.728
1.115
0,559
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Published as a conference paper at ICLR 2026
Figure 5: Qualitative Results of Point Cloud Estimation. PAGE-4D can estimate camera poses and depth maps from RGB inputs, even the presence of dynamic objects. (Best viewed PDF.) We report PSNR SSIM and
Table 4: Novel View Synthesis Nerfie (Gafni et al. 2021). LPIPS 4. for each scene and the average.
Mothed
10008
50000
1/785
25N8
30131
LIITS
1SNIL
SSIRET
11M55
1958
50000
LIB55
FSNK
2510E
LHIS
15801
35030
LIN5
13.7%
01.15
13.455
8:543
0.523
12.355
0.351
[1645
12.931
01416
14.80
0.513
3.552
12:90
0.35
0.95
0.445
14.85
8:533
0.59
8.477
11:28
B440
3.447
DAT2
0.333
13.355
0471
0.382
18.94
0,49
133155
03355
0.512
13:513
0.544
8.333
17.93
0.66
0.49
Completion (Comp.). and Overall error, where lower values indicate better reconstruction quality, As summarized in Tab. 3 our method achieves substantial improvements over prior feed-forward approaches. In particular, compared to the outputs produced by the point head of VGGT (Wang et al. 2025a), our approach reduces the mean Accuracy eтror by more than 60% (1.051 - 0.403) and the median error by over 70% (1.016 + 0.284). Similarly, our method yields consistent gains on Completion, with both mean and median errors reduced by over 20%. These results highlight the effectiveness our dynamic-aware modeling: while existing methods either fail to accurately reconstruct moving regions (c.g.. Easi3R (Chen et al., (0255) show degraded completion under dynamic motion (c.g., MonST3R (Zhang et al.] 2025b)), our method balances accuracy and completeness, producing robust reconstructions in challenging dynamic scenarios.
4.5 DYNAMIC SCENES RENDERING
Rendering dynamic scenes has become key focus in the computer vision community (Wu et al.
2024 Pumarola et al. 202 Li al. 2023; Zhou al.) 2023 2024). However, most existing ap- proaches rely heavily accurate camera poses and high-quality initial point clouds clouds-qqanticns quantities that are often time-consuming obtain and particularly challenging to estimate the presence of complex object motion. PAGE-4D addresses this limitation by jointly predicting temporally con- sistent camera poses and dense 3D point clouds directly from RGB sequences containing dynamic content. To evaluate the utility of PAGE-4D for dynamic scene rendering, we use its reconstructed point clouds as initialization for the recent 4D-Gaussian splatting framework (Wu et al., assess the resulting novel view synthesis quality the Nerfie benchmark (Galm et al.
2024) and
2021 As
shown in Tab. our method consistently achieves superior rendering performance across scenes compared prior feed-forward 3D reconstruction models. Notably, PAGE-4D provides more robust geometric initialization which leads to improvements over both static-scene baselines (c.g.. DUS(3R, VGGT) and recent dynamic-aware methods (e.g., MonST3R, CUT3R), demonstrating its effectiveness as geometry prior for high-fidelity 4D rendering.
4.6 ABLATION STUDIES
To evaluate the effectiveness of the proposed technique, we perform two ablation studies. First, examine the fine-tuning strategy by comparing tion layers are updated-wy with baseline that fine-tunes all layers of the network (VGGT* (Whole study the role of the dynamic-aware aware aggregator
Spproach-whese only the middle N₂ atten-
Model)). Second, with a variant that simply fine-tunes the middle N₂ layers of VGGT without disentangling dynamics (VGGT* * (Middle Layers)). From the comparison between VGGTs (Whole Model) and VGGT
comparing our full model

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Published as a conference paper at ICLR 2026
Table 5: Video Depth Estimation on Sintel (Butler et al. 2012), Bonn Palazzolo al., 2019 and DyCheck (Yang et al. 2025).
Method
Align
DyCheck
Abs Rel
di 2. 1:20
Abx Rel
1:25
Abs Rel
1:25
VGGT* (Whole Model)
0.405
0.593
0.101
0.891
0.175
0.735
VGGT (Middle Lavers)
VVGTT* Layers
(Video-Dephh)
scale
0.409
0.590
0.099
0,879
0.177
0.766
(Middle Layers) shown in Tab. 5 observe that restricting fine-tuning to the middle layers yields performance comparable to full fine-tuning, confirming that these layers capture the most critical information. More importantly, by comparing Ours VGGT* (Middle Layers + Mask Attention) with VGGT* (Middle Layers), we demonstrate that explicitly disentangling pose and geometry estimation through the dynamic-aware aggregator unlocks the potential of the backbone, leading to substantial performance gains.
5 CONCLUSION
Understanding dynamic scenes remains a central challenge in 4D computer vision, where object motion simultaneously provides valuable geometric and disrupts static-scene assumptions crit- ical for camera pose estimation. In this work, we introduce PAGE-4D, a feedforward framework that adapts a pretrained 3D foundation model dynamic environments through disentanglement strategy. Our analysis shows that while VGGT excels in static scenarios, its unified treatment of mo- tion leads to conflicts across tasks. To address this, we propose dynamics-aware aggregator that disentangles static and dynamic ing them for geometry and tracking. Combined with a targeted fine-tuning strategy on the most dynamic-sensitive layers, this design unlocks the backbone's latent capacity for handling motion. Extensive experiments demonstrate that PAGE-4D achieves state-of-the-ar results across depth, pose, and point cloud reconstruction benchmarks. Importantly, show that effective disentangle- ment enables strong generalization even with limited dynamic data, paving the way for scalable and efficient 4D scene understanding.
dynamics for pose estimation while leverag-
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REFERENCES
Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, and Matthias Müller. ZoeDepth: Zero- shot transfer by combining relative and metric depth. arXiv preprint arXiv:2302. 2023.
Aleksei Bochkovskii, Amaāği Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R Richter, and Vladlen Koltun. Depth pro: Sharp monocular metric depth in less than a second. arXiv preprint arXiv:2410.02073. 2024.
Marcel Büsching, Josef Bengtson, David Nilsson, and Märten Björkman. FlowIBR: Leveraging pre-training for efficient neural image-based rendering of dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8016-8026, 2024.
D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black. A naturalistic open source movie for optical flow evaluation. In European Conference on Computer Vision, pp. 611-625. Springer, 2012.
Yukang Cao, Jiahao Lu, Zhisheng Huang, Zhuowei Shen, Chengfeng Zhao, Fangzhou Hong, Zhaoxi Chen, Xin Li, Wenping Wang, Yuan Liu, et al. Reconstructing 4D spatial intelligence: A survey. arXiv preprint arXiv:2507.21045 2025.
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties self-supervised vision transformers. Proceedings of the IEEE/CVF international conference on computer vision. pp. 9650-9660, 2021.
Hila Chefer, Shir Gur, and Lior Wolf. Transformer interpretability beyond attention visualization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp- 782-791, 2021.
Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. MVSNeRF: Fast generalizable radiance field reconstruction from multi-view stereo. arXiv preprint arXiv:2203. 2021.
Xingyu Chen, Yue Chen, Yuliang Xiu, Andreas Geiger, and Anpei Chen. Easi3r: Estimating disen- tangled motion from DUS(3R without training. arXiv preprint arXiv:2503.24391, 2025.
Haiwen Feng, Junyi Zhang, Qianqian Wang, Yufei Ye, Pengcheng Yu, Michael J. Black, Trevor Darrell, and Angjoo Kanazawa. S14RTrack: Simultaneous 4D reconstruction and tracking in the world. Proceedings of the TEEE/CVF International Conference on Computer Vision, 2025.
Guy Gafni, Justus Thies, Michael Zollhofer, and Matthias Nießner. Dynamic neural radiance fields for monocular 4d facial avatar reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8649-8658, 2021.
Hang Gao, Ruilong Li, Shubham Tulsiani, Bryan Russell, and Angjoo Kanazawa. Monocular dy- namic view synthesis: A reality check. Advances in Neural Information Processing Systems, 35: 33768-33780, 2022.
Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, et al. Kubric: A scalable dataset generator. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nilion, pp. 3749-3761, 2022.
Jisang Han, Honggyu An, Jaewoo Jung, Takuya Narihira, Junyoung Seo, Kazumi Fukuda, Chae- hyun Kim, Sunghwan Hong, Yuki Mitsufuji, and Seungryong Kim. D²USt3R: Enhancing 3D reconstruction with 4D pointmaps for dynamic scenes. arXiv preprint arXiv:2504.06266 2025.
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew Davison. Rlbench: The robot learning benchmark & learning environment. IEEE Robotics and Automation Letters, 5(2):3019 3026, 2020.
Zeren Jiang, Chuanxia Zheng, Iro Laina, Diane Larlus, and Andrea Vedaldi. Geo4D: Leveraging video generators for geometric 4D scene reconstruction. arXiv preprint arXiv:2504.07961 2025.

---

Published as a conference paper at ICLR 2026
Linyi Jin, Richard Tucker, Zhengqi Li, David Fouhey, Noah Snavely, and Aleksander Holynski. Stereo4D: Learning how things move in 3D from internet stereo videos. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, 2025.
Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. DynamicStereo: Consistent dynamic depth from stereo videos. In Proceedings of the IEEF/CVF Conference on Computer Vision and Puttern Recognition, pp- 5858-5868, 2023.
Johannes Kopf. Xuejian Rong, and Jia-Bin Huang, Robust consistent video depth estimation. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1611- 1621, 2021.
Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding image matching in 3d with mast3r. In European Conference Computer Vision, pp. 71-91. Springer, 2024.
Hong Li, Houyuan Chen, Chongjic Ye, Zhaoxi Chen, Bohan Li, Shaocong Xu, Xianda Guo, Xuhui Liu, Yikai Wang. Baochang Zhang, et al. Light of normals: Unified feature representation for universal photometric stereo. arXiv preprint arXiv:2506. 18882, 2025.
Zhengqi Li, Qianqian Wang, Forrester Cole, Richard Tucker, and Noah Snavely. DynIBaR: Neu- ral dynamic mmage-based rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4273-4282 2023.
Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang. Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holynski, and Noah Snavely. MegaSaM: Accurate, fast and robust struc- ture and motion from casual dynamic videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10486-10496, 2024.
Hanxue Liang Jiawei Ren, Ashkan Mirzaci, Antonio Torralba, Ziwei Liu, Igor Gilitschenski, Sanja Fidler, Cengiz Oztireli, Huan Ling, Zan Gojeie, et al. Feed-forward bullet-time reconstruction of dynamic scenes from monocular videos. arXiv preprint arXiv:2412.03522 2024.
Chenguo Lin, Yuchen Lin, Panwang Pan, Yifan Yu, Honglei Yan, Katerina Fragkiadaki, and Yadong Mu. Mo Motion-aware 4D dynamic view synthesis in one second. arXiv preprint MwXiv:2507.10065. 2025.
Jiahao Lu, Tianyu Huang, Peng Li, Zhiyang Dou, Cheng Lin, Zhiming Cui, Zhen Dong, Sai-Kit Yeung, Wenping Wang, and Yuan Liu. Align3r: Aligned monocular depth estimation for dynamic videos. arXiv preprint arXiv:2412.03073 2024.
Jonathon Luiten, Tobias Fischer, and Bastian Leibe. Track reconstruct and reconstruct to track. IEEE Robotics and Automation Letters, 5(2):1803-1810, 2020.
Lukas Mehl, Jenny Schmalfuss, Azin Jahedi, Yaroslava Nalivayko, and Andrés Bruhn. Spring: A high-resolution high-detail dataset and benchmark for scene flow, optical flow and stereo. Pro- ceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 4288- 4297, 2023.
Armin Mustafa, Hansung Kim, Jean-Yves Yves Guillemaut, and Adrian Hilton. Temporally coherent 4D reconstruction of complex dynamic scenes. In Proceedings of the IEEE Conference on Computer Vision and Paltern Recognition, pp. 4660-4669, 2016.
John Oliensis. A critique of cenntture-from-eeee algorithms. Computer Vision and Image Under- slanding, 80(2):172-214, 2000.
Onur Ozyesil, Vladislav Voroninski, Ronen Basri, and Amit Singer. A survey of structure from 2017.
motion. arXiv preprint
Emanuele Palazzolo, Jens Behley, Philipp Lottes, Philippe Giguere, and Cyrill Stachniss, Refu- sion: 3d reconstruction in dynamic environments for rgb-d cameras exploiting residuals. In 2019 JEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7855-7862. IEEE, 2019.
12

---

Published as a conference paper at ICLR 2026
Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Lue Van Gool, and Fisher Yu. UniDepth: Universal monocular metric depth estimation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 10106-10116, 2024.
Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francese Moreno-Noguer. D-NeRF: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 10318-10327, 2021.
Maithra Raghu, Thomas Unterthiner, Simon Korbllith, Chiyuan Zhang, and Alexey Dosovitskiy. Do vision transformers like convolutional neural networks? Advances in neural information processing systems, 34: 14: 1-1222 2021.
Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Common objects in 3D: Large-scale learning and evaluation of real-life 3D category reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021.
J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers. A benchmark for the evaluation of rgb-d slam systems. In Proc. of the International Conference on Intelligent Robot Systems (IROS), Oct. 2012.
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for au- tonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF conference com- puter vision and pattern recognition, pp. 2454,445,, 2020.
Chengzhou Tang and Ping Tan. BA-Net: Dense bundle adjustment network. arXiv preprint arXiv:1806.04800 04807, 2018.
Zhenggang Tang, Yuchen Fan, Dilin Wang, Hongyu Xu, Rakesh Ranjan, Alexander Schwing, and Zhicheng Yan. MV-DUSOR+: Single-stage scene reconstruction from sparse views in 2 seconds. arXiv preprint arXiv:2412.0697. 2024.
Fengrui Tian, Shaoyi Du, and Yueqi Duan. MonoNeRF: Learning a generalizable dynamic radi- ance field from monocular videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 17903-17911 2023.
Basile Van Hoorick, Purva Tendulkar, Didac Surís, Dennis Park, Simon Stent, and Carl Vondrick. Revealing occlusions with 4D neural fields. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3011-3021, 2022.
Hengyi Wang and Lourdes Agapito. 3D reconstruction with spatial memory. arXiv preprint arXiv:2408. 16061, 2024.
Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. VGGT: Visual geometry grounded transformer. In Proceedings of the IEEE/CVF Con- ference Computer Vision and Pattern Recognition, pp.
2025a.
Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei Efros, and Angjoo Kanazawa. Con- tinuous 3d perception model with persistent state. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 10510-10522, 2025b.
Shuzhe Wang. Vincent Leroy, Yohamn Cabon, Boris Chidlovskii, and Jerome Revaud. DUSB3: Geometrie 3D vision made easy. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 20697-20709, 2024.
Guanjun WU, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Xinggang Wang. 4D gaussian splatting for real--ime dynamic scene rendering. Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp. 203 20310-20320, 2024.
Yuqi Wu, Wenzhao Zheng. Jie Zhou, and Jiwen Lu. Point3R: Streaming 3D reconstruction with explicit spatial pointer memory, arXiv preprint 2:Xiv:2507.02863 2025.
13

---

Published as a conference paper at ICLR 2026
Kai Xu, Tze Ho Elden Tse, Jizong Peng. and Angela Yao. Das3r: Dynamics- aware gaussian splat- ting for static scene reconstruction. arXiv preprint arXiv:2412.19584, 2024.
Tian-Xing Xu, Xiangjun Gao, Wenbo Hu, Xiaoyu Li, Song-Hai Zhang, and Ying Shan. Geome- tryCrafter: Consistent geometry estimation for open-world videos with diffusion priors. arXiv preprint arXiv:2504.01016, 2025.
Jianing Yang, Alexander Sax, Kevin J. Liang, Mikael Henaff, Hao Tang, Ang Cao. Joyce Chai, Franziska Meier, and Matt Feiszli. Fast3R: Towards 3D reconstruction of 1000+ images in one forward pass. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025.
David Yifan Yao, Albert J. Zhai, and Shenlong Wang. Uni4D: Unifying visual foundation models for 4D modeling from a single video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1116-1126, 2025.
Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. MVSNet: Depth inference for unstruc- tured multi-view stereo. In European Conference on Computer Vision, 2018.
Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, and Chunhua Shen. Metric3D: Towards zero-shot metric 3D prediction from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9043-9053, 2023.
Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M Ni, and Heung- Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.00 03600, 2022a.
Jiahui Zhang, Yuelei Li, Anpei Chen, Muyu Xu, Kunhao Liu, Jianyuan Wang, -iao-Xiao Long, Hanxue Liang, Zexiang Xu, Hao Su, al. Advances in feed-forward 3D reconstruction and view synthesis: A survey, arXiv preprint 2rXX::2507.14501, 2025a
Junyi Zhang, Charles Herrmann. Junhwa Hur, Varun Jampani, Trevor Darrell, Forrester Cole, De- qing Sun, and Ming-Hsuan Hsuan Yang. MonST3R: A simple approach for estimating geometry in the presence of motion. In International Conference on Learning Representations 2025b.
Shangzhan Zhang, Jianyuan Wang, Yinghao Xu, Nan Xue, Christian Rupprecht, Xiaowei Zhou, Yujun Shen, and Gordon Wetzstein. FLARE: Feed-forward geometry, appearance and camera estimation from uncalibrated sparse views. In Proceedings of the IEEF/CVF Conference on Com- puter Vision and Pattern Recognition, pp. 21936-21947, 2025c.
Zhoutong Zhang, Forrester Cole, Zhengqi Li, Michael Rubinstein, Noah Snavely, and William T Freeman. Structure and motion from casual videos. In European Conference on Computer Vision, pp. 20-37. Springer, 2022b.
Xiaoming Zhao, Alex Colburn, Fangchang Ma, Miguel Angel Bautista, Joshua M Susskind, and Alexander G Schwing. Pscudo-generrliized dynamic view synthesis from video. arXiv preprint arXiv:2310.0858/ 2023.
Yang Zheng. Adam W Harley, Bokui Shen, Gordon Wetzstein, and Leonidas J Guibas. PointOdyssey: A large-scale synthetic dataset for long-term point tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 19855-19865, 2023.
Kaichen Zhou, Jia-Xing Zhong, Sangyun Shin, Kai Lu, Yiyuan Yang, Andrew Markham, and Niki Trigoni. DynPoint: Dynamic neural point for view synthesis. Advances in Neural Information Processing Systems, 66:69532-69545. 2023.
Kaichen Zhou, Jia- Wang Bian, Jian man-QQng Zheng, Jiaxing Zhong, Qian Xie, Niki Trigoni, and An- drew Markham. Manydepth2: Motion-aware self-supervised monocular depth estimation in dy- namic scenes. IEEE Robotics and Automation Letters, 2025.
Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, and Ming- Hsuan Yang. Driv- ingGaussian: Composite gaussian splatting for surrounding dynamic autonomous driving scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp- (634-21644 2024.
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Dong Zhuo, Wenzhao Zheng. Jiahe Guo, Yuqi Wu, Jie Zhou, and Jiwen Lu. Streaming 4D visual geometry transformer. arXiv preprint (xXx::2577.1139, 2025.
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APPENDIX
QUALITATIVE RESULTS
To further assess the generalization ability of PAGE-4D, we apply it to diverse set of in-the-wild video sequences, as illustrated in Fig. These examples span wide range of dynamic scenar- ios, including human activities, object interactions, and complex background motions. We observe that PAGE-4D consistently produces stable and accurate predictions across all cases, effectively handling both static and highly dynamic regions. Notably, the method demonstrates strong robust- ness even under challenging conditions such as occlusions, fast motion, and varying illumination, highlighting its applicability beyond controlled benchmarks and into real-world video data.
7 ACKNOWLEDGMENT
All video materials used in this study were obtained from Pexels (https:/www.ppxxls.coom and are distributed under the free Pexels License. While the license does not require individual attribu- tion, we would like to acknowledge and thank the Pexels creators whose work contributed to the preparation of our figures and demonstrations.
8
ARCHITECTURE DESIGN
8.1 CHOICE OF MIDDLE-LAYER FINE-TUNING STRATEGY
To better understand the computational characteristics of our baseline, we analyze the param- eter distribution VGGT (Wang et al. 2025a), as summarized Tab, 6 The majority of parameters are concentrated in the global attention blocks, which dominate both representa- tional capacity and memory footprint. In contrast, the camera, depth, and point heads account for only small fraction of the parameters, suggesting that most of the network capacity is devoted to learning general- purpose spatiotemporal features rather than task-specific decoding. This observation leads to two key insights. First, fine-tuning the en- tire VGGT backbone is computa- tionally inefficient: updating all attention layers substantially in- creases runtime and storage costs without yielding proportional gains.
Table Parameter distribution across modules. "M" de-
notes millions of parameters.
Module
Depth
Point
Track
Camera Aggregator
Parameters 32.7M 32.7M 65.9M 216.2M 909.1M
Second, since most parameters reside in global attention, se- lectively adapting the layers most sensitive to dynamic content is more effective way to exploit the backbone's capacity.
Table 7: Study of Different Masking Strategies Applied to VGGT. This experiment conducted on the Odyssey dataset. We evaluate unscaled pose estimation using Relative Transla- tion Error (RPE trans) and Relative Rotation Error (RPE rot). For static regions, Static-D denotes the Absolute Depth Error, and Static-T T represents the Average Endpoint Error (EPE) for 2D point tracking.
To probe VGGT's handling of dy- namic regions, we examine the role of attention maps within the global attention blocks specificaly at layers 4, 11, 17, and 23 which also provide inputs to the geometry decoder. To assess their contribu- tion, we sequentially replace each layer's output with random noise and report the results in Tab. 7 Since only the last layer is fed into the camera head, randomizing other layers does not affect camera estimation performance; therefore, we omit camera estimation results here. The results show that the th layer exerts the strongest influence on geometry quality, underscoring the non-uniform importance of layers. Motivated by these findings, we propose targeted fine-tuning strategy: adapting only
Method
RPE trans
RPE rot 1.
Static-D
Static-T
Normal Input-MSS
0.244
0,942
0.085
17.071
0.246
1,006
0.099
17.866
DD-MSK
0.243
0.869
0,566
24.797
4th
0.114
18.821
11th
0.095
18.403
17th
1,663
39.841
23rd
0.103
17.645
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Frume!
Mask
Frame
Mask 2
Figure 6: Visualization of learned masks. Our dynamic mask prediction module effectively cap- tures dynamic content in the scene without explicit supervision. (Best viewed in PDF)
the middle 10 attention layers, which are most responsive to dynamic content. This achieves per- formance comparable or better than full fine-tuning, while substantially reducing computational overhead.
During training, we observe that keeping the dynamic mask throughout the entire optimization pro- cess, or applying the mask only in the first stage and removing it in the second stage, yields similar performance. Notably, both strategies consistently outperform the baseline model trained with the original backbone without masking. Therefore, in our implementation, we provide both variants.
8.2 DESIGN OF POSM-TEOMETRY DISENTA NAITTANNNEMENT
Figure b) shows that dynamic objects consistently receive lower attention weights, indicating the model Tearns to suppress them rather than incorporate their motion. We therefore test baseline strategies inspired by prior work on motion-aware modeling Chen et al. (2025):
Input-MSK (Input masking): Mask out dynamic regions at the image input level.
DD-MSK (Dynamic-Dynamic masking): Suppress attention among dynamic tokens them- selves.
Specifically, the DD-MSK strategy aims to mitigate the instability caused by excessive interactions within dynamic regions. Let Mynamic E BS P) denote the dynamic feature mask, where all patch tokens corresponding dynamic regions are preserved. The DD-MSK is then constructed
MDD.MSK MMDnaamc
.MDynamie
which blocks self- attention among dynamic patches while still allowing them attend static ones. In practice, this prevents dynamic regions from reinforcing noisy patterns, leading to more stable pose estimation and geometry reconstruction.
As shown in Table DD-MSK improves pose estimation by isolating reliable motion cues, but simultancously degrades geometry estimation, which requires integrating both static and dynamic motion signals. This observation aligns with architectural design: dynamic information should be disentangled across tasks rather than globally suppressed.
Summary. Our structural and empirical analysis supports two conclusions: 1. Middle attention layers are more influential. Perturbation analysis highlights the dominance of deeper global atten- tion layers, particularly the 17th motivating fine-tuning only the middle 10 layers. 2. Rigid masking suboptimal. Suppressing dynamic patches improves pose estimation but harms geometry recon- struction, confirming the need for task-specific disentanglement.
TRAINING DETAILS
We train our model on diverse mixture of dynamic and static datasets, including Odyssey, Dynami- cReplica, Kubric-MV, Spring, CO3D. Waymo, Sintel and RLBench, with a total of approximately 2.39M sampled sequences as Tab. f To balance domain diversity, we assign per-dataset sampling multipliers and cap the number of training batches per epoch. Images are resized to 518 x 518 with patch size 14. We adopt AdamW with an initial learning rate of I x 10-5, weight decay 0.01, and gradient clipping of 1.0. Mixed precision (bfloat16) training is used to improve efficiency. Follow- ing our middle-layer fine-tuning strategy, we freeze the shallow and final blocks of VGGT while updating only the middle 10 global attention layers, which we identify most sensitive dynamic content. The multitask loss combines camera, depth, and point supervision with weights of 5.0, 1.0, and 1.0, respectively.
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Published as a conference paper at ICLR 2026
10 ARCHITECTURE ANALYSIS
To further evaluate the effective- ness of our dynamic mask predic- tion module, we visualize the pre- dicted masks on sequences from the Odyssey dataset. As shown in Fig. 6 the learned masks suc- cessfully highlight moving objects such as people and vehicles, while leaving static backgrounds largely unmarked. Importantly, this sep- aration emerges without any plicit supervision, indicating that the model is able infer dynamic regions purely from motion cues and spatiotemporal inconsistencies. This validates our design choice: the dynamic mask prediction module provides reliable mechanism to disentangle dynamic and static content, thereby improving the robustness of pose estimation and geometry reconstruction in challenging dynamic scenes.
Table 8: Datasets used in the fine-tuning process. Dy- namic indicates whether the dataset contains dynamic objects. Frames and Scenes denote the number of image frames and unique object-centric scenes. Ratio is the scene-level sam- pling multiplier used to balance datasets during training.
Dataset
Dynamic
Realistic
Frames
Scenes
Ratio
Reizenstein
PointOyssa Zhonn e
N723
6X
131
10%
Kubrio-MY MV Grej)
7BK.
3K
10%
Dynamic
Spring Mehl etai
200K
37
10%
Wayme Son et dL
230K
1.1K
20%
Rl.Bench
240K
2K
10%
18

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Published as a conference paper at ICLR 2026
Figure 7: PAGE-4D takes sequence of RGB images depicting dynamic scene input and simultancously predicts the corresponding camera parameters and 3D geometry information all within a fraction of second. (Best viewed in PDF.)
19


Reviews:
[{"review_id": "oB1gzTEbp2", "reviewer": "Reviewer_5Axf", "summary": "This paper introduces an extension of VGGT that incorporates dynamic object masks. The core idea is to predict these masks and then inject them additively into the middle layers of the VGGT architecture. The authors demonstrate the effectiveness of this approach through experiments on five benchmarks involving dynamic objects: video depth estimation, monocular depth estimation, camera pose estimation, multi-view point map reconstruction, and 4D view synthesis.", "questions": "Please see the Weaknesses. I am willing to change my score provided the authors' answer my questions.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "+ This paper presents a straightforward yet effective enhancement to the VGGT architecture. The method stands out because it leverages a simple idea without requiring extensive fine-tuning, making it practical for integration. \n+ An advantage is its self-supervised mask prediction, which eliminates the need for any explicit mask supervision. \n+ The approach demonstrates substantial improvements across several dynamic benchmarks, underscoring its efficacy in handling complex scenarios.\n+ The writing is clear and easy to understand.", "weaknesses": "- The decision to use an additive mask within the mask attention mechanism, as seen in Equation (6), warrants further clarification. It would be beneficial for the authors to quantitatively report and explain why a multiplicative mask, which could directly attenuate irrelevant attention, was not considered.\n\n- To fully understand the mask's efficacy, a quantitative evaluation of the predicted mask accuracy is crucial. Assessing this at different layers of the global attention on the Odyssey test set, using a metric like IoU2D, would provide valuable insight into whether the network is genuinely learning effective masking.\n\n- The claim on Line 150 regarding the performance gap between static and dynamic regions on the Odyssey test set, where \"Absolute Depth Error in dynamic regions is 94% higher than in static regions,\" is not supported by a corresponding table or explicit results. This table should be included, and it would be insightful to see how this performance gap changes when the proposed Page4D method is applied to the Odyssey dataset.\n\n- Given that the current results focus solely on dynamic scenes, it is important to understand the impact of Page4D on static scene performance. Therefore, I recommend quantitatively reporting the performance of Page4D on a selection of tasks and datasets that include static scenes, such as:\n  - Camera Pose Estimation on the RealEstate10K and Co3Dv2 datasets (as presented in Table 1 of the VGGT paper).\n  - Dense MVS Estimation on the DTU dataset (as presented in Table 2 of the VGGT paper).\n  - Point Map Estimation on the ETH3D dataset (as presented in Table 3 of the VGGT paper).\n  - Novel View Synthesis on the GSO dataset (as presented in Table 7 of the VGGT paper).\n  - Dynamic Point Tracking Results on the TAP-Vid dataset (as presented in Table 8 of the VGGT paper). This would provide a more complete picture of the method's overall capabilities.", "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": "LqUY5Tm6Pj", "reviewer": "Reviewer_31BG", "summary": "The task of this paper is to jointly predict point clouds and camera poses for each image frame in a sequence of video frames depicting dynamic scenes. The proposed method, called PAGE-4D, is built upon the VGGT neural network architecture. The authors observed that dynamic contexts significantly affect the accuracy of camera pose estimation, while having a smaller impact on point cloud estimation. They also found that the middle layers of VGGT exhibit lower attention weights on dynamic pixels.\n\nBased on these observations, the authors propose a creative model design and fine-tuning strategy. In particular, they introduce an attention mask prediction module that suppresses attention weights for camera prediction tokens while leaving geometric tokens unaffected. This is an idea consistent with their empirical findings. To improve parameter efficiency during fine-tuning, they further propose a strategy that updates only the middle 10 layers of the network.\n\nTable 1 shows that PAGE-4D achieves state-of-the-art performance across multiple datasets, including Sintel, Bonn, and DyCheck. The ablation study demonstrates that fine-tuning only the middle 10 layers yields results comparable to full fine-tuning, and it also verifies the effectiveness of the proposed suppression mask.\n\nOverall, this is a well-motivated neural network design with solid experimental results and clear presentation. I believe this paper is strong enough to be accepted at ICLR, though I have a few minor concerns discussed below.", "questions": "- What is the GPU memory consumption during inference compared to VGGT?\n- How does the fine-tuned model perform on static scenes, compared to VGGT?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Good observation and thoughtful neural network design, leading to strong experimental results.\n- The presentation is clear, and the supplementary materials are comprehensive. I also appreciate that the authors have provided the code.\n- Overall, this is a well-structured and solid research paper. It identifies a problem, derives insights through careful observation, and proposes specific design choices based on experimental findings. Reading the paper feels smooth and coherent.", "weaknesses": "Looking at Figure 2(b), I feel that the content does not align well with the caption. The caption states that the VGGT layers have smaller attention weights on dynamic pixels. However, only one image (the 11th layer) among the four shown appears consistent with this description. In the other three images in Figure 2(b), there does not seem to be any noticeably high attention values on dynamic pixels. Could the authors double-check this? Or is it possible that the observation is not entirely accurate?", "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": "G6pEbMsf5X", "reviewer": "Reviewer_gSDj", "summary": "The paper introduces a dynamics-aware aggregator that separates static and dynamic information through a learned dynamics-aware mask.\n\nRather than treating motion as uniformly harmful or helpful, the authors make its influence task-dependent. The aggregator first predicts a mask that highlights dynamic regions, then feeds it into a cross-attention module—suppressing dynamic cues for camera-pose tokens while preserving them for geometry tokens.\n\nCombined with self-supervised fine-tuning of layers most sensitive to motion, this design exploits dynamics where they benefit geometry grounding and mitigates their adverse impact on pose estimation.\n\nAs a result, the method achieves accurate pose and geometry estimation across both static and dynamic scenes.", "questions": "Line 025: Denser point clouds\n• PAGE-4D reportedly reconstructs point clouds that are markedly denser than those of VGGT. Could you clarify which part of the paper drives this improvement? \n\nOverlap with Easi3R (Lines 146 – 176)\n• Section 3.1 and Fig. 2 echo the core finding of Easi3R—namely, that dynamic regions exhibit weaker activations and should be down-weighted for pose estimation. Yet Easi3R is not discussed in the Introduction, Related-Work, or Method sections.\n• Please elaborate on:\n– Conceptual similarities and differences between PAGE-4D and Easi3R.\n– The technical novelty beyond replacing DUSt3R with VGGT and moving to a feed-forward mask predictor.\n\n“Suppress vs. amplify” inconsistency (Lines 296 – 298)\n• The abstract states that motion cues are suppressed for pose estimation and amplified for geometry reconstruction. In the method description, however, the mask is applied in pose-estimation layers and left untouched for geometry reconstruction (i.e., neither suppressed nor amplified). Could you clarify this discrepancy and specify where “amplification” occurs?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Experiments demonstrate that PAGE-4D consistently surpasses the baseline VGGT in dynamic scenes, yielding superior results on camera-pose estimation, monocular and video depth estimation, and dense point-map reconstruction.\n\nThe authors devise a dynamic-mask prediction module that learns, in a self-supervised fashion, which spatial regions are likely to contain dynamic objects.\n\nThe model predicts the mask in earlier layers and injects it into later attention layers for pose estimation, enabling a single feed-forward pass; in contrast, methods such as Easi3R require a second forward pass.\n\nOwing to its plug-in design, PAGE-4D introduces only negligible runtime and memory overhead compared with VGGT.\n\nThe analyses presented in Table 6 and Table 7 are particularly insightful.", "weaknesses": "Relationship to Easi3R is under-explained. Lines 146–176 (Sect. 3.1 and Fig. 2) essentially restate the key observation of Easi3R—that dynamic regions show weaker activations and should be suppressed for pose estimation—yet the manuscript does not discuss this connection in the Introduction, Related-Work, or Method sections. As presented, PAGE-4D reads like a VGGT-based version of Easi3R (originally built on DUSt3R), augmented with a feed-forward mask predictor. A clearer positioning with respect to Easi3R is needed, including a discussion of conceptual overlap and technical differences.\n\nQuantitative comparisons are incomplete. Table 1 omits several recent dynamic-scene baselines—Easi3R, AETHER, Geo4D, and ViPE—making it difficult to gauge the real performance gap.\n\nTables 2 and 3 report only Easi3R(DUSt3R) numbers; the Easi3R(MoNSTR) variant is absent. Including it would give a fairer picture of state-of-the-art accuracy.\n\nTable 4 why not include MoVieS?\n\nThe paper does not assess dynamic-object segmentation. A DAVIS-style evaluation (as in Table 1 of Easi3R) would verify whether the predicted masks align with true motion regions and would offer further evidence for the quality of the dynamics-aware module.", "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": "WSGEI2be0u", "reviewer": "Reviewer_exiX", "summary": "The work proposes an extension of VGGT for improved accuracy of reconstruction and camera poses on dynamic scenes.\nThe idea is rather simple: using masked attention to ignore the patches of dynamic areas from pose estimation.\nEmpirically, the work finds that fine-tuning only 10 middle layers with masked attention is effective.\nExperiments show a notable improvement of depth estimation on three datasets and some improvement for the camera pose.", "questions": "* I would be curious to see evaluation results of VGGT* (Middle Layers) in other settings of Tab. 1 (e.g. monocular depth). The goal is to disentangle the contribution of the masked attention alone.\n* It is somewhat bizarre that masked attention, which affects the attention only for the camera token, benefits depth estimation more than the camera pose results in Tab. 2, which are mixed. I would be great if the authors could elaborate.\n* l. 257 “in a self-supervised manner” – isn’t the network supervised by the camera poses and depth?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* The idea is simple and requires only a partial model finetuning to take effect. This allows for adopting the VGGT architecture as a whole, preserving some of its desirable properties (e.g. runtime).\n* The work does a nice job at motivating the approach (c.f. Sec. 3.1).\n* Experiments span a compelling spectrum of benchmarks - depth and camera pose pose estimation, point reconstruction and novel view synthesis.", "weaknesses": "* The overall technical contribution — introducing masked attention and fine-tuning the model on dynamic data — is rather minor.\n* The improvements shown in Tab. 1 are convincing, but it is not exactly clear whether they stem from the finetuning per se, or masked attention. This closely relates to the next point.\n* Tab. 5 is rather short for an ablation study and reveals two observations: 1) on some datasets, the improvement may come already from fine-tuning, without introducing masked attention 2) the benefit of masked attention appears marginal on some benchmarks (e.g. DyCheck).", "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)