Visual Sync: Multi‑Camera Synchronization via Cross‑View Object Motion
Authors: Shaowei Liu
David Yifan Yao
Saurabh Gupta
Shenlong Wang
Venue: NeurIPS 2025 poster ()
Keywords: camera synchronization
video motion analysis
scene understanding
Primary area: applications
TL;DR: multi-camera synchronization in the wild

Abstract: Today, people can easily record memorable moments, ranging from concerts, sports events, lectures, family gatherings, and birthday parties with multiple consumer cameras. However, synchronizing these cross‑camera streams remains challenging. Existing methods assume controlled settings, specific targets, manual correction, or costly hardware. 
We present VisualSync, an optimization framework based on multi‑view dynamics that aligns unposed, unsynchronized videos at millisecond accuracy. Our key insight is that any moving 3D point, when co‑visible in two cameras, obeys epipolar constraints once properly synchronized. To exploit this, VisualSync leverages off‑the‑shelf 3D reconstruction, feature matching, and dense tracking to extract tracklets, relative poses, and cross‑view correspondences. It then jointly minimizes the epipolar error to estimate each camera’s time offset. Experiments on four diverse, challenging datasets show that VisualSync outperforms baseline methods, achieving an average synchronization error below 130 ms.

VisualSyne: Multi-Camera Synchronization via Cross- Fress-View Object Motion
Shaowei Liu!*
David Yifan Yao Saurabh Gupta 1
Shenlong Wang't
University of Illinois Ürbana-Champann
https://stevenllsv. github........... io/ tarriiiviiiiie /vssuulynnc
Cam #2
Cam #2
Cam #3
Cam
Cam
Cam #2
Before Sync
After Sync
Figure 1: VisualSyne Overview, Given multiple unsynchronized videos capturing the same dynamic scene from different viewpoints, VisualSyne recovers globally time-aligned video streams by estimat- ing temporal offsets between views. For example, in the volleyball scene, before synchronization the player's motion misaligned across videos; afterwards, given timestamp in all three streams corresponds to the same moment.
Abstract
Today, people can easily record memorable moments, ranging from concerts, sports events, lectures, family gatherings, and birthday parties with multiple consumer cameras. However, synchronizing these cross cross-camera streams remains challenging. Existing methods assume controlled settings, specific targets, manual correction, or costly hardware. We present VisualSync, an optimization framework based on multi-view dynamics that aligns unposed, unsynchronized videos millisecond accuracy. Our key insight is that any moving 3D point, when co-visible two cameras, obeys epipolar constraints once properly synchronized. To exploit this,
Equal contribution: Equal advising.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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VisualSyne leverages off-the-shelf 3D reconstruction, feature matching, and dense tracking to extract tracklets, relative poses, and cross-view correspondences. It then jointly minimizes the epipolar error to estimate each camera's s time offset. Ex- periments on four diverse, challenging datasets show that VisualSync outperforms baseline methods, achieving an average synchronization error below 130 ms.
Introduction
Recording dynamic scenes from multiple viewpoints has become increasingly common in everyday life. From concerts and sports events to lectures and birthday parties, people often capture the same moment using different handheld devices. These multi-view recordings present a rich opportunity to reconstruct scenes in 4D, enable bullet-time effects, or enhance the capabilities of existing vision models. However, these videos are typically captured independently, without synchronization or known camera poses, making it difficult to align and fuse them coherently.
Existing synchronization methods rely controlled environments, manual annotations, specific patterns (e.g. human pose), audio signals (e.g. flashes or claps), or expensive hardware setups such as time-coded devices, none of which available in casually captured videos. In this work, design a versatile and robust algorithm for synchronizing videos without requiring specialized capture or making assumptions about the scene content. Our key insight that, at the correct synchronization, the scene is static and thus the epipolar relationship (i.e. x² Fx = O for correspondent points æ and o' in two views) must hold true for all correspondences, whether on static dynamic objects (19] See Fig. 2 for an illustration.
While the insight follows directly from first principles and has been used in past attempts on this problem B3 34 60 31
[6], building practical and robust system that works on videos in the wild is challenging. We need a reliable estimate for the fundamental matrix between camera pairs, dynamic objects are a priori unknown and are generally small and blurry, and not all views may have overlap. Our key contribution is to leverage recent advances in computer vision, specifically dense tracking, tross-view correspondences, and robust structure-from frommmmoooommooiinn, to build a robust and versatile system that reliably synchronize challenging videos.
55
Specifically, we formulate a joint energy function that measures the violations to the epipolar constraints between correspondences between videos and adopt threc-stage optimization procedure. In Stage O, we use VGGT (56) to estimate fundamental matrices between each video pair, use MAST3R 30 to establish correspondences across videos, and use CoTracker3 24 24] to establish dense tracks within each video. This gives us access to quantities (correspondences and fundamental matrices) necessary evaluate the joint energy. Optimizing this joint energy directly is challenging. Therefore, in Stage 1, we decompose this energy into pairwise energy terms and estimate the best temporal alignment between each video pair via a brute force search. In Stage 2, synchronize the temporal offsets across all video pairs to assign a globally consistent temporal offset to each video.
We validate our approach on diverse datasets and show strong performance across different scenes, motions, and camera setups, and achieve gigh-precision synchronization even under severe viewpoints. Specifically, outperform SyncNerf (26). recent method for this task by radiance field optimization, and adaptations of two recent methods Uni4D [62] and MAST3R [30]. These results demonstrate the robustness and generality of our approach and open the door to scalable, unconstrained multi-view 4D scene understanding.
Related work
Tracking and Correspondence. Establishing reliable correspondences across time and views is fundamental for synchronizing multi-view videos [T3] 4
3 [T]. Recent models like 18 14, track points densely over time, offering strong temporal coherence. However, they do not model spatial correspondences across different viewpoints. On the other hand,
12
47
CoTracker 24 23
MASt3R 30 58 focuses on spatial matching and stereo reconstruction, providing dense cross- view correspondences, but does not handle temporal dynamics, especially moving scenes. Our method bridges this gap by constructing spatio-temporal cross-view correspondences, integrating both temporal tracking and spatial matching to enable accurate synchronization in dynamic, multi-view video settings.

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Camera #1
Carnera
Camera
Camera #2
No Sync
Sync
Time
Figure 2: Spipolar-geometrs cue for video syne: When cameras are time-aligned. keypoint tracks align with epipolar lines (bottom); misalignment causes deviations (middle). Minimizing these deviations across tracklets recovers the correct time offset.
Multi-View Structure-from-Motion.
Prructure-fro--aee
(SfM) techniques
58
39 [50 [1], such as COLMAP [48], have significantly advanced 3D reconstruction pipelines by producing accurate camera poses from multi-view images and videos. More recent models 53] like HLOC 47 46 and VGGT
[5 57 build on this progress using learning-basee based features and transformers to handle large-scale matching and pose estimation. While these methods achieve strong performance in estimating camera geometry, they fall short in synchronizing dynamic scenes, as they rely predominantly on static visual cues. In contrast, our approach explicitly decomposes the scene into static and dynamic components, leveraging static cues for pose estimation and dynamic cues from moving objects to perform robust temporal synchronization across views.
Video Synchronization. Video synchronization has been explored from multiple perspectives 33
49
34 40), such as those by Albl al. S and Li et al. 311, estimate temporal offsets using epipolar geometry, but typically assume static scenes or fixed viewpoint. Human-centric approaches use human pose as a synchronization signal, 36
59
60
40 65). Geometry- based methods B 55 16
28 21], yet these approaches are limited by the accuracy of human pose estimation, the number people present in the scene, and they struggle in diverse scenarios without prominent human activity. Audio-based approaches [49 20) use audio cues for synchronization, which can only work in quict environments and not generally applicable to in- the-wild settings where audio is noisy or unavailable. searning-basee methods like Sync-NeRF (26) jointly optimize camera poses and temporal offsets, but are often constrained to specific environments object types. Our work overcomes these limitations by leveraging pretrained visual foundation models and framing synchronization as an epipolar- based optimization problem. By reasoning jointly over static structures and dynamic foreground motion, we deliver generalizable solution for aligning asynchronous, unposed videos in complex real-world scenarios.
benefiting from its strong visual priors [10 63
3 Approach
3.1 Problem Formulation
Given set of N asynchronous videos (V') ) capturing the same dynamic scene from different viewpoints, our goal is to synchronize them and recover a globally aligned timestamp. Formally, we aim to estimate time offset s' R for each video i, to be applied to its original out-of-sync clock time. After synchronization, frames sharing the same clock time will correspond to the exact same moment across all videos.
Key Insight. Our key insight lies in the epipolar geometry between two cameras that capture the same scene. In Fig. 2 two cameras with known poses capture the same dynamic scene (c.g.. moving person and their dog). We track and associate a keypoint across both videos (here, the human's 's hand), yielding pair of tracklets (yellow and purple). If the videos are synchronized, then for any pair of frames with the same timestamp, the keypoint observations will satisfy epipolar geometry- for example, one keypoint will lie on the epipolar line of its counterpart. Conversely, if the videos not synchronized, this property does not hold, and the keypoint may deviate from the epipolar line.
Formally, let x() and (1) be pair of matched 2D tracklets in homogeneous coordinates between cameras i and j. forming continuous-time point trajectories and describing the same dynamic 3D point the world. Let K,, K, denote the known (or estimated) intrinsics, and T (t), T (t) the corresponding

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Multi-view Video
Camera Poses
Dense Trajectory & Cross-View Matching
Global Offset Estimation
Pairwise Offset Estimation
F
Before
After
Un-Synced
Synced
Figure 3: Proposed framework: Given unsynchronized videos, VisualSyne follows three-stage pipeline. Stage 0 estimates camera parameters with VGGT [56]. dense correspondences with CoTracker3 [23], cross-view matches with MAST3R (30), and dynamic objects with DEVA (9] In Stage I, we estimate pairwise frame offsets by minimizing epipolar violations over matched trajectories. Stage globally optimizes individual offsets to produce synchronized videos.
extrinsic trajectories. If the true synchronization offset between cameras i and j is A, then the epipolar constraint holds:
(x' A))
T FV H4. x2
0;
where F.j t+ is the fundamental matrix between camera time A and camera j at time t. Otherwise, it may not be equal to zero.
By leveraging this cue, we formulate an optimization problem that finds the time offset minimizing the epipolar distance of all associated keypoint trajectories for each camera pair with known poses. will then extend this approach to multi-camera and moving-camera scenarios.
Problem Formulation. Inspired by our discussion above, we formulate global synchronization as an energy minimization problem over {s*). Specifically, we aim find offsets that best align all video pairs by minimizing their pairwise synchronization error:
{8') = arg min
Σ EAy(A where A =² {s*)
Here, A denotes the relative temporal offset between videos and j, and (A) measures the misalignment error under this candidate offset in terms of the Sampson geometric error between associated tracklet pairs that are covisible between camera i and j.
Pairwise Term. The key idea of Eig (A) to quantify how much the paired tracklets violate epipolar geometry. Among various epipolar-error measures, adopt the Sampson error [9 35
44]
which approximates the squared Euclidean distance from a point to its corresponding epipolar line. By linearizing the epipolar constraint, it admits closed-fone expression and is computationally efficient for real-world optimization. Detailed derivation are presented in Appendix Formally, we write:
(x 4)+ FV H4. x² (1))
Ey = ΣΣ (x) (x*x)) I4 X (U)R A xi(1 AN1?
(3)
FF'
where F.J L+A, is the fundamental matrix between camera at time A and camera j at time t, and (1), x² (1) are matched continuous-time point-trajectory tracklets between cameras and j. Intuitively, the numerator is the squared algebraic epipolar residual, and the denominator sums the

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squared lengths of the two epipolar- line normals. This normalization converts the raw residual into an approximation of the squared point-to-ling distance, closely matching the true reprojection error, while remaining a fast and closed-form computation.
3.2 Inference
Challenges. Minimizing the energy in Eq. 2 poses three challenges. First, the optimization problem is highly non-convex. Second, the formulation is continuous-time, yet observations arrive at discrete frame times. Third, in real-world scenarios, is difficult to associate dense trajectories across cameras with significantly different viewpoints and to estimate accurate poses for moving cameras.
Overview. We address the challenge via three-stage optimization strategy. Stage 0 leverages large, pretrained vision models for dense pose-trajectory tracking, feedforward camera-pose and intrinsic estimation, and sxtreme-viewpon -anucuu approach to minimize the proposed energy optimization. Stage performs per- pair, discrete-time surrogate optimizations via exhaustive search find each camera pair's optimal alignment. Stage 2 aggregates these pairwise alignments to recover the global, continuous-time offset via solving a robust least square problem.
matching, making energy evaluation tractable. Then adopt
Stage 0: Visual Cue Extraction. Computing E, defined in Eq. requires camera parameters (intrinsics and poses) and dynamic point trajectory pairs across vIews. To obtain these, we VGGT [66] to jointly reasons about all cameras' intrinsics and pose trajectories from static background
regions in common coordinate system. We apply GPT4o, SAM and DEVA p 27
43 32 and
CoTracker 23 to segment dynamic objects and track dense 2D point trajectories within each video, and employ MASt3R (30] to match these per-view tracklets across cameras by comparing sampled keyframes, yielding the cross-view correspondences needed for Sampson error evaluation. We provide more details in the appendix.
Stage I: Estimating Pairwise Offsets. To handle the joint-optimizationn challenge with non-convexity and only discrete visual evidence at each frame, we drop the constraint in Eq. (2) and instead search over a discrete set of offsets A E S, for each camera pair j). In this way, we can independently minimize each pairwise energy term:
v(..):
A Ú = arg min Eiy AES
where S is a finite set of time offsets, step size is determined by the frame rate and range is hyperparameter.
Note that not all camera pairs (i,j) yield reliable time-offset estimates. In practice, some pairs have minimal temporal overlap, others lack sufficient viewpoint overlap, or visual cues may be noisy. Using the per-pair estimates in Eq. 4 We discard any pair whose ratio of the optimal energy to the next-best local minimum falls below 0.1 or more than two local minima found, resulting in & of reliable pairs for global synchronization.
Stage 2: Global Offset Esimation.
The goal in this stage is to recover the global offsets {s'} for all videos from the pairwise estimation v Since the discrete, imperfect estimates A may not admit solution {s') } perfectly satisfying s² s' A for every pair (i,j) E E, we formulate robust least-squares problem:
{8')* arg min (o))
Σ (KJ)EE
ps(s 8 A"),
where Ps is the Huber loss. We solve this with an iteratively reweighted least squares (IRLS) procedure [II], yielding the final global synchronization offsets {s'}*.
4 Experiments
4.1 Experimental Setup
Implementation details. Given the input videos, we first extract dynamic object categories using GPT [1 and apply Grounded-SAM [3 30 to obtain initial per-frame segmentations. We then run DEVA 9 to track these instance masks across time, producing temporally consistent segmentations for each moving object. For each tracked instance, we apply CoTracker3 [23] to perform per-instance temporal tracking. To establish cross-viee view correspondences, we sample keyframes every 10 frames
5

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Unsyno
MisualSync
UnidD
MostaR
Figure 4: Qualitative Comparison of synchronization on Egohumans 25 across baselines We visually assess temporal synchronization presenting magnified views of the shuttlecock's position across time. In this complex scenario -marked by large temporal discrepancies, small dynamic element, and moving cameras Visual Sync achieves the most accurate alignment.
CMU-Panoptic
Egohumene
Figure 5: Qualitative Comparison of Video Sync across datasets. We show the synchronized videos on CMU-Panoptic, UDBD, 3D-POP and Egohumans dataset. Top rows shows the estimated synchronized time stamps from different views. The bottom shows synchronized timelines across multiple videos. Our method performs robustly across diverse scenes.
and query Mast3R 33 within the dynamic instance masks, linking tracklets across views. Camera poses are estimated by VGGT [56]. We compute pairwise synchronization energy over the maximum overlapping time window between video pairs and evaluate energy across different offsets. Finally, we select reliable pairs for global synchronization. More details can be found in Appendix A
Datasets. We evaluate our method on comprehensive suite of multi-view video datasets capturing dynamic scenes. These datasets vary across several dimensions -innluiinnnnnliiinnn camera motion (static vs. dynamic), environment (indoor vs. outdoor), realism (real vs. synthetic), and motion type (human and non-human). CMU Panoptic [22 features a real-world indoor dataset with 30 static cameras captured at 30fps capturing human interactions. Egohumans 25 is challenging multi-view egocentric and static cameras capturing various sports taking place both indoors and outdoors at different resolutions.
3DPOP [37 is a large scale 2D 3D posture, identity and trajectory dataset featuring moving

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Table 1: Video Evaluation Results. For each dataset, we show mean and median errors (ms) for video metrics. We bold and underline the best and second best results respectively. Methods with indicates using GT camera pose as input. Without relying on any GT input, our method achieves the best overall performance across all four datasets, spanning diverse subjects and scenes.
Egohumans
CMU Panoptic
3D-POP
UDBD
Method
6 mmean
Smeak
mmcan
Smedy
mmean
8medk
8mean
dmed
Uni4D'
62
447.4
222.1
777.9
99.9
1600.1
1265.4
103.2
25.1
Mast3R
742.3
263.8
113.4
58.1
150.3
72.2
10.1
Sync-NeRF"
26
919.5
866.7
1138.9
1100.0
0.4
0.2
Ours
122.1
46.6
112.6
41.5
114.7
77.8
20.2
5.9
Table 2: Stage-1 Pairwise Evaluation Results. For each dataset, we show mean and median errors (ms) for pairwise metrics. We bold and underline the best and second best results respectively. Methods with indicates using GT camera pose as input. Our method outperforms other baselines on both metries across datasets.
Egohumans
CMU Panoptic
3D-POP
UDBD
Method
A@ 1001
A@500t
A@ 1001
A@
A@ 1001
A@ 5001
A@ 1001
A@ 5001
Uni4D
62
23.8
49.4
32.3
60.7
0.9
9.5
46.2
74.1
Mast3R
80
24.3
50.4
29.6
49.8
15.7
69.1
77.8
95.4
Sync-NeRF
26
3.0
13.8
0.0
8.2
86.7
97.35
Ours
33.9
55.8
26.0
51.2
33.3
69.3
82.1
94.3
pigeons. Unsynchronized Dynamic Blender Dataset (UDBD) is synthetic toy example created with dynamic blender assets used in SyneNeRF [26].
To prepare these multi-view datasets for multi-video synchronization, we take subsequences of each video while ensuring that they all have a common overlap. Each sequence roughly 10 seconds long, with a random cropping of around 2-3 seconds from the front and back to simulate offsets and unsynchronized videos. These offsets are used for evaluative purposes.
Baselines. For baselines, we explore leading methods using different strategies for multi-video synchronization. Following Uni4D 622] we adopt geometric approach using metric depth estimation to compute Chamfer distances between projected dynamic pixels. Ground-trit -tuth camera poses are used to triangulate scene points and resolve per-image scale ambiguity. For learning- based approach, we use Mast3r (30], which leverages attention and confidence maps shown to capture motion and rigidity Σ. We compute energy for each offset as the mean confidence between keyframe pairs within dynamic masks. SyRc-NeRF (26) incorporates temporal offsets into photometric optimization. We adapted its codebase for varying intrinsics, but excluded Egohuman due to egocentric camera challenges. For Uni4D and Mast3r, we compute pairwise offsets followed by global optimization, similar our method.
Metries. We evaluate both pairwise and per-video offset performance across all datasets. For pairwise evaluation. predicted offsets are compared with ground-truth relative offsets between every pair of cameras within the same dynamic scene. We report the AUC for eπтor thresholds at 100ms (A @ 100) and 500ms (A @ (00) respectively. For video evaluation, we compute single offset per-video while fixing the offset for the same reference camera. We report both the mean (ōmean) and median (8med) error. Since our datasets have different FPS, we report our results milliseconds. The mean synchronization error ( 100 ms) is heavily influenced by small number of extremely challenging camera views, as we did not exclude any views from our evaluation. Typically, the mean error is more sensitive to few challenging camera views, as we did not exclude any from evaluation.
4.2 Results
Our analysis of offset results across diverse datasets and baselines is detailed in Tab. (video synchronization) and Tab. (pairwise synchronization). Notably, on the challenging Eggoumanns dataset (25). VisualSync demonstrates superior performance, achieving successful video synchronization with a median error of just 46.6 milliseconds. The geometric approach, Uni4D, exhibits strong performance on datasets where dynamic objects are in closer proximity to the camera.
Quantitative.
7

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Table 3: Ablation of key components on Egohumans dataset. We bold and underline the best and second best results respectively. The 1st two rows show the oracle performance leveraging GT information as input. The 2nd block compares different camera pose estimations, the 3rd block compares different energy terms, the 4th block compare different solvers for global optimization. Our proposed pipeline achieves the best overall performance.
Design Choices
Solver
Pairwise
Video
Segmentation
Correspondence
Camera
Energy
A@ 1001
A@5001
8mean
8meat
GT
GT
GT
Sampson Sampson
IRLS IRLS
94.8
97.9
11.3
2.0
DEVA
CoTracker-Mas<3R
GT
56.0
85.0
72.4
28.6
DEVA
CoTracker+MusC3R
hloc
Sampson
IRLS
38.2
68.8
199.5
75.3
DEVA
CoTrackor+Mast3R
ransac
Inlier
IRLS
20.1
39.5
1656.5
1544.8
DEVA
CoTTacker+MMax3R
vggt
Cosine
IRLS
28.0
61.1
239.8
94.6
DEVA
MaTsacker+Mast3RR
yggt
Algehraic
IRLS
32.6
63.6
167.3
57.9
DEVA
CoTracker+Mast3R
vggt
Epipolar
IRLS
36.2
69.4
125.0
35.4
DEVA
CoTrackerMMastRR
vggt
Sampson
LS
20.3
56.7
205.9
118.0
DEVA
CoTracker+Mast3R
vggt
Sampson
IRLS
40.2
73.4
122.1
46.6
This suggests that more accurate metric estimations in these scenarios directly translate to improved alignment results. Conversely, Uni4D's performance significantly deteriorates on the 3D-POP dataset [37], where small, distant dynamic objects (pigeons) likely introduce inconsistencies in multi- view metric estimations. The energy landscape of Uni4D also noisy and there is no clear cues to remove spurious pairwise results, resulting in worse global synchronization. The optimization-based method, Sync-NeRF, jointly optimizes temporal offsets and photometric loss. However, consistent with findings in related work [10), Sync-NeRF struggles to calibrate more complex dynamic scenes beyond UDBD [26] in settings of large offsets like ours, likely due to a lack of strong priors and effective offset initialization. Interestingly, the learning-based approach, Mast3R [30], showcases surprisingly strong generalization capabilities. Despite not being explicitly trained for this task, outperforms the other two baselines on several datasets in both pairwise and video synchronization evaluations. However, its performance on the EgoHumans dataset (25] is notably weaker, potentially indicating a limitation in handling challenging egocentric views and motion blur.
Qualitative. We present qualitative comparisons on the EgoHumans dataset 25 against Uni4D and Mast3R in Fig. 4 As shown in Fig. 4 both methods struggle with highly dynamic motions and alignment. The timeline below shows ground-truth key events and sequence lengths. We further demonstrate our method on four datasets (Fig. [ and in-the-wild sports footage (NBA, EFL) (Fig. (6. effectively handling rapid motion, zooms, and dynamic camera movements across diverse scenes, Additional qualitative results can be seen in Fig. 13 and Fig. 14
4.3 Analysis
Key components. We evaluate the contribution of key components in framework through an ablation study on the EgoHumans dataset, as shown in Tab. S Note A@ 100 and A@ 505 are intermediate metries across all video pairs, including those with opposite viewpoints or temporal overlap. The first block reports oracle results using ground-truth segmentation, camera poses, and correspondences for all video peirs-regardless of overlap-dde with perfect inputs and serving as an upper bound. The second block examines different camera pose estimation methods. Our framework performs consistently across alternatives, with VGGT achieving the best results. Compared to the 28. 28.6ms oracle result (2nd row), using estimated camera poses from VGGT achieves 46. 6ms median error, demonstrating the robustness of our approach under imperfect pose inputs and its effectiveness in practical conditions. We further report camera pose and synchronization results (relative angular error in rotation and translation following VGGT) across randomly selected EgoHumans sports videos in Tab. 7 The third block ablates various pairwise energy terms used for synchronization. We include baseline method inspired by [3), which relies solely on dynamic tracklets and uses RANSAC to compute inlier matches as the pairwise energy metric. contrast, our method leverages both static background and camera pose information. We also evaluate three geometric energy terms cosine error, algebraic error [29]. and symmetric epipolar distance- which are detailed in Appendix A Among energy terms, the Sampson error performs best, as it explicitly models noise in the tracklets and provides lower bound
demaap--tmmmstttiiinggg near-perfect performance
8

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Table Ablation of input settings, spurious pair detection, and stage contribution on the Egohumans dataset. The first 3 rows ablate number of input pairs. RST denotes Random Spanning Tree using only the minimal number of pairs needed to form connectivity. For each setting, we run 10 times and report the mean and variance for each metric. Pairwise metrics in are computed after global sychronization except for 4th row. In 5th row, we show the importance of removing spurious pairs, Our method achieves comparable performance even only using 50% of pairs input.
Input Setting
Stage
Pairwise*
Video
Pairs Ratio
Spurious Det.
A@ 1001
A@ 500T
6mean +
Smert
RST
Full
19.5 1.9
43.2 + 3.1
436.4 63.1
130.0 + 24.5
50%
Full
28.9 + 0.5
59.8 + 1.0
212.6 + 9.5
70.7 + 1.3
80%
Full
35.4 1 0
69.2 = 0
144.4 0
44.7 1 0
100%
Stage-1
33,9
55.8
100% X
Full
30.7
58.1
371.4
111.5
100%
Full
40.1
73.4
122.1
46.6
Table 5: Ablation of varying frame rates on the CMU Panoptic dataset. We keep 30 fps for the constant setting and downsample them to 5-30 fps for the varying setting, achieving simi- lar performance without any pipeline changes.
Table 6: Ablation of low frame Rates on the CMU Panoptic dataset. Downsampling from 30 fps to fps causes a slight performance drop due to reduced temporal overlap, yet the method remains robust under the low-fps setting.
Input FPS
Smean
8meak
Input FPS 8meany
Smeak
Constant Varying
112.6
41.5
112.6
41.5
103.9
51.5
15
157.2
45.6
on the true epipolar error through a linear approximation under noisy estimates. The last block compares different solvers. A least-squares solver performs reasonably but is sensitive to outliers. Our IRLS-based solver achieves better accuracy by down-weighting unreliable estimates. Overall, our configuration achieves the best performance, validating the effectiveness of each design choice.
Input and different stage contributions. Tab, 4 evaluates the impact of input pair selection, spurious pair removal, and multi-stage optimization. In the first block, we ablate the number of input pairs. RST uses only the minimal set to form a connected graph, yet achieves < <10 ms median error. Even with 50% of pairs, performance remains close to the full setting, showing robustness to limited input. The second block highlights the benefits of spurious pair filtering and two-stage optimization. By exploiting energy landscape structure, our method filters unreliable pairs and improves global accuracy. The final row, using the full pipeline, yields the best results.
Input frame rate. To evaluate the robustness of our method under different temporal conditions, we conduct two ablation study on the CMU Panoptic dataset (22). In Tab. [5] we test synchronization across videos with varying frame rates (2nd row), sampling each video between fps and 30 fps. Our method is applied directly without any pipeline changes, achieving 51.5 ms performance-comee comparable demonstrannnnnnnggggggg strong adaptability to frame rate variations in real-world data. In Tab. 6 we test robustness to low frame rates by downsampling videos from 30 fps to 15 fps. While performance slightly degrades due to reduced temporal overlap, the method remains accurate, demonstrating resilience under sparse sampling. Notably, our preprocessing modules (Co-Tracker 23 and DEVA 9 are optimized for high frame rates, making 15 fps particularly challenging.
to 41.5 ms the original 30 fps
Runtime. Motion segmentation and VGGT (56) pose estimation run at 0.3 s and 0.35 per frame, respectively. Tracking (23) takes 120s per 10s video, and Mast3R (30) takes 60 s per video pair. Energy evaluation and global sync are efficient, taking under 10s and is per pair, respectively. Although our method is ( in the number of videos, we show in Tab. 4 that using only 50% of pairs or Random Spanning Tree (RST) offers a good trade-off. All runtimes are measured on single A6000 GPU. On the CMU Panoptic dataset (15 videas. 200 frames each), our method takes about 3.3 comparabee to Uni4D (3.9 hrs) and Syne-NeRF (4.2 hrs), though slower than MAST3R (1.2 hrs). Efficiency can be further improved with lightweight modules and additional computing resources with parallel preprocessing, making the method practical for offline multi-camera applications such sports analysis, film production, and surveillance.

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UnSiync Basketball
Visun VysunlSync Basketbail
UnSync Soccer
VisualSync Seccer
Figure 6: Qualitative Synchronization VisualSync In-the- Weid Sports Videos. We showcase VisualSync on challenging multi-view sports footage with large camera motions, motion blur, and zoom variations. Despite these real-world challenges, our method achieves accurate synchronization. In the absence of ground-trutt alignments, we qualitatively verify accuracy through the precise alignment of key events (e.g., ball release, contact) across views.
Bafore Sync
After Sync
GT Sync
RGB
T
Figure 7: K-Plane Rendering on VisualSyne Results. We train K-Planes on CMU Panoptic [22] multi-view videos for novel view synthesis. Unsynchronized inputs (Ist) produce blurry results, while our synchronized outputs (2nd) are sharp and comparable to ground-truth sync (3rd). The 4th column shows real images. Our method enables high-quality synthesis from unsynchronized inputs.
Limitations. Our method has three main limitations. First, it requires a subset of reliable camera poses (not necessary for the entire sequence). Second, it can't handle clips containing non-un non-uniform motion speeds for example, videos that alternate between slow-motion and fast-motion segments. Third, the pairwise estimation step scales quadratically (O(N2) with the number of videos, which can affect efficiency in large-scale setups. Failure case analysis of each module is shown in Fig. 12
4.4 Application
Video synchronization unlocks downstream applications like multi-view dynamic scene reconstruc- tion. As demonstrated Fig- [7] directly applying K-Planes [17] to unsynchronized data yields unsatisfactory novel view renderings. In contrast, Video Sync approach enables significantly improved novel view synthesis, achieving results comparable to those obtained with ground truth syn- chronized video. This demonstrates that our method can serve fundamental tool for downstream applications such as novel-view synthesis in real, multi-view unsynchronized dynamic world.
5 Conclusion
We presented VisualSync, a robust framework for synchronizing unposed, unsynchronized multi- camera videos with millisecond accuracy. By minimizing epipolar error over dense correspondences, it recovers precise time offsets across diverse scenes and motions. Experiments on four datasets show VisualSync outperforms recent methods. Our approach contributes a practical step toward enabling dynamic multi-view video motion understanding and related downstream tasks.
10

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Acknowledgements
This project is supported by NSF Awards #2525287, #2404385, #2414227, #2340254, #2312102. and #2331878, the IBM IIDAI Grant, and an Intel Research Gift. We greatly appreciate the NCSA for providing computing resources.

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chest 0.54
Figure 8: Grounding-DINE 32
SAM2 42 Segmentation results We visualize Grounding-
DINO's proposed bounding box given the dynamic labels produced by GPT4o, along with SAM2 segmentation results with confidence scores. Note that objects that are generally not dynamic (eg. basketball, football, chest) is identified as dynamic in these specific scenes due to inputting video frames into GPT4o.
Cotracker
Cotracker
Mast3R
Figure 9: Visualization of Cotracker and Mast3R correspondences We visualize actual spatial- temporal correspondences predicted using CotrackerV3 (temporal) and Mast3R (spatial).
A Implementation Details
Motion Segmentation. For dynamic object segmentation, we follow the pipeline of Uni4D [62] with key modifications. Unlike Uni4D, which relies on Recognize Anything (64) and LLM filtering, we directly feed video frames into GPT-4o to identify dynamic classes. The GPT4o Prompt used for automatic motion segmentation is shown in Fig. Every 20th frame sampled input, and the detected classes guide GroundingDINO 322 to generate bounding boxes, which then prompt SAM 2 for precise segmentation masks. As shown in Fig. 8 this pipeline achieves robust dynamic object segmentation, with DEVA g applied afterward for temporally consistent motion segmentation.
Spatial-Temporal Correspondence. To capture motion trajectories of dynamic objects, we apply CoTracker [24] to each video i within its dynamic region {M;, producing set of 2D tracklets (X*). Each tracklet x' (x' (4)} represents the observed image-space trajectory of a dynamic 3D point over time. To establish spatio-temporal correspondences across views, we perform cross-view matching using Mast3R [30]. For each tracklet x in video i, we seek its matching tracklet x in another video To ensure robust matching under asynchronous capture, keyframes from each tracklet and compute pairwise similarity between all keyframe pairs across views. For each pair of sampled frames, we construct candidate tracklet pair (x', x²) from the correspondences obtained by Mast3R 30 in the two views. The visualization output shown in Fig. 9
sample subset of
Correspondence Filtering. To further suppress noise, we leverage instance segmentation matching from DEVA filter correspondences across video pairs. We first construct pairwise matching matrix by counting correspondences between each instance pair over a sampled subset frames. Bipartite matching is then applied obtain optimal one-to-one assignments between instances in the two videos. To ensure reliability, we discard matched instance pairs with fewer than 100 correspondences. After this instance-level matching, we retain only Mast3R correspondences whose endpoints belong to the same matched instance. All remaining correspondences are aggregated into unified set of spatio-temporal matches, which serve as input to our energy-based based synchronization process.
Camera Parameters. We use VGGT [56] to extract camera poses and intrinsics in our preprocess- ing pipeline. For static cameras, we feed only the first frame as input, while for dynamic cameras, all available frames are used to estimate poses and intrinsics. To manage memory constraints, we subsample dynamic sequences to ensure that every offset computation has overlapping frames with predicted camera poses. The outputs of VGGT include fer-frame extrinsics (P SE(3)) and intrinsies K for each video i, where P = R tt]. These parameters are then used to compute relative poses and fundamental matrices. Example output is shown in Fig. I0
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Figure 10: VGGT [56] Predicted Camera poses compared to GT poses for all datasets We visual- ize VGGT predicted camera poses and ground truth camera poses for Egohumans [25], Panoptic [22], UDBD [26]. and 3D-POP (37] respectively. Different colors represent different multi-vier cameras, while the corresponding lighter palette represents ground truth camera poses.
Gontect
computer vasion ussiatant specialasd anstyeing htames ideniity cljects are ACTUALLY MOVINGG sequance, Your dentity catagarica otjects that wiibily acreso the provided fromes objects copocity to move, Focus only objects evidence movoment
postton ripe trmms namiing onjects:
Use toweroase cataggrriss
pansen??
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confidenty determine moving frames
Use generic clase thene mutriple just lut 'mann aach indivicual man)
Prompt
These are frames from viddoo Anolyze them and identtty ONLY cotago los onpeots that are ACTUALLY MOVING sonoso these frames. Return ONLY's JEONiessente with the toilewiing structure:
"dynamic") "oojjett" oojecti", "ogoect"]]
"reasoring" "detalled explanation how you idendified movement beoween fromes for object type*
For "dymammee tst:
usjuet categerries show visiste position chunggos framreaa
SINBULAR FORM objects (e.g.
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include
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"man"
"woman"
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cating they are walking running The tol.ateo it
Figure 11: GPT4o Prompt used for automatic motion segmentation We sample every 20th frame from video and input GPT4o with the following context and prompt to identify motion classes to be given to GroundingDINO module for robust video motion segmentation.
Sampson Error. We use the Sampson error in Sec. 3.1 to quantify epipolar constraint violations.
Below, analyze it as linearized approximation the true epipolar distance. Let x, =xx A); x (4) denote a noisy spatial-tempon correspondence at time / - in video and / in video j. Let 2, be the underlying clean correspondence. To quantify the deviation of the noisy observation x, from satisfying the epipolar constraint, we define the energy & as:
E² min Ae
|| x:
s.t. Clz,) = 0
This energy measures the minimal correction required to project x, onto the epipolar manifold defined by C(z,) O. The constraint function C(z,) evaluates the epipolar consistency between two views:
C(z,) x² (t A) F F x² (t)
where Fly I+A, is the fundamental matrix at frames + A and
Since this constrained optimization is nonlinear and difficult to solve directly, we approximate it by linearizing the constraint around x,, Using a first-order Taylor expansion, we obtain:
C(z.) C(x.) + Je(z, xx)
where J, is the Jacobian of C. with respect to x,- Solving for the optimal correction yields the Sampson approximation of 82, which provides first-order lower bound on original energy.
(x (t A) F.
HA, x (t))²
82 Sampson
(10)
F THA
(t) + FUT
HA, x'(t + A)
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camera pose, motion segmentation, and spatial correspondence modules. Specifically, observe the incorrect predicted camera poses for the dynamic camera (red) against the ground truth (pink). Background individuals are occasionally mis-segmented for motion segmentation, and some segmentations appear fragmented. Furthermore, Mast3R sometimes generates incorrect correspondences within dynamic masks.
Figure 12: Failure Case Visualizations We visualize failure
Table 7: Camera pose error vs. synchronization accuracy. Even with large rotation and translation errors in camera pose estimates (relative angular errors from VGGT), our method maintains low synchronization errors (e.g., 9.6 for Fencing and 19.4 ms for Tagging). The higher error in Tennis stems from distant camera placement with minimal observable motion.
Metric
Fencing
Volleyball
LegoAssemble
Badminton
Tagging
Basketball
Tennis
Cam Rot
10,9
8.5
3.1
10.2
5.8
4.0
2.7
Cam Trans Err +
14.0
7.1
11.8
12.2
13.9
6med 4
9.6
30.8
38.3
34.6
19.4
27.5
113.6
Other Energy Terms. Our ablation study also compares other three distinct geometric energy terms: symmetric epipolar distance, cosine error, and algebraic error. Following the definitions proposed by Terekohov al. (54), these terms are formally defined
(xx'(t A)) F x2) (t)2
((x'(t A)) F
(t)
Epipolar
(11)
F (111,,2
|[
A)Ii,
(xx'(+ A)) HA,
(x A (1)
(12)
Cosine
|xx(t+ +
(t)l22
I(F o (i A) (2
Ellgebratc (x' F
(13)
54 and experiments in Tab. 3, the Sampson emror is robust input noise. our setting, we extend the point-wise Sampson error to spatial-temporal formulation by considering tracklets input.
As shown in 44
B Failure Case
VisualSync depends on several upstream modules and thus sensitive to their prediction errors. As shown in Fig. [2] inaccuracies in camera pose estimation, motion segmentation, or correspondence matching can propagate through the pipeline and introduce error in synchronization. However, most unreliable cases could be detected by analyzing the pairwise energy landscape. Estimates with ambiguous or low-contidence minima are discarded to prevent degradation of global synchronization. Despite occasional failures, robust preprocessing effectively limits their impact, enabling Visual- Syne to maintain strong performance for in t-t-hh-wldd videos. Its modular design also ensures that improvements to individual components directly enhance overall synchronization quality.
C
Additional Results
Camera pose error vs. synchronization accuracy. We report camera pose and synchronization results (relative angular errors in rotation and translation following VGGT) across randomly selected EgoHumans sports videos in Tab. 7 demonstrating the robustness of our approach under varying pose estimation quality. Even with large pose errors, our method maintains low synchronization errors (e.g., 9.6 ms for Fencing and 19.4 ms for Tagging), indicating that pose noise does not directly lead to synchronization failure.
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CML-- Panoptic
UDBD
3D-POP
Egemmm
Figure 13: Qualitative Comparison VisualSync across datasets We show the synchronized videos CMU-Paree CMU-Panoptic, UDBD, 3D-POP and Egohumans dataset using VisualSyne. The top rows show the estimated synchronized time stamps from 3 different views. The bottom row shows synchronized timelines between multiple videos. Our method performs robustly across diverse scenes.
UnSync
VisualSync
Uni4D
M6st3R
Figure 14: Qualitative Comparison of synchronization on Egohumans across baselines We visualize synchronization results the challenging volleyball sequence in Egohumans. Notice that VisualSyne achieves the most accurate alignment even for egocentric views (orange highlight).
Qualitative results. To further demonstrate VisualSync's capabilities, we present additional qual-
itative results across 4 datasets, specifically Egohumans (5),, Panoptic 222 UDBD (26), and
3D-POP (37. in Fig. [3 We also provide comprehensive comparisons with baselines Uni4D [62
and Mast3R 30 in Fig. 14, Note that we excluded Sync-NeRF (66 for comparison as it struggled to produce meaningful offset predictions beyond its simpler native UDBD dataset. For visualization, show three selected camera views. The first view (blue highlight) serves as the reference, and corre- sponding frames from other views aligned accordingly. The timeline below depicts ground-truth keyframe alignments, marking the synchronized frame positions across camera sequences. These qualitative results demonstrate VisualSync's 's strong synchronization performance across datasets and baselines. Full video visualizations can be found in the supplementary material or on the project page bttps://stevenlsw.iihiuu wtt.thhhw....sssuassn.nnna
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Reviews:
[{"review_id": "72smpjVWub", "reviewer": "Reviewer_QBkg", "summary": "This paper presents a method for time-synchronizing multiple cameras by observing moving 3D points and ensuring they follow the epipolar constraints.   It achieves an error below 130ms.   It uses the following set of tools: camera parameters via VGGT, dense correspondences with CoTracker3, cross-view matches with MAST3R, and dynamic objects with DEVA.  The loss function, the Sampson error, is related to the squared Euclidean distance from a point to its corresponding epipolar line.  \n\nThe key challenge is that each of these measurements is noisy, and the total loss function across all camera pairs and trajectories is highly nonlinear.  The method employs an exhaustive search of time alignment to find the optimal time alignment and calibration between two cameras. \nThis is followed by iteratively reweighted least squares for the global optimization.", "questions": "None. \n\nThe authors have listed their weaknesses.  Addressing them is beyond the scope of this paper.   However, it is unclear whether NeurIPS is the proper venue for this work.", "limitations": "none.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "The algorithm is more effective than all existing strong baseline methods, including both geometric and learning-based approaches.   On the challenging Egohuman dataset, it reduced the time synchronization error by a factor of 4. \n\nIt also listed the weakness of the paper itself:\na) it depends on accurate camera poses;\nb) it fails when there is rapid motion or large viewpoint changes\nc) it does not handle frame rate variations.", "was_revised": true, "final_justification": "Thank you for the rebuttal. It addressed my concerns.", "rebuttal": "### a)  Dependency on accurate camera poses\n\nWhile our method requires predicted camera poses as input, **it does not rely on accurate poses for every frame**. In practice, a **subset of frames with reliable pose estimates is sufficient**, thanks to our design—particularly the **energy landscape optimization** and **robust filtering of spurious pairs**. This allows the method to remain resilient to pose noise and gracefully degrade under imperfect predictions.\n\nThis is empirically supported in two ways:\n\n1. **Pose error vs. sync accuracy** (table below): Even with large rotation and translation errors (relative angular error in rotation and translation following VGGT) in camera pose estimates, our method maintains low synchronization errors (e.g., 9.6 ms for *Fencing* and 19.4 ms for *Tagging*), indicating that **pose noise does not directly translate to sync failure**.\n\n2. **GT vs. estimated poses**: As shown in **Tab. 3 (2nd and bottom row)**, using estimated poses on the challenging *EgoHumans* dataset still yields a **median error of 46.6 ms**, close to the **28.6 ms** oracle result with ground-truth poses—demonstrating **robust performance even under imperfect pose inputs**.\n\nFurther, **Tab. 4 (5th row)** shows how our method filters out unreliable matches via the energy landscape, enhancing stability.\n\nWe acknowledge this limitation in our paper, but clarify that it is **not a binary failure mode**. We will make this point more explicit in the revision.\n\n| Sport         | Cam Rot Err ↓ | Cam Trans Err ↓ | δ median↓ |\n|---------------|---------------|------------------|-------------------|\n| Fencing       | 10.86          | 13.96             | 9.6               |\n| Volleyball    | 8.53           | 14.62             | 30.8              |\n| LegoAssemble  | 3.12           | 7.07              | 38.3              |\n| Badminton     | 10.24          | 11.82             | 34.6              |\n| Tagging       | 5.82           | 9.10              | 19.4              |\n| Basketball    | 4.00           | 12.23             | 27.5              |\n| Tennis        | 2.73           | 13.89             | 113.6             |\n\n> **Note:** High sync error in **Tennis** is due to **distant camera placement**, resulting in minimal observable motion (see supplementary video).\n\n--- \n\n### b) Rapid motion or large viewpoint changes:  \n\nWe demonstrate real-world effectiveness by applying our method to challenging in-the-wild videos, as shown in the supplementary material (**Supp Fig. 3 and Video**). These include NBA basketball sequences and FIFA soccer sequences with rapid shooting motion, diverse backgrounds, and varying camera viewpoints.\n\n---\n\n### c) Frame rate variations:\n\n\nFirst, we would like to clarify that our method can **synchronize videos with different (but known) frame rates very well**. In practice, frame rate information is usually available from video metadata, including for internet videos. Our main limitation is with videos that have varying motion speeds within a single clip (e.g., segments with both slow-motion and fast-motion or non-uniform motion), not with differing constant frame rates across videos.\n\nTo demonstrate robustness and adaptability of our method to varying frame rates, we further evaluated our method on the **CMU Panoptic** dataset underlying 2 challenging scenarios: **1) known varying frame rates** and **2) unknown varying frame rates**. Videos are sampled at frame rates of 5-30 fps. We directly apply our method **without any pipeline changes** in scenario 1). For scenario 2), we **remove the assumption of known frame rates** and follow the extension detailed in the **Supp. E**. We present experimental results along with synchronization performance on known fixed 30-fps reported in **Tab 1** for reference. Our method performs comparably under varying known FPS (median: **51.5 ms**) to the fixed 30 fps setting (median: **41.5 ms**). When frame rates are unknown, performance degrades gracefully (median: **80.5 ms**) due to additional uncertainty, demonstrating robustness of our framework even without frame rate metadata.\n\n| Method         | δ mean ↓ | δ median ↓ |\n|----------------|--------------------|--------------------|\n| Constant Known FPS        |     112.6      |     41.5       |\n| Varying Known FPS        |     103.9      |     51.5       |\n| Varying Unknown FPS        |     164.2      |     80.5       |\n\n---\n\n### d) Venue suitability:  \n\nWe believe our work is well-suited for NeurIPS, as it addresses a fundamental problem at the intersection of computer vision and machine learning, using tools from **visual foundation models** and **optimization-based inference**. Our approach combines insights from geometry, learning, and large-scale visual understanding, with clear implications for **downstream applications** such as 3D reconstruction and video understanding."}, {"review_id": "jVIbCWbTAw", "reviewer": "Reviewer_5Bep", "summary": "This paper introduces a 3D geometry-based method for synchronizing multi-view videos of the same scene that have different starting recording times. It leverages off-the-shelf techniques to acquire camera poses, cross-view correspondences, and per-view 2D point tracks. The authors develop an optimization method that uses epipolar constraints to optimize the time offset for each video. The paper evaluates its method on various multi-view datasets, demonstrating its outperformance over baseline methods. However, the proposed method has several limitations: it cannot handle videos with different FPS, assumes ideal camera poses, and its optimization can be hindered by outlier correspondence matches. Overall, despite addressing a practical problem, the proposed solution still has significant limitations.", "questions": "- The optimization operates on 2D point tracks at discrete timestamps. While a global offset estimation may approximate a continuous-time solution, it's unclear if this leads to sub-optimal results. If the input videos have low framerates (i.e, the point track observations become more discrete), will the optimization results significantly degrade?\n\n- In the experiments, many videos have stationary cameras rather than moving cameras. Would the stationary camera be a better anchor for synchronization? Would the method perform worse with a set of videos where all cameras are in motion?\n\n- The paper uses a neural radience field method (K-Planes) as a proxy to demonstrate the effectiveness of synchronization. However, K-Planes is not a state-of-the-art method. Is there any reason not to use more recent methods or explicit representation like dynamic 3D Gaussian splatting?", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 4, "strengths_and_weaknesses": "**Strengths**\n- This paper tackles a practical problem relevant to real-world multi-view video applications.\n- The authors perform a thorough evaluation across diverse multi-view datasets and include comprehensive ablation studies on the algorithm's design.\n- The overall presentation is clear and well-structured.\n\n**Weaknesses**\n- High sensitivity to input quality: The method's performance heavily depends on the accuracy of upstream components, specifically camera pose estimations, 2D point tracks, and cross-view correspondences. In Table 3, utilizing ideal ground truth data for poses and correspondences yields significantly higher accuracy (e.g., 94.8 vs. 40.2 in the metric AUC@100). A major limitation is the method's inability to refine or mitigate errors from imperfect estimations and outliers in these inputs, which inherently degrades the final synchronization results.\n- Insufficient quantitative performance: the quantitative evaluation reveals significant synchronization errors, averaging around 100ms. This margin remains substantial (e.g., ~3 frames at 30 FPS) and raises concerns about the method's practical utility for precise synchronization.\n- Limited frame rate handling: The method presented in the main paper assumes all input videos share an identical frame rate (FPS). While the supplementary material briefly demonstrates an extension for different frame rates, this crucial aspect lacks a thorough experimental evaluation.\n- Minor writing issue:\n  - Table 2 caption. \"we show mean and median error\", while this table shows AUC instead of errors.", "was_revised": true, "final_justification": "The rebuttal has addressed my concerns.", "rebuttal": "### a) Sensitivity to input quality  \n\nWe clarify that **AUC@100** in **Tab. 3** is an intermediate metric across all video pairs, including those with opposite viewpoints or no temporal overlap. For ground-truth evaluation (**1st** row in Tab. 3), we use dataset-provided GT poses and correspondences for all pairs—regardless of overlap—to demonstrate that our system produces near-perfect output when given perfect input. \n\nIn practical scenarios, the more relevant comparison is between the **bottom row** and **2nd** rows. On the challenging EgoHumans dataset, our framework achieves a median error of 46.6 ms, which is close to the 28.6 ms oracle result obtained with ground-truth poses—demonstrating robust performance with imperfect inputs.\n\nAs further shown in **5th** row in **Tab. 4**, our method leverages the energy landscape to filter spurious pairs, highlighting its robustness to imperfect inputs.\n\n ---\n\n### b) Quantatitive performance\n\nThe mean synchronization error ( \\~100 ms) is heavily influenced by a small number of extremely challenging camera views, as we did not exclude any views from our evaluation. The **Supplementary Video** provides visual examples to highlight the difficulty of synchronization (e.g. synchronization between third-person and egocentric cameras with rapid motion blur). Notably, the median error is much lower (\\~40 ms), indicating that our method achieves strong synchronization accuracy for the majority of cameras. We also report error percentiles on EgoHumans dataset below, showing that the higher mean is driven by a minority of extreme cases.\n\n\n| Percentile      | 25th       | 50th (Median) | 75th       | 100th (Max) |\n|-----------------|------------|----------------|------------|--------------|\n| Error (ms) ↓    | 16.3   | 46.6        | 156.9   | 1798.2   |\n\n---\n\n### c) Diverse frame rate handling\n\nFirst, we would like to clarify that our method can **synchronize videos with different (but known) frame rates very well**. In practice, frame rate information is usually available from video metadata, including for internet videos. Our main limitation is with videos that have varying motion speeds within a single clip (e.g., segments with both slow-motion and fast-motion or non-uniform motion), not with differing constant frame rates across videos.\n\nTo demonstrate robustness and adaptability of our method to varying frame rates, we further evaluated our method on the **CMU Panoptic** dataset underlying 2 challenging scenarios: **1) known varying frame rates** and **2) unknown varying frame rates**. Videos are sampled at frame rates of 5-30 fps. We directly apply our method **without any pipeline changes** in scenario 1). For scenario 2), we **remove the assumption of known frame rates** and follow the extension detailed in the **Supp. E**. We present experimental results along with synchronization performance on known fixed 30-fps reported in **Tab 1** for reference. Our method performs comparably under varying known FPS (median: **51.5 ms**) to the fixed 30 fps setting (median: **41.5 ms**). When frame rates are unknown, performance degrades gracefully (median: **80.5 ms**) due to additional uncertainty, demonstrating robustness of our framework even without frame rate metadata.\n\n| Method         | δ mean ↓ | δ median ↓ |\n|----------------|--------------------|--------------------|\n| Constant Known FPS        |     112.6      |     41.5       |\n| Varying Known FPS        |     103.9      |     51.5       |\n| Varying Unknown FPS        |     164.2      |     80.5       |\n\n--- \n### d) Optimization on low frame rates\n\nWhile our intermediate pairwise offset estimation operates at discrete timestamps, the **final synchronization output is continuous in time**. This design reflects the frame-based nature of input videos, point tracks, and camera pose estimates.\n\nWe also experimented with **interpolating point tracks and camera poses** to enable continuous-time matching. However, we observed **no significant performance improvement**, likely because the discrete matching better couples with the observed data.\n\nTo test robustness to low frame rates, we downsampled the **CMU Panoptic** dataset from original **30fps** to **15 fps**. As expected, performance slightly degrades due to reduced temporal overlap, but our method **still achieves reasonable synchronization accuracy**, demonstrating resilience under challenging conditions. We want to further add that our preprocessing methods, Co-Tracker and DEVA, are designed for video input and typically assume a high frame rate, making 15 FPS uncharacteristically low for optimal performance.\n\n| FPS         | δ mean ↓ | δ median ↓ |\n|----------------|--------------------|--------------------|\n| 30 (original)     |    112.6       |    41.5        |\n| 15     |       157.2    |      45.6      |\n\n---\n### e) Stationary vs. moving cameras \n\nIt is not necessarily true that static cameras make better anchors. The major benefit is static cameras often offer more accurate pose estimates, but they may suffer from **limited viewpoint or temporal overlap** with others. In contrast, dynamic cameras can provide **richer visual connections**, which our method effectively exploits.\n\nAs shown in our **supplementary videos**, our approach works well even when **all cameras are moving** or when **pose estimates are noisy**, including challenging in-the-wild cases.\n\nTo validate this, we conduct an ablation using **only dynamic cameras** in selected *EgoHumans* sequences—specifically those without opposing viewpoints (e.g., *volleyball*, *badminton*) or with two opposing players (e.g., *tennis*, *fencing*). As shown in the below table, the fist column (static & dynamic) leverages both static and dynamics cameras for synchronization while the second column (dynamic-only) only use dynamic cameras. We report the dynamic camera synchronization performance in the table. As show below, the synchronization performance remains strong with only dynamic cameras input, confirming that our method is **effective for both static and dynamic camera setups**.\n\n| Method         | Static & Dynamic             |                      | Dynamic-only             |                      |\n|----------------|---------------------|----------------------|---------------------|----------------------|\n|                | δ mean ↓  | δ median ↓    | δ mean ↓  | δ median ↓    |\n| Legoassemble       |         45.9            |    25.0                  |           50.0       |       50.0             |\n| tagging       |     31.1                |       25.9               |               25.0      |         0.0             |\n| basketball       |    29.1                 |   30.7                   |             12.5        |              0.0        |                    |\n\nWe also apply our method to in-the-wild videos (**Supp. Fig. 3 and Video**), including NBA basketball and FIFA soccer sequences where all cameras are moving. Our method still achieves good synchronization accuracy in these challenging real-world scenarios.\n\n---\n### f) Use of K-Planes vs. Dynamic 3D Gaussian Splatting\n\nOur method is **agnostic to the underlying radiance field representation** and can interface with a wide range of dynamic scene reconstruction models, including K-Planes, Dynamic Gaussian Splatting, or others.\n\nWe chose K-Planes in our experiments for consistency with Sync-NeRF [24]. We agree that more recent methods such as **dynamic 3DGS** offer compelling benefits in speed and quality. Incorporating such representations to demonstrate downstream application is a valuable and very possible direction, and we will do this in our  revision to further show the versatility of our synchronized outputs for downstream 3D reconstruction and rendering.\n\n ---\n## g) Minor\n\nWe will fix the writing issue in revision."}, {"review_id": "VGmw2ShlZx", "reviewer": "Reviewer_pwqc", "summary": "The paper presents a novel framework for synchronizing unsynchronized multi-camera videos capturing dynamic scenes. It addresses a common challenge in everyday scenarios—such as sports events, concerts, or family gatherings—where videos from multiple handheld devices lack time alignment. By leveraging cross-view object motion, the method recovers globally consistent temporal offsets with millisecond accuracy, enabling applications like 4D scene reconstruction and novel-view synthesis.", "questions": "See in Weaknesses.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. By integrating advanced models (e.g., CoTracker3 for trajectory tracking and MAST3R for cross-view matching), the method avoids training from scratch, thereby improving efficiency (Section 3.3, Stage 0).\n2. The method significantly outperforms baselines on Egohumans (δ_med=46.6ms) and CMU Panoptic (δ_med=41.5ms), particularly in complex scenarios.\n\nWeaknesses:\n\nThe core idea — that synchronized frames of dynamic points should obey epipolar constraints is simple, which seems like more of an effective integration of recent tools (e.g., VGGT, CoTracker, MAST3R). \n\n1. The method assumes a fixed frame rate, but in real-world videos, inconsistent frame rates may limit its applicability (Limitations). It is recommended to extend the approach to a variable frame rate model.\n2. It is recommended to add comparisons of resource efficiency between methods.\n\n3. Expand the comparison to include PoseSync[59] (human pose priors) and audio synchronization[46], strengthening the validation of the 'assumption-free' approach.\n\n4. There shall be many camera synchronization methods in the literature, including traditional and deep learning methods. The study and comparison seem not enough.", "was_revised": true, "final_justification": "The rebuttal and discussions addressed my concerns about the paper contributions. I tend to raise the score.", "rebuttal": "### a) Effective Integration of Recent Tools\n\nWe agree that the core idea—synchronized frames should obey epipolar constraints—is conceptually simple. However, we see this as a **strength** rather than a limitation. Synchronizing dynamic, unconstrained videos remains a highly challenging task, and a method that is both **simple and effective** is valuable in real-world settings.\n\nWhile our method builds on recent advances such as **VGGT**, **CoTracker**, and **MAST3R**, our **main contribution lies in the unified energy formulation and inference strategy** that integrates both static and dynamic cues to solve a **new problem these tools alone cannot address**. We appreciate the reviewer’s recognition of our integration efforts, and believe that the clarity of our formulation is key to the method’s robustness across diverse settings.\n\n---\n### b) Inconsistent frame rates\nFirst, we would like to clarify that our method can **synchronize videos with different (but known) frame rates very well**. In practice, frame rate information is usually available from video metadata, including for internet videos. Our main limitation is with videos that have varying motion speeds within a single clip (e.g., segments with both slow-motion and fast-motion or non-uniform motion), not with differing constant frame rates across videos.\n\nTo demonstrate robustness and adaptability of our method to varying frame rates, we further evaluated our method on the **CMU Panoptic** dataset underlying 2 challenging scenarios: **1) known varying frame rates** and **2) unknown varying frame rates**. Videos are sampled at frame rates of 5-30 fps. We directly apply our method **without any pipeline changes** in scenario 1). For scenario 2), we **remove the assumption of known frame rates** and follow the extension detailed in the **Supp. E**. We present experimental results along with synchronization performance on known fixed 30-fps reported in **Tab 1** for reference. Our method performs comparably under varying known FPS (median: **51.5 ms**) to the fixed 30 fps setting (median: **41.5 ms**). When frame rates are unknown, performance degrades gracefully (median: **80.5 ms**) due to additional uncertainty, demonstrating robustness of our framework even without frame rate metadata.\n\n| Method         | δ mean ↓ | δ median ↓ |\n|----------------|--------------------|--------------------|\n| Constant Known FPS        |     112.6      |     41.5       |\n| Varying Known FPS        |     103.9      |     51.5       |\n| Varying Unknown FPS        |     164.2      |     80.5       |\n\n---\n\n### c) Runtime comparison\n\nThank you for the suggestion. We have included a runtime and resource efficiency comparison against baselines such as **MAST3R**, **Uni4D**, and **Sync-NeRF** on the **Panoptic** dataset (15 videos, ~200 frames each) using **a single A6000 GPU**.\n\nOur method is **training-free**, and most of the overhead comes from **visual cue extraction**. While we acknowledge that further optimizations (e.g., lightweight modules) can improve runtime, **efficiency is not the primary focus of our work**, which targets accuracy and robustness under challenging conditions. With additional computing resources and parallel video preprocessing, the overall running time can be significantly reduced. We view efficiency-focused improvements as an important direction for future work. \n\n| Method           | Runtime (hrs)  |\n|------------------|---------------|\n| Uni4D* [58]       |       3.9        |\n| Mast3R [28]       |         1.2      |\n| Sync-NeRF* [24]   |       4.2      |\n| **Ours**          |       3.3        | \n\n### d) PoseSync and audio synchronization\n\nThank you for the suggestion. While both PoseSync [59] and audio-based synchronization [46] are valuable efforts, they are **not directly comparable** due to differences in assumptions and problem settings.\n\n**PoseSync** assumes a **single moving human** and leverages **pose priors**, whereas our datasets feature **multiple moving people** or **non-human subjects** (e.g., pigeons in *3DPOP* and dynamic objects in sync-NeRF), making direct comparison infeasible. That said, we agree it would be useful to include PoseSync on single-human datasets, and will add such experiments in the final version (beyond the rebuttal timeline).\n\n**Audio-based methods** rely on additional audio input and make different assumptions. While orthogonal to our visual-only setup, we agree it is important to **clarify this distinction**, and we will add a more detailed discussion in the Related Work section to better position our **assumption-free, general-purpose approach**.\n\n---\n\n### e) Additional prior works\n\nWe respectfully note that our selected baselines are **competitive and representative** for general video synchronization—an underexplored problem—spanning both **traditional geometric** and **deep learning** approaches. While we acknowledge the reviewer’s concern, the comment (“shall be many” and “seem not enough”) is a bit general to us. We would **greatly appreciate any concrete recommendations** on additional methods relevant and feasible for evaluation in our setting of **generic multi-view time synchronization**, and we will gladly consider incorporating them in our revision.\n\nIn the meantime, we will expand the **Related Work** section to better articulate our baseline choices and further situate our contributions within the broader literature."}, {"review_id": "EPQB5x3fnc", "reviewer": "Reviewer_dtE8", "summary": "This paper presents VisualSync, a training-free framework for synchronizing unsynchronized videos captured from multiple cameras. It formulates synchronization as an epipolar-based optimization problem and leverages pretrained visual foundation models to construct a robust three-stage optimization pipeline. Experimental results demonstrate superior performance across four diverse datasets, consistently outperforming strong baselines including SyncNeRF, Uni4D, and Mast3R.", "questions": "Please see the weaknesses section, especially the heavy reliance on the other models and the efficiency.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Paper Strengths\n1. Novel Insight.  The idea of leveraging epipolar constraints to achieve video synchronization is new to me.\n2. Complete Systematic Design. A training-free, well-designed three-stage inference pipeline, including visual cue extraction, estimating pairwise offsets, and global offset estimation, is proposed. \n3. Extensive Experiments. The method is evaluated on four diverse datasets (CMU Panoptic, EgoHumans, UDBD, and 3D-POP) with detailed ablations. \n\n\nWeaknesses\n1. Heavy reliance on external models:\nThe proposed pipeline depends on several pretrained models, including GPT, Grounded-SAM, DEVA, CoTracker3, Mast3R, and VGGT, to extract visual cues. This makes the system overly reliant on the robustness of these components and also leads to a too complex, inefficient pipeline. The performance is particularly sensitive to VGGT’s pose estimation quality, which is not always reliable, especially under challenging viewpoints or motion blur.\n\n2. Computational cost and scalability:\nAs noted in Lines 248–250, components like CoTracker3 and Mast3R are already computationally expensive. Combined with the method’s O(N²) pairwise synchronization strategy, this severely limits scalability, especially for latency-sensitive or large-scale applications. Furthermore, the paper lacks a runtime or efficiency comparison with existing baselines or competitors, which is important for assessing practical usability.\n\n3. Lack of failure case analysis:\nThe paper briefly mentions potential failure cases (e.g., fast camera motion, small dynamic objects), but does not provide qualitative examples or a detailed discussion. Including visual or diagnostic results for such scenarios would help clarify the method’s limitations and robustness boundaries.", "was_revised": true, "final_justification": "My final score for this paper is 'Borderline Accept'. The paper presents a neat idea, well elaborated and tested.", "rebuttal": "### a) Reliance on external models\n\nWhile our pipeline integrates state-of-the-art tools such as VGGT, MAST3R, and CoTracker, our core contribution lies in the robust energy formulation and inference framework, which is modular and robust to upstream noise.\n\nOur **energy function value itself serves as a robust indicator** for detecting unreliable outputs from preceding models. As shown in **Tab. 4 (5th and bottom row)** , our method leverages the energy landscape to **actively filter out spurious pairs**, allowing the system to tolerate failures in individual components—especially noisy pose predictions from VGGT.. This design ensures that **localized errors** (e.g., due to motion blur or egocentric viewpoints) do **not compromise overall synchronization**.\n\n\nCrucially, our method does **not require accurate poses for every frame**. In practice, a **subset of frames with reliable pose estimates is sufficient for robust synchronization**, thanks to our energy-based optimization and filtering of spurious pairs. As a result, our method gracefully handles imperfect predictions and remains robust in challenging scenarios.\n\n*EgoHumans* dataset presents severe challenges due to **egocentric viewpoints** and **abrupt head motion** (**see Supp videos for visualization of these challenges**). As shown in **Tab. 3 (2nd and bottom row)**, using estimated poses on the challenging *EgoHumans* dataset yields a **median error of 46.6 ms**, close to the **28.6 ms** oracle result with ground-truth poses. \n\nWe report **camera pose and synchronization results** (relative angular error in rotation and translation following VGGT)  across randomly selected EgoHumans sports videos below, demonstrating the robustness of our approach under varying pose estimation qualities. Even with large rotation and translation errors in camera pose estimates, our method maintains low synchronization errors (e.g., 9.6 ms for *Fencing* and 19.4 ms for *Tagging*), indicating that **pose noise does not directly translate to sync failure**.\n\n| Sport         | Cam Rot Err ↓ | Cam Trans Err ↓ | δ median ↓ |\n|---------------|---------------|------------------|-------------------|\n| Fencing       | 10.86        | 13.96          | 9.6                  |\n| Volleyball    | 8.53         | 14.62          | 30.8               |\n| LegoAssemble  | 3.12         | 7.07          |   38.3              |\n| Badminton     | 10.24         | 11.82          | 34.6                 |\n| Tagging       | 5.82         | 9.10          | 19.4              |\n| Basketball    | 4.00         | 12.23          | 27.5                 |\n| Tennis    | 2.73         | 13.89          | 113.6                |\n\n> **Note:** High sync error in **Tennis** is due to **distant camera placement**, resulting in minimal observable motion (see supplementary video).\n\nWe further demonstrate our system's effectiveness by applying our method to challenging in-the-wild videos, as shown in the supplementary material (**Supp Fig. 3 and Video**). These include NBA basketball sequences and FIFA soccer sequences with noisy estimated camera poses. Our method still achieves good synchronization accuracy in these challenging real-world scenarios.\n\n---\n\n### b) Computational cost and scalability\n\nWe have included a runtime and resource efficiency comparison against baselines such as **MAST3R**, **Uni4D**, and **Sync-NeRF** on the **Panoptic** dataset (15 videos, ~200 frames each) using **a single A6000 GPU**.\n\nOur method is **training-free**, and most of the overhead comes from **visual cue extraction**. While we acknowledge that further optimizations (e.g., lightweight modules) can improve runtime, **efficiency is not the primary focus of our work**, which targets accuracy and robustness under challenging conditions. In our experiments, we show our system could accurate synchronize 30 cameras on the **CMU Panoptic** dataset. With additional computing resources and parallel video preprocessing, the overall running time can be significantly reduced. \n\n| Method           | Runtime (hrs)  |\n|------------------|---------------|\n| Uni4D* [58]       |       3.9        |\n| Mast3R [28]       |         1.2      |\n| Sync-NeRF* [24]   |       4.2      |\n| **Ours**          |       3.3        | \n\nIn practice, our method is fast enough for typical multi-camera applications (e.g., 4-5 camera), such as movie production, sports analysis, and video surveillance, where offline processing is acceptable. We see further efficiency improvements (e.g., via lightweight models) as a valuable direction for future work, especially for very large-scale or latency-sensitive scenarios.\n\n---\n### c) Failure case analysis  \n\nWe provide detailed failure cases analysis of each module within our framework and qualitative examples in **Supp.F**. We will further improve the discussion of limitations and robustness of our method in future version."}]

Author rebuttal:

Meta-review: This paper presents Visual Sync, a training-free framework for synchronizing multi-camera videos using epipolar constraints and modern vision foundation models. The approach is novel in its unified energy formulation and shows strong empirical performance across several datasets, often surpassing prior synchronization methods. Reviewers valued the motivation, broad applicability, and solid results. Concerns remain about heavy reliance on external pretrained models, limited scalability due to O(N²) matching, and modest technical novelty beyond integration. Despite these limitations, the consensus is positive. Therefore, I recommend acceptance.
Decision: Accept (poster)