UFO-4D: Unposed Feedforward 4D Reconstruction from Two Images
Authors: Junhwa Hur
Charles Herrmann
Songyou Peng
Philipp Henzler
Zeyu Ma
Todd Zickler
Deqing Sun
Venue: ICLR 2026 Poster (2026)
Keywords: Feedforward dense 4D reconstruction; 4D interpolation; 3D reconstruction; scene flow estimation
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: UFO-4D predicts dynamic 3D Gaussians and camera poses from a pair of unposed images in a single feedforward pass, enabling rendering of image, 3D geometry, and 3D motion at any intermediate view or timestamp using just one model.

Abstract: Dense 4D reconstruction from unposed images remains a critical challenge, with current methods relying on slow test-time optimization or fragmented, task-specific feedforward models. We introduce UFO-4D, a unified feedforward framework to reconstruct a dense, explicit 4D representation from just a pair of unposed images. UFO-4D directly estimates dynamic 3D Gaussian Splats, enabling the joint and consistent estimation of 3D geometry, 3D motion, and camera pose in a feedforward manner. Our core insight is that differentiably rendering multiple signals from a single Dynamic 3D Gaussian representation offers major training advantages. This approach enables a self-supervised image synthesis loss while tightly coupling appearance, depth, and motion. Since all modalities share the same geometric primitives, supervising one inherently regularizes and improves the others. This synergy overcomes data scarcity, allowing UFO-4D to outperform prior work by up to 3 times in joint geometry, motion, and camera pose estimation. Our representation also enables high-fidelity 4D interpolation across novel views and time. Please visit our project page for visual results: https://ufo-4d.github.io/

Published as a conference paper at ICLR 2026
UFO-4D: UNPOSED FEEDFORWARD 4D RECON- STRUCTION FROM Two IMAGES
Junhwa Hur!
Charles Herrmann' Deging Sun'
Songyou Peng
Philipp Henzler!
Zeyu Ma²
Todd Zickler?
Google Prrinceton University
Harvard University
Input image pair
4D representation
Rendered outputs
Dynamic 3D Canasias in the canenical space
Renákra
legst
goundrry
UFO-4D
3D (000:00
Camera pose
Figure I: Given pair of unposed images, the proposed UFO-4D outputs dynamic 3D Gaussians in the canonical space and relative camera pose in a feedforward manner. This explicit 4D represen- tation can solve various downstream tasks such as 3D geometry (point, depth) and motion (scene flow, optical flow). Besides, it can interpolate image, geometry, and motion at novel view and time.
ABSTRACT
Dense 4D reconstruction from unposed images remains critical challenge, with current methods relying on slow test-time optimization or fragmented, task- specific feedforward models. We introduce UFO-4D, a unified feedforward frame- work to reconstruct dense, explicit 4D representation from just pair of unposed images. UFO-4D directly estimates dynamic 3D Gaussian Splats, enabling the joint and consistent estimation of 3D geometry, 3D motion, and camera pose feedforward manner. Our core insight is that differentiably rendering mul- tiple signals from a single Dynamic 3D Gaussian representation offers major training advantages. This approach enables a self-supervised image synthesis loss while tightly coupling appearance, depth, and motion. Since all modalities share the same geometric primitives, supervising one inherently regularizes and improves the others. This synergy overcomes data scarcity, allowing UFO-4D to outperform prior work by up to 3x in joint geometry, motion, and camera pose estimation. Our representation also enables high-fidelity 4D interpolation across novel views and time. Please visit our project page for visual results: nttps://ufo-4d. 4d. thub. io/.
INTRODUCTION
Joint estimation of camera pose, 3D geometry, and 3D motion a task known as 4D scene re- construction from casually captured images is a fundamental challenge computer vision with applications in robotics (Xu et al., 2024), autonomous driving (Geiger et al., 2012), and 3D/4D gen- erative AI (Liang et al., 2024; Xie et al., 2025; Yu et al., 2024). Recovering dense 4D information from limited 2D inputs, however, is an inherently ill-posed problem. While data-driven approach can be solution, a challenge continues with a scarcity dense, large-scale 4D training data. Syn- thetic datasets (Zheng al., 2023) provide dense. pixel-perfect supervision, yet they often suffer from a significant domain gap and a lack of diversity. Real-worlh data (Jin et al., 2025) generally constrained sparse and noisy annotations, limiting its effectiveness for training robust models.

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Published as a conference paper at ICLR 2026
Consequently, dynamic scene reconstruction has traditionally centered on slow, test-time optimiza- tion pipelines This approach relies on intermediate 2D signals such as depth and optical flow, which imposes high computational cost and caps performance at the quality of these inputs. A recent shift towards feedforward models, including DUST3R (Wang et al., 2024b), MonST3R (Zhang et al,, 2025a), and DynaDUS:3R (Jin et al., 2025), has yielded impressive results individual perception lasks; however, a single, unified feedforward architecture capable of holistically addressing the full spectrum of 2D and 3D perception tasks does not yet exist. This lack of unified representation prevents models from exploiting the tightly coupled nature of geometry and motion.
To address these limitations, we introduce UFO-4D, a unified feedforward model for dense 4D re- construction from just two unposed images. UFO-4D predicts an explicit and dense representation, Dynamic 3D Gaussian Splatting (D-3DGS), in single forward pass. This unified representation enables the consistent, integrated estimation of multiple 2D and 3D reconstruction tasks, including depth, scene flow, and optical flow, all from the same underlying model.
Furthermore, our unified representation unlocks two key advantages during training. First, differ- entiable rendering provides a dense, self-supervised photometric loss that is independent of sparse or noisy ground-truth labels. Second, because 3D geometry and motion originate from the same set of Gaussian primitives, their respective losses are tightly coupled in 3D. This coupling creates a regularizing effect where each supervisory signal helps compensate for imperfections in the other. As a result, UFQ-4D achieves state-of-the-ar performance on geometry and motion benchmarks, achieving more than 3x lower EPE Stereo4D and KITTI than competing methods. Furthermore, our explicit 4D representation enables novel applications, including high-fidelity spatio-temporal interpolation of appearance, geometry, and motion from a single prediction.
Our primary contributions are:
A unified model for unposed feedforward 4D reconstruction from two images using dy- namic 3D Gaussian Splatting representation.
A robust cupervupeviiioon framework that leverages differentiably rendered outputs to mit- igate the scarcity of annotated data.
4D spatio-temporal interpolation of image, depth, and motion as a new application of the feedforward output.
State-of-the-art performance on 3D geometry and 3D motion benchmark datasets.
2 RELATED WORK
3D reconstruction of static scenes. The field of 3D reconstruction of static scenes, such as multi- view geometry (Bae et al., 2022; Ding et al., 2022; Zhang et al., 2023), structure from motion (SIM) (Lindenberger et al., 2021; Pan et al., 2024; Wang et al., 2024a), and simultaneous localization and mapping (SLAM) (Lipson et al., 2024; Teed & Deng, 2021; Zhu et al., 2024), has been also significantly advanced by learning-bass based approaches. Canonical approaches (Agarwal et al., 2011; 1; Furukawa al., 2010; Schonberger & Frahm, 2016) rely projective geometry, such as feature extraction, triangulation, bundle adjustment, etc. However, learning-based approaches demonstrate that each component in the pipeline can be advanced by data-driven approaches. Even the whole pipeline can be end-to-end (Elflein et al., 2025; Liang al., 2025a; Wang & Agapito, 2025; Wang et al., 2025a; 2024b; Weinzaepfel et al., 2023; Zhang et al., 2025b) when networks learn strong priors from large-scale data. However, these methods specifically target static scenes that satisfy the epipolar geometry constraint.
well-established pipeline based on
3D reconstruction of dynamic scenes. Traditional approaches (Kumar et al., 2017; Luiten et al.,
2020; Mustafa et al., 2016) often tackle this problem using multi-stage pipelines that combine given depth and motion cues and optimize the main objective using rigidity or piecewise smoothness priors about geometry and motion. Recent approaches demonstrate that this highly ill-posed problem can simply be solved by learning strong priors on geometry of dynamic scenes. Those strong prior can be obtained by light-weight finetuning (Lu et al., 2025; Zhang et al., 2025a) of static 3D reconstruction model (Wang et al., 2024b), large-scale training (Wang et al., 2025c), or repurposing generative model (Mai et al., 2025). While these feedforward methods generate per-frame pointclouds, they lack temporal correspondence, limiting their application to motion understanding tasks.
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Dense 4D reconstruction. Dense 4D reconstruction methods mainly fall into two categories. One line of work relies on per-seene test-time optimization (Lei et al., 2025; Liu et al., 2025; Wang et al., 2023; 2025b). The approaches show high-fidelity reconstruction, but often take hours to run and depend on pre-computed cues such as camera poses and optical flow. To enable real-time applica- tion, other feedforward approaches have been proposed (Feng et al., 2025; Han et al., 2025; Liang et al., 2025b; Sucar et al., 2025), represented in sparse pointmaps. Concurrent works are primarily designed for novel view synthesis and require camera poses at test time (Lin et al., 2026; 2025; Xu et al., 2025b), while others require training separate heads for each downstream task (Badki et al., 2026). In contrast, our method reconstructs a dense and explicit 4D representation a feedforward manner without requiring camera poses as input. Moreover, our holistic approach is suitable for both reconstruction and synthesis, demonstrating that synthesis improves reconstruction tasks.
Dense 4D datasets. The lack of large-scale, densely annotated 4D datasets is a major bottleneck for the development of dense 4D reconstruction. While synthetic data (Zheng et al., 2023) suf- fers from a domain gap, the recent real-world Stereo4D dataset (Jin et al., 2025) provides valuable supervision, though its annotations remain sparse and can be unreliable occluded or distant re- gions. UFO-4D, which uses unified dynamic 3D Gaussian representation, effectively leverages self-supervision to overcome the limitations of sparse ground truth annotations in existing datasets.
FEEDFORWARD 4D RECONSTRUCTION FROM TWO UNPOSED IMAGES
Our method, UFO-4D, performs feedforward 4D reconstruction from pair of unposed images. As in Fig. 1, given two images and their camera intrinsics, UFQ-4D directly predicts the relative camera pose and set of dynamic 3D Gaussians in the coordinate system of the first camera. This explicit 4D representation enables not only the synthesis of novel views and interpolated time steps, but also the direct rendering of various geometric and motion attributes, e. g., 3D point and 3D scene flow. As result, we can leverage both view synthesis and downstream reconstruction tasks for supervision.
Formulation. Given input unposed images I and I++1, our model fo maps them to the scene representation (G, P), consisting of set of dynamic 3D Gaussians G and relative camera pose P:
(I) where the set D(I) denotes a set of pixels in the image Each dynamic 3D Gaussian (Luiten et al., 2024) comprises its 3D center μ RR3 3D motion v E RR,, covariance matrix parameterized by quaternion rotation r E R.1 and size $ E RR, view-dependent color represented by spherical harmonics in each color channel h E R& and opacity o [0. Our model estimates one 3D Gaussian for each pixel from each image 1, and I with the 3D motion v for Gaussians of I, being forward motion (t-f++1) and those for I--1 being backward motion (++1-+t). Before rasterization, we translate the 3D center of Gaussians for I1+1 with their motion e. v+v and inverse their motion vector (i. e. -v) to align with the Gaussians for I, to be at the time step. The union of the Gaussians from the two images explicitly represents the dense 4D scene elements that are visible in one or both images. All of the Gaussians G and the relative camera pose P defined in the coordinate of first image I, which serves as the canonical space. Following (Ye et al., 2025), we additionally input camera intrinsies, which are commonly available in commercial devices.
1+1 + (G, with G = {(H, v, r, s, h, o)p p E D(I,) U D(I++)))
Network architecture. Figure 2 presents an overview of our network architecture, inspired by DUS(3R (Wang et al., 2024b) and NoPoSplat (Ye al., 2025). The weight-sharing encoder (Doso- vitskiy et al., 2021) processes each input image into image tokens separately. The encoded tokens are concatenated with an intrinsic token and a pose token and then fed into ViT-based decoder. The intrinsic token is obtained after feeding camera intrinsics into a linear layer (Ye et al., 2025). Pose token a learnable parameter that is optimized during training. Here, cross-attention layers within each attention block are responsible for matching and integrating information between the two input images (An et al., 2025; Chen et al., 2025).
Next, we connect heads to read out parameters of dynamic 3D Gaussians for each image and a rela- tive camera pose as defined in Eq. (1). The center head predicts the 3D center of each Gaussian. The attributes head predicts its quaternion rotation r, scale s, spherical harmonics h and opacity o; and a velocity head predicts its 3D motion vector v. These heads use a DPT-based architecture (Ran- ftl et al., 2021) with different output channel dimensions. The pose head, 3-layer MIP,, predicts
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Imiissies taken
Posc tiken
Dynamic 3D Gaussian
VII Encoder
VII Decoder
Attrbuuics bead
Diffsreniiblee
Velocity head
40 rasteniatiin
Image
Weight: shared
Cross artemion
VII Encoder
+
VII Docoder
Altibutics head
Dynamic 3D Gonasians
Velocity head
cimonical comera coondinate
Image 1+|
imnisics takon
Post
Sitee
at novel view and time
Figure 2: Network architecture. Given pair of input images I and I++1 and camera intrinsics K, UFO-4D outputs parameters for dynamic 3D Gaussians and relative camera pose in a feed-forward manner. Given the estimates, UFO-4D can render image, point, and motion at any interpolated time and view. While the intrinsic token needs the real camera intrinsics, the pose token is a learnable parameter and does not require the inference-ting pose input.
Rasterized motion
Rasterized 3D point
Camera ray
at
1'= 1 At
(a) Translation
(b) Rasterization
Figure 3: (a) Each Gaussian is translated with its motion to represent 3D scene at time At. (b) Point and motion as well as an image are rasterized together.
the relative camera pose comprising a translation 7 and quaternion q. The estimated pose used for rasterizing image, depth, and motion at time + during both training and inference time. This direct estimation of pose not only makes inference-ting post regression (Wang et al., 2024b; Zhang al., 2025a) unnecessary but also gives better accuracy (Table G in Appendix).
Differentiable 4D rasterization. Our method relies on unified and differentiable rasterization process that is critical for both model training and downstream 4D perception tasks. We extend the standard 3D Gaussian rasterizer render not only color images but also dense pointmaps and scene flow at any intermediate time [t, t 1]. achieve this, we first model the scene at a continuous time t' t + At (where At [0, 1)) by assuming linear motion. The set of Gaussians (t'') is formed by updating the position of each predicted Gaussian along its velocity vector:
g(t') = ((p + Atv, V, v, c, o)p..
(2)
with p E D(L.)
The temporal evolution is shown in Fig. 3a. To render an image, we then follow the standard alpha- blending pipeline from 3DGS (Kerbl et al., 2023; Luiten et al., 2024). For given camera view, the color I (p) at each pixel is computed by a-blending of N -oordered depth-sorted Gaussians along each ray:
ip(p)
Σ c, II (1-)), j=1
SENY
where the set contains all the Gaussians that contribute to pixel p. c; is the view-dependent color derived from spherical harmonics, and is the opacity computed from the projected 2D Gaussian. Note that, depth orders of each Gaussian can change with At, and this rasterization process naturally handles occlusion and disocclusion.
A key aspect of our method is applying this rendering principle to geometry and motion. While prior work often only the sparse Gaussian primitive centers (Luiten et al., 2024), produce
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dense, geometrically consistent maps by substituting the color c; in the blending formula with other intrinsic Gaussian attributes. Toward more precise and dense reconstruction of 3D surface and motion, we render points X E ER' and 3D scene flow maps Ve R follows:
(p) =iieyy io [T (1 0j),
(4a)
(P) = EiENy voo, I1 (1-og).
(4b)
This unified approach, visualized in Fig. 3b, is fully differentiable (Matsuki et al., 2024). The critical advantage is that it allows supervision signals from rendered images, point maps, and flow maps to be backpropagated through the rasterizer, enabling robust, joint optimization of all heads with single framework.
Loss. Our method takes a semi-supervised learning approach. The total loss Lanaa consists of supervised loss Lsup and a self-supe loss Liett'
Luoial
Luup + Iselt-
(5)
The supervised loss Lup penalizes the differences between predictions and ground truth for motion, point, and pose:
Ssu =
Lunotion + Upoint Lpoint + Upose Lpose
(6a)
Lmottonn -Eeeft,+1 M (llv, VGT II
v 1)
(6b)
Lepint
Eme(t,++))
[ (llpke X X
X
(6c)
Liuce = ||q derl| + ||T TGT
(6d)
We apply Lmotion at each Gaussian center's motion as well rasterized motion V, normalized
by the number of valid ground truth V and the same for Tppoint We enforce Tpese for translation and quaternion separately. For datasets with sparse annotated ground truth, we apply the losses the outputs and pixels with valid ground truth.
We also include two self-supervised losses on rasterized images, rasterized point and motion:
Leelf = Lphoto + Wsmooth Lsmothh:
(7a)
Lehhtt Ene(r,++1) (msc(iu, Iu) + Wippps Iu)),
(7b)
Lemoothh Ene (x.( (e 10,
18,1ull),,
(7c)
where photometric loss applies mean square error (MSE) and LPIPS (Zhang et al., 2018) loss be- tween input images (I, and 1++1) and respective rasterized images (i, and I,+1). The smoothness loss that encourages spatial smoothness on predicted outputs is applied at rasterized motion, V, and V V(+1. and rasterized point maps, X, and X,+1, based on an edge-aware smoothness loss (Godard al., 2017; 2019). We find that the smoothness loss effectively clean up floaters in rasterized point and motion.
Downstream 2D/3D tasks. By default, our method estimates outputs for 3D perception tasks, e., point, 3D scene flow, and camera poses. From them, 2D perception tasks are naturally solved by projecting them into 2D space. Depth is the last channel of estimated point, optical flow obtained by projecting 3D scene flow, and moving objects are segmented by thresholding the scene flow.
4D interpolation. The dynamic nature of our representation enables full 4D interpolation. Given estimated set of dynamic 3D Gaussians, method interpolate image, point, and motion between two time steps at any camera view point inbetween. As described in Eq. (2), interpolated 3D scene be represented (('') by translating each 3D Gaussians by its motion. We can then simply rasterize image, point, and motion at any view based on the rasterization Eqs. (3) and (4b),
4 EXPERIMENTS
Implementation details. For training, we use a mixture of Stereo4D (Jin et al., 2025), PointOdyssey (Zheng et al., 2023), and Virtual KITTI 2 (Gaidon et al., 2016) by sampling each
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Table I: Geometry estimation: We report end-point error (EPE) for pointmap accuracy and abso- lute relative error (Abs. Rel.) and 8<21.25 for depth accuracy. Lower is better for EPE and Abs, Rel., and higher is better for 8<1.25.
Stereo4D
Born
KITTI
Sintel finul
Method
EPE
Abs. Rel. 6.1.25
EPE
Abs. Rel.
6..2.2
EPE
Abs. ReL.
6<11.2
EPE
Abs. Rel. 1,25
MAS:3 (Leroy 2024)
1,887
0.314
66.50
0.487
0.174
76.49
2.304
0.087
92.04
3.997
0.366
61.03
MonST3R (Zhang al., 2025a)
1.504
0.191
74.75
0.188
0.062
96.22
2.797
0.107
87.61
3.607
0.341
57.61
DSnaDUSI3R (Jin at. 2025)
0.811
0.112
86.07
0,084
92.75
4.362
61.04
ZeroMSF (Liang et al., 2025b)
I662
0.220
76.18
0.208
0.079
91.58
3.792
0.107
92.28
3.637
0.333
63.86
St4RTaakk (Feng et al,, 2025)
1,467
0.209
71.26
0.067
95.17
4.731
0.125
82.86
4.145
0.357
UFO-4D (Ours)
0.659
0.106
88,38
0.162
0.064
96.04
2.568
0.090
88,92
3,866
0.319
61,94
Table 2: Motion estimation. We evaluate the scene flow accuracy on the Stereo4D test split (Jin al., 2025) and KITTI Scene Flow 2015 Training (Menze et al., 2018). Our approach substantially outperforms others, achieving the best numbers on all metrics.
Stereo4D
KIT'11
Method
forward
backward
forward
EPEn
8 1
EPEm J. 6%)
EPEip
6
DynaDUSt3R (Jin et al., 2025)
0.175
51.46
0.161
52.16
0.463
1.09
ZeroMSF (Liang et al., 2025b)
0.164
45.63
0.442
16.20
St4RTrack (Feng et al., 2025)
0.373
26.62
0.751
4,01
UFO-4D (Ours)
0.049
83.06
0.050
82.67
0.137
47,80
dataset in a minibatch with probability of 60%, 20%, and 20% respectively. All datasets include (sparse) annotations of 3D points, 3D sceneflow, and camera pose. For efficient storage, we use 10% of the full Stereo4D dataset by subsampling frames of the dataset. We initialize our network (Fig. 2) using pretrained weights from NoPoSplat (Ye al., 2025) (Gaussian head) and MAS (Leroy et al., 2024) (rest), except for the camera head, which is trained from scratch. We train the model with varying aspect ratios with image width to be 512. We apply photometric augmentations and geometric augmentations such as random scale crop, horizontal flip, and temporal flip. We train the model for 120k iteration steps with a global minibatch size of 16. The training takes around three days on four NVIDIA A100 40GB GPUs.
4.1 EVALUATION ON 2D AND 3D TASKS
We evaluate UFO-4D on various 2D and 3D geometry and motion tasks, such as depth, point, and scene flow, and compare with recent methods that are specifically designed for joint geometry and motion estimation given pair of input frames: DynaDUSt3R (Jin et al., 2025), ZeroMSF (Liang et al., 2025b), and the concurrent work ST4RTrack (Feng et al., 2025).!
All methods are evaluated under the same protocol, following DynaDUSE3R and ZeroMSF that use per-image median scale alignment for both point and scene flow. DynaDUSt3R uses the full Stereo4D dataset for training while ZeroMSF uses mixture of synthetic datasets, including SHIFT, Dynamic Replica, Virtual KITTI 2, MOVi-F, Spring, and PointOdyssey, Please see details on the inference setup of each method in Section A.2.
Geometry estimation. Table compares the accuracy of pointmaps and depth for geometry es- timation on multiple benchmarks including Stereo4D test split (Jin et al., 2025), Bonn (Palazzolo et al., 2019), and KITTI Scene Flow 2015 Training (Menze et al., 2018). For pointmap, we report averaged end-point error (EPE) between predicted pointmap and ground truth pointmap defined at the canonical camera coordinate. For depth, we report standard depth metrics, absolute relative error (Abs. Rel.) and percentage of inlier points (6<1.)
Our method substantially outperforms all direct competitors on Stereo4D and remains very compet- itive on the rest datasets. The trade-off across datasets of each method can stem from the mixture of training datasets that the method uses (cf. Table C). For example, methods (e. g. MonST3R and
S14RTrack is primarily optimized for long-term tracking, unlike the short-term dynamies evaluated here.

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Depeh
Motion
Dopeh
Motion
(N/A)
Depth
Mation
Input puiz
DynaDUS(3R Jin al. (2025)
ZeroMSF
S14RTaak Fong et al. (2025)
UFO-4D (Ours)
Ground truth
Liang et al. (2025b)
Figure 4: Qualitative comparison of depth and projected 2D optical flow on Stereo4D, Bonn, and KITTI For motion on KITTI. it visualizes motion relative to the camera, as GT is defined. Unlike DynaDUS(3R, ZeroMSF and St4RTrack, which suffers from residual motions in static region and inaccurate motion on object boundaries, UFO-4D exhibits clear motion boundaries and separation between moving objects and background. More qualitative results are in Section A.1.
Table 3: Pose estimation. We report standard metrics, ATE and RPE translation (RPE and rotation (RPE for pose estimation on Stereo4D test, Bonn, and Sintel final, Our feed-forwatt ap- proach outperforms methods relying on iterative solvers (e. g. MonST3R, St4RTrackk on all metrics.
Sterco4D (Jin et al., 2025)
Bonn (Palazzolo al., 2019)
Sintel (Butler et al., 2012)
Methed
ATE
RPE
RPE
ATE
RPE
RPE
ATE
RPE
RPE..
MonST3R (Zhang 2025a)
0.0647
0.805
0.0031
0.544
0.0290
0.0410
1.080
Su4KTrack (Feng et al,, 2025)
0.0602
0.0851
0.729
0.0024
0.0034
0.400
0.0231
0.0326
0.884
UFO-4D (Ours)
ZeroMSF) that the Spring dataset for training usually show good performance Sintel due their narrow domain gap. Fig. 4 visualizes qualitative results of all methods. Our method estimates good depth boundaries especially where supervision signal is missing.
Scene flow estimation. Table 2 compares with methods that estimate scene flow between two monocular images. The metries includes 3D end and-point-error (EPE) between ground truth and pre- dicted estimates and the fraction of 3D points that have motion error within 5 em (80.05) compared to ground truth. Our method achieves the best accuracy on metrics, especially with more than three times lower EPE on Stereo4D and KITTI than the best method. Fig. 4 visualizes the source of the gain of our method. Unlike other methods, our method shows clear motion boundaries and disentangiement of motion between background and moving objects.
Pose estimation. Table evaluates pose accuracy on Stereo4D test split, Bonn, and Sintel Final. Given two-frame inputs, estimate relative pose and report accuracy using standard metries, in-

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High
Low
Input image
Rendered image
Rendered depth
Rendered motion
Opacity map
Opacity legend
Figure 5: Opacity as learnable confidence: Opacity maps show the model's behavior in a (dis)occlusion scenario. Our model learns assign high confidence (opacity) to disoccluded re- gions, and for mutually-visible regions, selects only one corresponding Gaussian from the two views, enabling an efficient and compact 4D representation.
cluding ATE and RPE (translation and rotation). We provide more details on evaluation protocol in Appendix. Our method achieves the best performance across all datasets. Unlike MonST3R (Zhang et al., 2025a) and St4RTrack Feng et al. (2025), which obtain pose via a PnP solver (Lepetit et al., 2009) with RANSAC (Fischler & Bolles, 1981), our approach is fully feed-forward. We demon- strate that direct estimation eliminates the need for additional post-processing while simultancously achieving higher accuracy. Further analysis in Section A.10 using PnP+RANSAC on our method confirms that our gain originates from both more accurate geometry and the robustness of our feed- forward estimator.
4.2 ANALYSES
Opacity as learnable confidence. One notable emergent property of our model is that opacity serves learnable confidence metric. Through the defined loss in Eq. (5), our feedforward model learns to predict an opacity value for each Gaussian that properly weight its contribution to the rendered image, points, and motion. Figure 5 visualizes disocclusion occurs. The last column visualizes an opacity map for each image, where blue means high opacity and therefore large contributions to rendered outputs. We see that not all Gaussians have high opacity: For regions that are visible in both views (e. g. background and the person), the model chooses of the corresponding Gaussians from the two images compactly represent the scene. Regions that are occluded in one view and become visible in the other are also highlighted high opacity (e. g. background being occluded by the person in the second view). This emergent property allows the model to autonomously attenuate the influence of less relevant Gaussians for efficient 4D scene representation.
example where heavy occlusion and
Loss ablation. In Table 4, we analyze how using 3D Gaussians as representational bottleneck encourages synergy between tasks during joint training. We train the models on the Stereo4D dataset at 256 x 256 resolution for 40k iterations with a batch size of 12. We report image reconstruction (PSNR) and the accuracy of both Gaussian attributes (i. e., center and velocity) and rasterized outputs for points and motion (EPE).
When gradients from the photometric loss to the Gaussian center and velocity are removed ((a) (b)), point and motion accuracy drops significantly, highlighting the importance of our photometric loss (Eq. (7c)). Also, removing the supervision on rasterized points and motion ((a) - (c)), results in substantial accuracy degradation across all tasks. Qualitative examples in Fig. 6 visualize these findings. The rendering losses for points and motion are crucial for sharp motion boundaries, which tells its significant more than numbers in the table.
model without any rendering losses (d), í. e., only estimating 3D point and motion of each pixel, performs behind our full model. This indicates that even though our network shares its capacity to
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Table Loss ablation. We report image reconstruction in PSNR and accuracy of Gaussian center (Point), rasterized point (Point rast.), Gaussian velocity (Motion), and rasterized motion (Motion rast.) in end-point error (EPE). Integrating the photometric loss gradient (Eq. (7c)) with all heads boosts overall performance. Losses rendered motion (Eq. (6b)) and point map (Eq. (6c)) are crucial for achieving high accuracy.
Alias Configuration
Image (PSNR
Point (EPF.
Point rast. (EPF.
Motion (EPE: J) Motion rast. (EPF.
Full model
23.929
0.827
0.841
0.069
0.064
image gradient.
24.268
0.899
0.070
(C)
W/ motion and puint rendering
20.675
0.911 0.886
0.943
0.071
0.069
W/o metion. point, and image readering
0.071
Input
(n)
gradient
W/ image
Wo motion and point rendering
(a) model
(b) Wi image gradient
W/o motion and point rendering
Figure 6: Qualitative comparison on loss ablation. Gradient backpropagation from the image synthesis loss and rendering losses on point and motion helps UFO-4D improve motion and point estimates, especially on object and motion boundaries.
Table 5: Architecture comparison. For apples-to-apples comparison, we train other methods with different output representation on our training protocol. Our method achieves the best accuracy except for Bonn on both point and motion end-point errors (lower the better).
Sterco4D
KITTI
Outpul representation
Equivalent or closest models
Motion
Poini
Motion EPE
Point EPE
Dynamic 3DGS
UFO-4D (Ours)
0.058
0.781
0.244 0.283
3.553
0.211
Per pixel and motion
DynaDUSL3R ZemoMSF MonST3R
0.057
0.809
4.567
0.196
Per pixel point
0.804
4,562
0.201
estimating Gaussian attributes, the supervision enabled by differentiable 4D rasterizer leads to better point and motion accuracy. Further, this approach unlocks novel capabilities, 4D interpolation.
Architecture comparison. As methods often rely on distinct training recipes, it is crucial to de- couple architectural benefits from implementation details. To achieve this, we compare our method with other baseline methods trained under our training protocol for apples-to-apples comparison. We change output representation
(i) per-pixel point and motion estimation (equivalent to Dy- naDUSt3R (Jin et al., 2025) and ZeroMSF (Liang et al., 2025b)) and (ii) per-pixel point only (equiv- alent to MonST3R). We follow the original training setup except that we use 256x 256 training resolution and train model for 60k iteration steps.
Table 5 shows that our method outperforms per-pixel representations on Stereo4D and KITTI, with substantial gains on KITTI. However, our performance on Bonn is slightly behind. We attribute this to the prevalence of textureless regions in Bonn where our model predicts large-scale, overlapping Gaussians (Fig. E). While this ensures spatial continuity, it introduces error- pror-prone spatial dependen- cies among neighboring pixels unlike per-pixel point representation. More discussion is followed in Section A.7.

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Interpoleted imogee
Interpolaeeo depeh
Interpoleted motion
Input pair
Interpolnted imaage
Interpolated dapat
Intepolated modee
Input
Figure 7: 4D interpolation DAVIS. Given an input pair on the left, the righthand side shows rasterized images, depth, and motion maps at the canonical camera coordinate at interpolated time between the two input frames. Our method also clearly segments out moving objects.
4D interpolation. We demonstrates the new capability of our method: 4D interpolation. Given an input pair, Fig. 7 shows rendered image, depth, and motion maps at the canonical camera coordinate interpolated time between the two imput frames, via 4D differentiable rasterizer. Our method can render those at any time at any camera views.
5 CONCLUSION
We have introduced UFO-4D, unified feedforward model for 4D reconstruction from pair of un- posed images. By predicting a holistic dynamic 3D Gaussian Splatting representation, UFO-4D ef- fectively leverages self-supervision overcome challenges like data scarcity and occlusions. UFO- 4D significantly outperforms prior work in estimating 3D geometry and motion, while uniquely en- abling high-fidelity 4D spatio-temporal interpolation. Our work demonstrates the power of unified, explicit representations for dynamic scene understanding, opening promising avenues for extending this framework to sequence videos and more complex motion models.
Future work. We identify several directions for future explorations. A naive extension to long sequences would memory-intensive due the linear growth of Gaussian Splats; future work could explore compact scene representation (An et al., 2026; Xu et al., 2025a). Also, our model's assumption linear motion and constant brightness is best suited for short time intervals. This could be addressed by incorporating learnable, non-linear motion models and time-varying Gaussian attributes to handle more complex dynamics and photometric changes. Moreover, rendered images, motion, and geometry novel view and time can be included as additional supervision objectives to enhance tetto-l consistency, when their annotations (e. g. from multi-view video datasets) available.
ACKNOWLEDGMENTS
We sincerely thank Richard Szeliski. Junyi Zhang, Michael Rubinstein, Richard Tucker, and Linyi Jin for their helpful discussions and technical assistance throughout the project. We also appreciate the anonymous reviewers for their constructive feedback during the review process.
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A APPENDIX
A.1 ADDITIONAL QUALITATIVE COMPARISON
We present additional qualitative results on Stereo4D (Fig. A), KITTI (Fig, B), and Bonn (Fig. C). Our method consistently shows more accurate depth and motion estimates than competing ap- proaches. notable advantage is our robustness to significant camera rotation and object motion in Fig. A), where other methods often produce erroneous estimates. Fur-
(e. g- the first two thermore, our method effectively disentangles dynamic object motion from camera ego-motion, successfully isolating moving objects where other methods frequently assign non-zero motion to static backgrounds. Unlike the blurred predictions seen in DynaDUSt3R, our method preserves sharp motion boundaries and geometric consistency across all datasets.
Depih
Mation
Depth
Motion
Depih
Motion
Input pair
DynaDUSSaR Jin al. (2025)
UFO-4D (Ours)
Ground truth
Liang al. (2025b) Fong et al. (2025)
Figure A: Qualitative comparison on Stereo4D. We visualize depth and projected 2D optical flow. UFO-4D consistently outperforms other methods in depth and motion accuracy. Notably, in scenes with significant camera rotation (top two rows), our method robustly estimates 3D geometry and isolates 3D motion of dynamic objects in the canonical space. In contrast, other methods struggle to disentangle camera ego-motion, resulting in residual motion artifacts on static background.
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Depih
Motion
Depih
Motice
Depeh
Medion
Depih
Mation
Depih
Moxion
Input pair
DynaDUSGRR Jin al. (2025)
UFO-4D (Ours)
Ground truth
Liang et al. (2025b) Feng al. (2025)
Figure B: Qualitative comparison KITTI. We visualize motion in camera coordinates, sistent with the ground truth of KITTI. Our method outperforms competitors with notably clearer motion boundaries, whereas ZSroMSF and St4RTrack frequently show erroneous motion estimation.
A.2 EVALUATION PROTOCOL
In Section 4.1, we compare our method with direct competitors, e.g., DynaDUSt3R (Jin et al., 2025), ZeroMSF (Liang al., 2025b), St4RTrack (Feng et al., 2025), that output point and 3D scene flow. Due to that each method has their own inference setups, we include details here on their evaluation setups used in our comparison study.
DUS3DR Similar to our method, DynaDUSt3R (Jin et al., 2025) outputs pointmaps and scene flow in the first frame's coordinate system, allowing to use its predictions directly. For inference, we resize inputs to 512 x 512 for Stereo4D and Bonn. For KITTI and Sintel, we use resize inputs to 382 x 512 following their protocol and 208 512 preserving aspect ratio, respectively. All outputs are resized back to the original resolution for evaluation.
ZeroMSF. We the same inference resolution in DynaDUSt3R (Jin al., 2025). While ZeroMSF (Liang et al., 2025b) defines pointmaps in the first camera coordinate, parameterizes scene flow Camera-space 3D offsets eStoy-nsi motion relative to a fixed camera. To ensure fair comparison on datasets where their motion is defined at the canonical camera coordinate (Stereo4D), we convert this flow to world coordinates using ground truth extrinsics. Although using ground truth poses grants ZeroMSF an advantage. it is necessary to minimize errors arising from coordinate system mismatch.
MASt3R, MonST3R, and St4RTrack. For MAS:3R, MonST3R, and St4RTrack, we follow the original evaluation protocol in their implementation. Unlike method, MonST3R and St4RTrack
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Depih
Mexion
Depib
Mation
(N/A)
Depih
Motion
(NIA)
Input pair
DynaDUS:3R Jin al. (2025)
ZeroMSF Liung (2025b)
Sı4RTrack Feng (2025)
UFO-4D (Ours)
Ground
Figure C: Qualitative comparison on Bonn. Comparing to other methods, our method outputs clear 3D motion disentangled from camera motion. ZeroMSF and St4RTrack suffer from non-zero residual motion background. DynaDUSt3R tends to show unclear motion boundaries.
do not directly output relative camera pose in feedforward manner. For the pose estimation, we use a PnP solver with RANSAC with their default hyperparameters.
A.3 SOME OBSERVATIONS ABOUT THE STEREO4D DATASET
Stereo4D (Jin et al., 2025) provides large-scale annotated data for 4D understanding, specifically including camera intrinsics, extrinsics, depth, 3D point trajectory. While is the biggest real-world based annotated dataset, we found that it includes noisy annotations that residuals from its optimiza- tion process given depth and motion cues processed from off-the-shelf models. Fig. visualizes some samples from Stereo4D. The last column visualizes motion map in the standard optical flow color coding. Close examination shows that some static region (e. g., ground and building wall) include non-zero motion, which typically under centimeter level.
This noisy annotation may result in non-zero motion estimates for static parts, as visualized in Fig. for DynaDust3R. Our dense, sule-super photometric loss on rendered images complements the imperfect ground truth and lead to better motion accuracy than other methods.
A.4 EVALUATION WITH PER- SER-VALID-PIXLL AVERAGING
While the main paper reports metrics using per-frame averaging (where each frame is weighted equally), an alternative protocol used by DynaDUS13R (Jin et al., 2025) is per-valid-pixel averaging. This approach weights each frame's contribution by its number valid pixels. For a comprehen- sive comparison, report results using this metric as well. Under this per-valid-pin pixel evaluation, UFO-4D also consistently outperforms other baselines on Stereo4D and KITTI and perform very competitive Bonn and Sintel final, shown in Tables A and B.
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First image
Second image
Visualized 3D motion
Figure D: Visualization of annotation of Stereo4D. The first two columns visualize two input frames, and the last columns visualizes 3D motion annotation defined at the canonical camera co- ordinate. While Stereo4D provides large-scale real-world annotated data, the label includes noisy annotation, g., non-zero 3D motion for static region.
Table A: Geometry estimation (under per-valid-pixel averaging protocol): Using the same eval- uation metric in DynaDUS:3R, our method consistently outperforms other methods.
Stereo4D
KITTI
Sintel final
Method
1.1.22
Rel.
Rel. 6xç 1.25
MASI3 (Leroy 2024)
1.658
0.290
65.60
0.484
0.173
76.68
2.247
0.086
92.15
3.907
0.360
60.68
MonST3R (Zhang at, 2025a)
1,380
0.194
72.57
0.187
0.062
96.26
2,741
0.106
87.96
3.514
0.338
57.23
DSIaDUSBR (Jin at. 2025)
0.683
0.103
87.94
0.423
0.084
92.83
4,805
0.113
84.10
4.296
0.295
60.88
ZeroMSF (Liang al., 2025b)
3,560
St4RTrack (Feng et al., 2025)
1.269
0.198
71.37
0.180
0.067
95.24
4.661
0.124
83.13
4.097
0.349
$6.87
(Ours)
0.103
2.485
3.828
0.317
Table B: Motion estimation (under per-valid-pixel averaging protocol) We evaluate the scene flow accuracy on the Stereo4D test split (Jin et al., 2025) and KITTI Scene Flow 2015 Training (Menze et al., 2018) Our approach consistently outperforms others on all metrics.
Stereu4D
KITTI
Method
forward
backward
forward
EPEip
20.05
EPE
Opp
EPEsp
60.06
DynaDUS(3R (Jin al., 2025)
0.118
65.19
0.106
64.47
0.467
1.09
ZeroMSF (Liang et al., 2025b)
0.067
67.38
0.452
13.84
To4RTrack (Feng et al., 2025)
0.233
42.56
0.768
3.36
UFO-4D (Ours)
0.023
91.78
0.025
91.28
0.139
46.71
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Table C: Dataset ablation. Given three training datasets, Stereo4D (ST), PointOdyssey (PO), and Virtual KITTI 2 (VK), we try a different mixture of each dataset, train our model, and report per- formance on three evaluation datasets, Stere4D, KITTI, and Bonn, reporting PSNR for image re- construction and end-point error (EPE) for point and motion. The best accuracy on Stereo4D is achieved by solely training on Stereo4D. Adding Virtual KITTI2 improves especially motion aceu- racy on KITTI but hurts point accuracy on Bonn. Adding PointOdyssey improves accuracy on Bonn but hurt that on KITTI as trade-off. All experiment is done in a small scale training.
Dataset propertion
Stereo4D
KITTI
Bonn
ST
PO
VK
Image
Point
Motion (U)
Image (f)
Point (U)
Motion (
Image (f)
Point (L)
0.7
0.15
0.15
23.547
0.065
18.845
18.568
0.314
0.85
0.15
23.419
0.065
0.839
18.791
0.298
4.102
18.056
0.344
0.85
0.15
23.587
0.064
0.874
18.037
0.355
6.214
18.610
0.291
0.5
0.5
21.918
0.074
1.292
19,363
0.267
2.761
19.824
0.273
1.0
23.929
0.064
0.841
18.610
0.311
6.138
18.913
0.316
Table D: Training with different initialization. We compare our model trained on different pre- trained weights, report image PSNR, 3D point EPE, and 3D motion EPE on Stereo4D (Jin et al., 2025), KITTI (Menze et al., 2018), and Bonn (Palazzolo et al., 2019) datasets. Initialization from MASt3R gives overall better accuracy whereas that from MonST3R improves 3D point accuracy on
KITTI and Bonn.
Sterco4D
KITTI
Bonn
Method
PSNR1
Point EPF.
Motion EPF.).
PSNR1
Point EPF.
Motion EPF...
PSNR1
Point EPF.
Initialization from MASUR (buseline) Inirialization MonST3R
24.519
0.781
0.058
19.606
3.553
0.244
26.606
0.211
24.200
19.506
A.5 DATASET ABLATION
To analyze how each dataset combination affects accuracy across differenet evaluation dataset, we conduct an ablation study on different mixture of training datasets. Table C provides the result.
Given three training datasets, Stereo4D (ST), PointOdyssey (PO), and Virtual KITTI2 (VK), we train our full model using the same scall-scale training configuration as Table 4 in the main paper. During training, we sample each dataset given each provided dataset proportion in the table.
Addition of different dataset on top of Stereo4D gives different behaviors on each test dataset. The best accuracy on Stereo4D dataset can be achieved by training solely on the Stereo4D dataset, in- stead of the mixture of different datasets. The addition of Virtual KITT12 improves accuracy on KITTI but hurts accuracy on Stereo4D and Bonn trade-off. The addition of PointOdyssey does affect accuracy on Stereo4D much, while improving accuracy on Bonn but degrading accuracy on KITTI This concludes that there is single best dataset for generalization. A mixture of datasets improves generalization to other datasets in general, but the accuracy obtained is worse than that of the model trained dataset specialized to the target domain.
A.6 MODEL. INITIALIZATION
As described in Section 4, we initialize our network using pretrained weights from MAS13R (Leroy et al., 2024), with the Gaussian head initialized from Ye et al. (2025). We also explore initializing from MonST3R (Zhang et al., 2025a) to leverage its capability of dynamic scene reconstruction.
Table D compares these different initializations on the Stereo4D, KITTI, and Bonn datasets using the training setup from the architecture comparison study in Section 4.2 (i. e., 256 x 256 training resolution, 60k iteration steps). Interestingly MASt3R initialization yields overall better accuracy. MonST3R provides better 3D point accuracy on Bonn and KITTI. We presume that the performance of the checkpoint from MonST3R is particularly optimized for Bonn, as observed their competitive accuracy Bonn in Table 1, though our model does not necessarily need this specific prior.
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KITTI
Bonn
Stervo-D
X
Input image
Predicted depth
Gaussian scale
Figure E: Visualization of the scale of 3D Gaussian. For each dataset, KITTI, Bonn, and Stereo4D, visualize Gaussian scale (the bottom row) along with an input image and predicted depth of our method. The Gaussian scale (r, 2) is visualized in the (R, G, B) color space. For textureless region such as sky or wall, our method predicts larger scale of gaussian whereas small-scale 3D Gaussians are dominant in high-frequency texture region. Bright color means larger Gaussians.
DynaDUSGR
UFO-4D (Ours)
Input image
Predicted depth
Emor map
Predicted depth
Eror map
Figure F: Comparison with DynaDUSt3R on Bonn. Unlike per-pixel point representation, our representation gives subtle inaccuracy in geometry textureless region (e. g. wall). Brighter means higher errors.
A.7 DISCUSSION ON BONN
While our method outperforms baselines across most benchmarks, its performance on the Bonn dataset is comparable to prior work (Tables and 5). We attribute this to the prevalence of textureless regions (e. g. walls) in Bonn, where our model predicts large. highly overlapping Gaussians (Fig. E). Although this overlap promotes surface continuity, it renders the point estimate dependent on weighted combination of multiple Gaussians, making susceptible accumulated blending errors from nearby Gaussians. In contrast, per-pixel representations (e. g. DynaDUS(3R, MonST3R) avoid this spatial dependency by estimating depth isolation.
Consequently, as visualized in Fig. F, our method exhibits slightly higher errors in these texture- less regions. We quantitatively confirm this hypothesis Table E, which demonstrates that depth accuracy degrades as the scale of the estimated 3D Gaussians increases.
A.8 CROSS-ATTENTIO, MAP VISUALIZATION OF THE CAMERA POSE TOKEN.
Our method is capable estimating accurate relative camera poses in scenes with dynamic objects. To investigate the underlying mechanism enabling this robustness, visualize the cross-altention maps between the learnable camera pose token and the image tokens in Fig. G. As a reference, we also visualize static baseline for each scene by using two identical images input. We observe that at specific blocks (e. g. the 8th, 1th, and 12th blocks), the cross-attention maps exhibit significantly higher attention scores in static regions, effectively suppressing attention moving objects.
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Table E: Impact of Gaussian scale geometry accuracy on Bonn. We report Point EPE and Depth Abs. Rel. across different ranges of estimated Gaussian scales. We observe that depth accu- racy decreases along with the increased scale of estimated 3D Gaussians.
Scale Range
Point (E:E)) 1
Depth (Abs. Rel.)
0
0.5
0.189
0.073
0.5
1.0
0. 0.151
0.059
1.0
0.200
0.068
1.5
2.0
0.189
0.067
2.0
0.185
0.071
2.5 3.0
0.205
0.085
Input pair
Block 8th
Block 11th
Block 12th
Dynamic
Stati
Dynanic
Statke
Statie Dynanic
Figure Visualization of cross-attention maps of the camera pose token. We observe that at specific blocks (Blocks 8th, 11th, and 12th), the camera pose token more attends to image tokens located in static regions (i. e., high attention scores brighter colors), which provide reliable cues for relative camera pose estimation. Moving objects tend to have darker colors, i. e., lower attention scores. As a reference at each second row, we also visualize the cross-attention maps for a static scene configuration, where identical images used input.
A.9 DISCUSSION ON THE ACCURACY GAIN ON KITTI
In Table in the main paper, our representation shows much better accuracy gain on KITTI, com- paring to the model with per-pixel point and motion representation. We find that the huge accuracy gain on KITTI is from both the accurate pose estimation and better supervision with our photometric losses.
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Table F: Ablation Pose and Representation. We compare our method with per-pixel baselines using different pose estimators (pose head vs. PnP-RANSAC). Our Dynamic 3DGS representation with the feedforward pose head achieves the best accuracy on Stereo4D and KITTI.
Stereo4D
KITTI
Bonn
Oulput representation
Pose estimalor
Motion EPE
Point EPE
Motion EPE
Point EPE
Point EPE
Dynamic 3DGS (Ours)
Pose head
0.057
0.781
0.244
3,583
0.211
head PoP-RANSAC
0.060 0.058
4.
0.206
Per-pixel point and mation
0.809
0.283
4.567
0.196
Image
Ground Truth
Ours
Per-pixel representation
Pred.
Eiror
Pred.
Error
Pred.
Errvr
Pred.
Eiror
Pred.
Eroor
Figure Qualitative comparison of our method and baseline with per-pixel point represen- tation on KITTI Unlike per-pixel representation model, our method yields much better accuracy especially on planar road surface, benefiting from our accurate pose estimation and photometric losses rendered images. In enror maps, darker means more accurate.
Our accuracy gain for motion on KITTI primarily originates from our accurate pose estimation. Since KITTI evaluates motion in local camera coordinates, predictions in the canonical camera co- ordinate need be transformed using estimated poses. For baselines, we use PnP-RANSAC that follows their formulation. In contrast, Our method utilizes significantly more accurate, directly estimated feedforward pose, leading better motion accuracy the end. We validated this source of improvement by training per-pixel baseline equipped with pose head (i. equivalent to Dy- naDUSt3R with the pose head.). As in Table its motion accuracy on KITTI now almost matches method, highlighting the importance of the our feedforward pose estimation.
For gain on point accuracy, we attribute the gain to the photometric loss acting as an additional dense supervision signal, which also gets benefits from our robust pose estimation. Fig. H shows that our representation has much lower errors on planar road areas comparing to the model with per- pixel representation. With accurate pose, the loss the rasterized image effectively encourages
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Table G: Pose estimation with PnP+RANSAC. Our method with PnP+RANSAC also outperforms direct competitors, validating the higher accuracy of our estimated geometry than others. Also our direct feedforward estimator achieves ~16.6% higher accuracy than this baseline, further demon- strating its robustness over noise-sensitive iterative solvers. ATE and RPE translation (RPEmas) and rotation (RPE are reported.
Stereo4D (Jin al., 2025)
Boon (Palazzoln et al., 2019)
Sintel (Butler et al, 2012)
Method
ATE
RPE
RP
ATE
RPE
RPE
ATE
RPEinaa
RPE
MonST3R (Zhang al., 2025a)
0.0458
0.0647
0.0043
0.0410
S44TTack (Teng et al., 2025) (Ours, PoP+RANSAC)
0.0602
0.0851
0.729
0.0024
0.0034
0.400
0.0231
0,0326
0.884
UFO-4D (Ours, with the pose head)
0.0101
0.0142
0.179
0.0020
0.0028
0.271
0.0122
0.0172
0.298
the model to respect multi-view stereo constraints, guiding the estimated point to be geometrically consistent with the parallax in both views. This allows the model to better leverage both images as geometric supervision, whereas baselines only have view-indeppndent sparse per-point supervision.
A.10 DISCUSSION ON THE BETTER POSE ACCURACY
Table 3 the main paper demonstrates that our method achieves the best pose accuracy among direct competitors. We further investigate the source of the gain to determine whether it originates from our feedforward estimation or the improved geometry. To answer the question, we also estimate relative pose via PnP+RANSAC from our predicted Gaussian centers.
The answer is both. performs other methods. This validates the higher accuracy of our estimated geometry than others, indicating that estimated points are aligned well with direction of each camera ray. Furthermore, direct feedforward estimator achieves ~66.6% higher accuracy than this PnP baseline, further demonstrating its robustness over noise-sensitive iterative solvers.
Table G, our method with PnP with RANSAC still substantially out-
The usage of Large Language Models (LLMs) in paper writing. The paper got helps from LLMs for polishing writing and grammar correction.
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Reviews:
[{"review_id": "mQXWWlSCB9", "reviewer": "Reviewer_Y37w", "summary": "This paper focuses on a dynamic Gaussian reconstruction framework that infers 4D scenes from two images. The reconstructed 4D scenes enables motion extraction and novel view synthesis. The experiments demonstrate the effectiveness of the proposed method.", "questions": "Please refer to the weaknesses above.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The paper focuses on a cutting-edge task that directly predicts Gaussian attributes from unposed images. The task is applicable of many downstream applications.\n2. This paper implement a standard and simple network with pure ViTs, which can be potentially scaled up to large datasets and parameters.", "weaknesses": "1. The results are not convincing. This paper does not show enough visualization on the rendering quality of the reconstructed 3DGS at different novel views. And no videos are provided as the supplementary for a clear visualization on the reconstruction quality. So I am not sure whether the generated 3DGS are of high-quality. Given the current figures, I do not see significant performance gain compared to only predict colored points, which weakens the insight of predicting Gaussian primitives.\n2. A video showing novel view synthesis results are required.\n3. I found that the input image pairs are often of very similar viewpoints. I wonder if the method can handle pairs of images that contains large offsets in camera poses? \n4. A comparison with 4DGT is recommended since 4DGT also focuses on predicting dynamic Gaussians. Since 4DGT requires camera poses as input, you can simply apply an off-the-shelf camera pose estimation model first.", "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": "SBEV7DvLUG", "reviewer": "Reviewer_jDQa", "summary": "This paper introduces UFO-4D, a unified feedforward architecture for reconstructing dense 4D scenes from two images without known camera poses. The method simultaneously predicts dynamic 3D Gaussian splats and the relative camera transformation in one forward pass, creating an explicit 4D scene representation that jointly recovers 3D structure, motion fields, and camera pose while enabling spatio-temporal interpolation. By employing differentiable rasterization, the framework integrates both self-supervised photometric constraints and supervised signals for geometry and motion, establishing a strong coupling between depth and motion prediction. Comprehensive evaluations show state-of-the-art results across multiple geometry and motion benchmarks, supported by qualitative and quantitative analysis, ablation studies, and novel 4D interpolation use cases.", "questions": "Q1. How does UFO4D estimate relative camera pose? How does the performance of camera pose estimation compare with prior works?\n\nQ2. As mentioned in the discussion of the Stereo4D dataset, there seem to be pixels in both images where the tracking and correspondence information is not provided in the dataset. For these pixels, what does the velocity vector in the predicted Gaussian learn? In addition, how sparse are these signals during training? Can other supervision from dense losses compensate for this sparsity?\n\nQ3. How robust is UFO4D to large baseline images?\n\nQ4. Why does UFO4D initialize the weights from Mast3r instead of learning directly from the finetuned version MonST3R? It seems that training could be further boosted and become more efficient when initialized from MonST3R.\n\nWith several questions and unclear parts remaining, I would like to recommend border reject as my initial rating. However, with my questions being resolved, I am eager to raise my score.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Unified feedforward architecture:** Unlike previous works that obtain geometry and motion estimation with separate steps, the proposed UFO-4D offers an unified 4D representation capable of solving multiple downstream perception tasks jointly which has been found to be beneficial in other domains.\n- **Leveraging 4DGS representation:** By leveraging 4D Gaussian Splatting as the representation, this allows the model to be trained with additional photometric supervision, and also allow other estimations such as motion vectors to be easily rasterized allowing additional self-supervised signals, and allowing all signals to train a joint representation.\n- The overall architecture is simple and straightforward, with the details of the architecture being explicitly mentioned, making it easy for re-implementation of the method.", "weaknesses": "- **Lack of direct comparison:** Although I agree that leveraging a representation that tightly couples both pointmap and motion estimation will further boost performance, it is difficult to understand the benefits that come from adopting this new representation itself as the proposed method also utilizes a very recently open-sourced dataset Stereo4D for training. It would be nice to include a comparison of baseline method trained with the same dataset recipe to better highlight the advantages of the proposed representation.\n- **Small baselines:** The qualitative figures shown in the paper seem to take two frames with small baselines as input. As a result, it is difficult to understand how well the method is estimation motions when given large baselines (two frames that are far away) as input.\n- **Limited evaluation:** Adding more evaluation results in dynamic datasets such as Sintel[1] or TUM-Dynamics[2] could better highlight the strength of the paper. Also adding evaluation of the estimated camera pose for all datasets would be needed to emphasize the importance of adopting the proposed representation.\n\n### References\n\n---\n\n[1] Butler, Daniel J., et al. \"A naturalistic open source movie for optical flow evaluation.\" European conference on computer vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.\n\n[2] Sturm, Jürgen, et al. \"A benchmark for the evaluation of RGB-D SLAM systems.\" *2012 IEEE/RSJ international conference on intelligent robots and systems*. IEEE, 2012.", "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": "fmodS3axuz", "reviewer": "Reviewer_W48t", "summary": "This paper proposes a method to predict appearance and geometry (in the form of gaussian splats, motion and cameras) from two views. This is done in a feedforward manner and the main novelty is in connecting 2D motion/geometry supervision (in the form of point maps and scene flow) to the 3D outputs of the network, which is done by reusing the gaussian splats rendering aparatus for non-RGB rendering.", "questions": "Mostly I would like to know that a comparison is underway or that preliminary experiments indicated the gains are worth it, on a apples-to-apples comparison. This would turn the rating into a strong accept.\n\nAs the contribution is predicated on a simple idea, if other reviewers bring up (published) papers that call its priority into question, I would like to see a proper distinction between the methods.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "Although there are several papers tackling the general problem of 4D estimation (many correctly mentioned in the related work), this contribution stands out as a simple idea that is very well executed. Namely, the idea of rasterizing geometric and motion information into 2D to supervise it with scene flow and point maps (i.e. LIDAR) from real datasets seems somewhat novel (if a little obvious in hindsight, like all good ideas). This differs from approaches that demand 3D supervision, which often limits them to synthetic data, and approaches based purely on photometric supervision, which are plagued with degenerate solutions in the case of motion data (due to the inherent ambiguity of camera vs object motion).\n\nThe paper is written very clearly and the experiments are extensive, with significant gains. These are slightly tempered by the pretraining being based on recent SOTA weights, but the method brings a clear advantage.", "weaknesses": "For being based on NoPoSplat and MASt3r, a direct comparison with those methods where appropriate is needed. This would quantify the actual gains the method brings. However, it is understood that broadening the applicability of the original moodels can be a sufficient justification for the proposal.\n\nAs a minor aside on clarity, the smoothness loss should be written explicitly to make the paper self-contained, since it is an important factor for performance.", "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": "pjB5CRWaPk", "reviewer": "Reviewer_aizK", "summary": "The paper introduces UFO-4D, a feedforward model that takes two unposed images (with intrinsics) and predicts a dynamic 3D Gaussian Splat representation in a canonical frame plus the relative camera pose. A single differentiable 3DGS rasterizer is used to render RGB, pointmaps, and scene flow at arbitrary times and viewpoints by swapping rendered attributes through the same rasterizer. Training mixes supervised losses with photometric and edge-aware smoothness losses on rendered outputs. Strong results are reported on Stereo4D, Bonn, and KITTI, including 4D spatio-temporal interpolation from just two frames.", "questions": "1. The motion and depth maps in the qualitative figures appear blurrier than those from ZeroMSF. Is this softness an inherent effect of the Gaussian representation and splatting, or does it result from the rendering resolution or regularization losses used during training?\n2. Does the proposed method still perform reliable 4D interpolation when the two input views have a large baseline or significant motion?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Unified explicit 4D design. Casting image/geometry/flow supervision through one renderer on 3D Gaussians without any additional regression heads is elegant and practical, aligning with the efficiency of 3DGS splatting while extending it to 4D prediction.\n2. Quantitative gains. Clear improvements vs Zero-MSF and DynaDUSt3R on multiple datasets, suggesting the rendering supervision is beneficial for motion estimation. \n3. Good ablation studies on training data, including single-dataset and mixed-dataset configurations, clarifying domain-transfer trade-offs.", "weaknesses": "1. Pose metrics are missing. Pose is central but quantitative metrics aren’t reported.\n2. Lack of evaluations. Additional comparisons would strengthen the paper, particularly to dynamic reconstruction settings like **MonST3R [1]**, methods that jointly estimate pointmap and scene flow such as **Dynamic Point Maps [2]**, **St4RTrack [3]**, **POMATO [4]**, and classic scene-flow estimators like **RAFT-3D [6]**.\n3. As noted in the paper’s limitations, the method is designed and evaluated for two views; there is no demonstrated extension to multi-view or longer sequences. This is problematic for dynamic scenes where motion reasoning benefits strongly from multi-view temporal context. Compared with several works [2,3,4,5] that do scale to multi-view settings, restricting to only two views appears limiting for long-range tracking, occlusion handling, and temporal consistency.\n4. Lack of large-baseline or large-motion evaluation. The examples and datasets used in the paper mostly appear to involve small baselines and moderate motion. It would strengthen the work to include results on large-baseline or high-motion scenarios, or to evaluate on datasets such as MPI-Sintel that feature stronger motion variation. Demonstrating robustness in these challenging regimes would better validate the method’s generalization and motion reasoning capabilities.\n5. Figure issue (Fig. 7): In the last example, the image and depth outputs appear swapped.\n\n[1] Zhang, Junyi, et al. \"Monst3r: A simple approach for estimating geometry in the presence of motion.\" ICLR’24.\n\n[2] Sucar, Edgar, et al. \"Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction.\" ICCV’25.\n\n[3] Feng, Haiwen, et al. \"St4rtrack: Simultaneous 4d reconstruction and tracking in the world.\" ICCV’25.\n\n[4] Zhang, Songyan, et al. \"POMATO: Marrying Pointmap Matching with Temporal Motion for Dynamic 3D Reconstruction.\" ICCV’25.\n\n[5] Han, Jisang, et al. \"D^ 2USt3R: Enhancing 3D Reconstruction with 4D Pointmaps for Dynamic Scenes.\" NeurIPS’25.", "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)