PoseCrafter: Extreme Pose Estimation with Hybrid Video Synthesis
Authors: Qing Mao
Tianxin Huang
Yu Zhu
Jinqiu Sun
Yanning Zhang
Gim Hee Lee
Venue: NeurIPS 2025 poster ()
Keywords: Pose estimation ; Video generation; diffusion model
Primary area: deep_learning

Abstract: Pairwise camera pose estimation from sparsely overlapping image pairs remains a critical and unsolved challenge in 3D vision. 
Most existing methods struggle with image pairs that have small or no overlap. Recent approaches attempt to address this by synthesizing intermediate frames using video interpolation and selecting key frames via a self-consistency score. However, the generated frames are often blurry due to small overlap inputs, and the selection strategies are slow and not explicitly aligned with pose estimation.
To solve these cases, we propose Hybrid Video Generation (HVG) to synthesize clearer intermediate frames by coupling a video interpolation model with a pose-conditioned novel view synthesis model, where we also propose a Feature Matching Selector (FMS) based on feature correspondence to select intermediate frames appropriate for pose estimation from the synthesized results. Extensive experiments on Cambridge Landmarks, ScanNet, DL3DV-10K, and NAVI demonstrate that, compared to existing SOTA methods, PoseCrafter can obviously enhance the pose estimation performances, especially on examples with small or no overlap.

PoseCrafter: Extreme Pose Estimation with
Hybrid Video Synthesis
Qing Mao Tianxin Huang Yu Zhu' Jinqiu Sun Yanning Zhang'f Gim Hee Lee
School of Computer Science, Northwestern Polytechnical University
School of Computing. National University of Singapore
3 School of Computing and Data Science, The University of Hong Kong
"Scoool of Astronautics, Northwestern Polytechnical University
maoqing@mail nwpu. edu. en ynzhang@nupu.eduuccm / htt.ho..o//moooiiguuo///eeeeetet
gimbee.lee@mus leeenus eec
Abstract
Pairwise camera pose estimation from sparsely overlapping image pairs remains a critical and unsolved challenge in 3D vision. Most existing methods struggle with image pairs that have small or no overlap. Recent approaches attempt to address this by synthesizing intermediate frames using video interpolation and selecting key frames via self-consistency score. However, the generated frames are often blurry due to small overlap inputs, and the selection strategies are slow and not explicitly aligned with pose estimation. To solve these cases, we propose Hybrid Video Generation (HVG) to synthesize clearer intermediate frames by coupling video interpolation model with a pose-conditioned novel view synthesis model, where we also propose Feature Matching Selector (FMS) based on feature correspondence to select intermediate frames appropriate for pose estimation from the synthesized results. Extensive experiments on Cambridge Landmarks, ScanNet, DL3DV- 10K, and NAVI demonstrate that, compared to existing SOTA methods, PoseCrafter can obviously enhance the pose estimation performances, especially examples with small overlap.
Introduction
Accurately estimating the relative pose between two images is fundamental task 3D vision, critical for enabling autonomous navigation, robotics, and augmented reality. While modern feature- based [ 2 or dearning-based pipelines [ perform well when input views share sufficient overlap. their performance deteriorates sharply in the small or no overlap case, owing to the fundamental scarcity of reliable feature correspondences.
To address this issue, InterPose 5 introduced a two-stage strategy: intermediate views are generated using a video interpolation model, and enriched frames selected with frame selection strategy provided to the pose estimation model. Although InterPose demonstrated obvious improvements extreme cases with small overlaps, faces two core limitations. First, the quality of generated intermediate frames remains a bottleneck. Free and open-source models like DynamiCrafter [6] can generate plausible interpolations near the input image pair but often fail to maintain geometric consistency in the central frames, likely due to the limited overlap between input views. Commercial models like Runway 7 and Luma Dream Machine [ offer sharper results but still suffer from blur and drift in challenging scenes, in addition to incurring high inference costs. Second, InterPose relies
Work fully done while first author is a visiting PhD student at the National University of Singapore. t Correspondingg author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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on a self-consisteney score mechanism to select frames, which filters out informative frames across multiple generated video clips. This purely statistical selection strategy slow and not explicitly aligned with the objectives of pose estimation.
To address these challenges, we propose PoseCrafter-a training-free framework for estimating camera poses from imput image pairs with small or no overlap. PoseCrafter comprises two key components: Hybrid Video Generation (HVG) and Feature Matching Selector (FMS). In HVG, we first use the pre-trained video interpolation model DynamiCrafter [6] to synthesize a coarse video and identify few reliable "relay" frames. These selected frames are then used to estimate the initial pose and generate high-fin seeh-fidliity intermediate views through pose- cose-condit novel view synthesis model, ViewCrafter [9] In this work. we find that selecting only the one frame immediately after the start frame and the one frame just before the end frame yields the most effective relay frames, Including additional frames tends to degrade performance due to increased blurriness in the central synthesized frames. More analysis can be found in Sec. 3.2
In FMS, instead of using statistical scores for frame selection, we propose simple but effective solution, evaluating synthesized frames based on their feature correspondences with the input image pair determine their suitability for pose estimation. In detail, we extract local descriptors from each candidate frame and match them to the input image pair, compute RANSAC [0]] inlier counts, and select the top & frames with the highest total inliers.
Extensive experiments on common benchmarks, including Cambridge Landmarks [T]] ScanNet [2]. DL3DV-10K 13), and NAVI [14] show that PoseCrafter can obviously improve the accuracy of pose estimation on extreme pose image pairs with small or no overlaps, without any requirements for additional training or ground-truth supervision. In summary, our contributions are as follows:
We propose Hybrid Video Generation(HVG) to synthesize high-fidelity intermediate frames, by effectively coupling a video interpolation model and pose-conditioned novel view synthesis model.
We develop Feature Matching Selector(MMS) deterministicallyy select the most infor- mative frames suitable for pose estimation, eliminating the need for expensive statistical self-consistency scoring in existing works.
We demonstrate that, by integrating HVG and FMS, our proposed PoseCrafter can achieve SOTA performances across four challenging benchmarks for extreme pose estimation.
2
Related Work
Extreme Pose Estimation Traditional relative pose pipelines combine local feature matching 21, RANSAC-based [10] five-point or eight-point solvers, and bundle adjustment [13]. These methods perform well when images share substantial overlap but fail under extreme cases. While deep networks have strengthened individual components 16 they still rely on the large overlap. To address cases with small or no overlap, several approaches 22 3 have been proposed. Yang et al. 120 alternate RGB-D scene completion with pose regression, iteratively fusing geometry from both scans; Cai al. 21 construct dense correlation volumes over synthetic video clips infer large regresses pixel-algned point maps to jointly recover depth, reconstruction, and pose without camera calibration. Temporal modeling has also been explored. Tang et combine attention and recurrent networks for pose prediction. However, each of these methods requires supervised data and training resources. More recently, video-diffusion techniques have been adapted for pose recovery. JOG3R (24] fine-tunes intermediate features of pre-trained video generation model for SfM-style estimation, InterPose (5) reuses standard diffusion interpolation and self-consistency scoring scheme to select the most reliable frames, both without retraining the video model. These studies highlight the power of synthesized intermediate views and motivate our PoseCrafter framework.
rotations; and DUSt3 3
Generative Video Models Early video synthesis methods relied on generative adversarial net- works (GANs) 25 and autoregressive models. GAN-based approaches such as TGAN (26). MoCoGAN [27 and StyleGAN-V (88 extend image GANs to model temporal coherence, but often suffer from frame inconsistencies and require complex architectures. Autoregressive tech-
niques 29 30]generate vidcos frame by frame, but they incur high computational costs and error

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Hybrid Video Generation
Feature Matching Selector
Iaput Image Pair
Rebrstt Reaay ationation
Pose- uideed
Video Refinement
rafter
VineCrafter
Matkad
Top
Prooee Networ
Hybrid Video,
Pose Model (egg DUSEER
Pose Model (eg. DUS(3R)
Pose Model (e.g. DUS(3R))
Figure 1: Overview of the PoseCrafter pipeline. Starting from input image pair (left), the Hybrid Video Generation module uses DynamiCrafter to interpolate a short video clip, extracts the most reliable "relay" frames, and refines the sequence using the pose-conditioned ViewCrafter reduce mid-sequence blur and drift. The Feature Matching Selector then evaluates each synthesized frame based its feature correspondence with the input pair, selecting the most informative views for pose estimation. These selected frames are then fed into a pose estimation model (e.g. DUSt3R) to estimate the final pose.
accumulation. Diffusion models have recently set a new standard for both image and video synthesis. Video-diffusion frameworks such VDM [31], Imagen-Video (32), and Make-A-Video [33] generate high-fidelityy, temporally coherent sequences via iterative denoising. ViewCrafter 9 proposed by combining video diffusion priors with coarse point-cloud cues synthesize high-fidelity, pose- controlled novel views from one or a few images. There are also some video generation models that be used for interpolation that use diffusion to generate smooth, geometry-aware intermediate frames. For example, DynamiCrafter [6] is based on motion-consistent editing. These diffusion- models outperform prior GAN and autoregressive methods in both visual quality and temporal consistency, making them ideal for downstream tasks such as extreme pose estimation.
Our Method
As illustrated in Figure given pair of images with small no overlap. PoseCrafter estimates their relative camera pose through two complementary stages. In Hybrid Video Generation (Section 3.2) we sequentially combine video interpolation model, DynamiCrafter, with a pose-conditioned novel view synthesis model, ViewCrafter, to generate refined video frames that significantly reduce mid-sequence blurring and correct structural drift. the subsequent Feature Matching Selector (Section 3.3) evaluate the synthesized frames based their feature correspondences with the input image pair and select the top most informative frames for final pose estimation.
3.1 Preliminaries
DynamiCrafter DynamiCrafter a publicly available diffusion model for video synthesis that employs progressive latent denoising architecture to animate two static images into dynamic sequences (6]. Given an input image pair (Lo, Ir e Hlx x3) and an optional text prompt P, generates dense sequence of intermediate frames using the following formula:
{ { = (l lr, P),
where GDC denotes the pre- pre-trained DynamiCrafter model. The variable E Z denotes the frame index, where To and Ir correspond to the input image pair.
3

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DC
Our
(R) Generated Video Frames
DC
(b) Confidence Images
Figure 2: Examples of mid-frame blurriness and confidence degradation. (a) presents frames generated by DynamiCrafter (DC) and our proposed method. (b) highlights high-confidence regions identified by DUST3R during pose estimation. Green boxes mark areas affected by blurriness in the DC-generated frames, while boxes indicate how these regions performed in DUST3R's confidence maps. We observe that the corresponding regions in the DynamiCrafter results exhibit notably low confidence, whereas our method generates frames with higher confidence for DUST3R.
ViewCrafter
ViewCrafter is a pose- condittnit diffusion model for novel view synthesis of static 3D scenes, which can generate high-fidelity novel views along an interpolated camera trajectory through input images following the formula:
(a) = Gvo Ir. (C))T
where Gvc denotes the pre-trained ViewCrafter generator. {Ct) denotes camera poses alongside the trajectory interpolated between the input image pair.
3.2 Hybrid Video Generation
Although InterPose (5] improves pose estimation performance by directly interpolating frames
between input image pairs (e-g., using DynamiCrafter (6), the generated sequences often exhibit noticeable blurriness and inconsistencies, particularly in the middle frames. As shown in Figure 2 these blurry regions result significantly lower confidence during pose estimation with DUSt3R (3].
ultimately affecting overall performance. In contrast, ViewCrafter (9) is capable of synthesizing clear, high-fidelity frames, but it requires input images that allow for the estimation of a plausible camera trajectory. To address this issue, we propose Hybrid Video Generation (HVG), which couples DynamiCrafter and ViewCrafter. Specifically, we first use DynamiCrafter to generate initial interpolated frames and then select a few reliable "relay" frames. These relay frames help ensure a plausible camera trajectory and are subsequently used by ViewCrafter to synthesize high-quality intermediate frames. As shown in Figure Our, intermediate frames generated by our HVG exhibit higher confidence scores in DUSt3R BJ, further confirming that HVG contributes positively to pose estimation. The whole process comprises two phases: Robust Relay- -raaye Generation, and Pose-Guided Video Refinement.
Robust Relay-Frame Generation In this stage, we use the pre-trained DynamiCrafter model to interpolate a short video clip from the input image pair (the start frame Ig and the end frame Ir). Although DynamiCrafter generates plausible interpolations near the input pair, it often fails to maintain geometric consistency in the central frames, weakening feature correspondences and degrading downstream pose estimation. To isolate the most reliable "relay" frames, we conducted study (T) that evaluated various subsets of interpolated frames. We found that retaining only the start/end frames and their immediate neighbors (lo, 41, IT-1, IT) minimizes rotation error and

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maximizes stability. Consequently, PoseCrafter forwards only these four frames as "relay" frames to the subsequent high-fidelity novel view synthesis stage.
Pose-Guided Video Refinement Given the four reliable relay frames (To, 11, TT-1 Jr), we first recover their camera poses {Co, C1 Cr-1, Cr} using the pretrained DUS(3R network. We then interpolate a smooth, dense camera trajectory {C²]e=o by applying spherical linear interpolation rotations on S3(3) and linear interpolation to translations R Conditioned on this trajectory and the reliable relay frames, ViewCrafter synthesizes high-fidelity intermediate views. This pose- conditioned refinement effectively removes mid- dedueneee blur and geometric drift, producing crisp. structurally accurate frames that markedly improve feature correspondences for final pose estimation.
Table Relay-ffanm frame sampling analysis using mean rotation error (MRE.). The setting #Frames=2 corresponds to {To. Ir) }. #Frames=4 corresponds to {Tu, I1, Iy-1, 1/,1,,,1 Jr #Frames= corresponds to {Io, I1, I2. Ir- Ir-1, Ir), and #Frames=8 corresponds {Iu, I1, I2, I3. IT- 3, Jr 1r-1.11i } The case #Frames=16 uses all frames. Results indicate that #Frames=4 consistently achieves the lowest MRE and the highest stability across datasets.
#Frames (n)
Dataset
2
4
6
Cambridge Landmarks
20.56
14.47
16.66
16.87
17.83
ScanNet
19.67
16.23
17.03
17.16
18.56
DL3DV-10K
15.22
14.27
14,40
14.73
14.52
NAVI
7.78
6.94
7.18
9.64
10.92
3.3 Feature Matching Selector
Although our proposed Hybrid Video Generation (HVG) produces high-fidelity and visually clear intermediate frames, not all of them are equally beneficial for pose estimation, as some generated content may be inconsistent with the input image pair. InterPose [5] addresses this problem by computing a statistical self-consistency score across multiple separate estimations. However, this approach is both slow and unstable due to the inherent uncertainty introduced by repeated sampling. Moreover, as a purely statistical measure, does not explicitly ensure that the selected frames are well-suited for pose estimation.
To overcome these drawbacks, we employ simple but effective deterministic selection strategy based on feature matching, named as Feature Matching Selector (FMS), For each synthesized frame It we extract local descriptors and match them to both the start frame Iy and end frame Ir, then compute RANSAC inlier counts No(t) and (t). Each frame is scored by
S(t) = No()) + NT((),
and we select the top A frames whose scores exceed preset threshold. This inlier-driven criterion avoids the need for repeated estimations and explicitly accounts for geometric consistency with the input image pair, enabling the selection of more informative frames for subsequent pose estimation.
4 Experiments
4.1 Experiment Setup
Data Preparation Following InterPose 5). we evaluate our method on four common benchmarks: Cambridge Landmarks [II]. ScanNet (12). Navi [14] and DL3DV-10K(13) For Cambridge Land- marks and ScanNet, we select test pairs by sampling images whose relative yaw difference falls into two ranges [50%-659] and [65--90") to evaluate performance under small and no overlap cases. For the object-centric Navi and DL.3V-10K datasets, due to the large object overlap, we adopt a single yaw range of [50% following InterPose's 's setting. All images are resized, and center-cropped to 512 x 320 before processing, to adapt to the setting of DynamiCrafter (6).
Evaluation Metrics Following prior work [ we conduct evaluations using the following metrics:
5

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Mean Rotation Error (MRE) The average geodesic distance between the predicted and the ground-truth rotation, reported in angular.
Mean Translation Error (MTE) The average geodesic distance between the predicted and the ground-truth translation, reported in angular.
Rotation and Translation Accuracy (R(0), T(0)) Given angular threshold 0 (e.g., 5", 15, 30%), the rotation accuracy R(0) is the fraction of estimates with rotation error below 0; the translation accuracy T(0) is the same definition using translation angular error.
Area Under Curve (AUC30) The normalized area under the rotation or translation error curve from 0" to 30 provides compact summary of performance across the full range of angular thresholds. In this work, we report the smaller of the rotation and translation AUC30 values as the final metric.
4.2 Implementation Details
In the hybrid video generation stage, first generate 16 interpolated frames between each input image pair using DynamiCrafter. From these, we select frames as reliable relay frames (as described in section 3.2), which are then used by ViewCrafter render 25-frame sequences for subsequent selection with Feature Matching Selector (FMS). In FMS, we extract 2,000 ORB keypoints per frame and compute RANSAC inlier counts with respect to both the start and end keyframes. The generated frames are then ranked based on their inlier counts, and the top k = 6 frames( frames(excluding the input image pair) are selected for the final pose estimation. All experiments were conducted on a single NVIDIA RTX 6000 GPU to ensure consistent evaluation environment.
4.3 Comparison with State-of-the-Art
To validate our method's effectiveness, we compare it against two closely related baselines: DUSt3R 3 and InterPose [5) DUSt3r estimates relative pose directly from an input image pair, while InterPosee enhances pose estimation results by using videos generated by a pre-trained video model and intermediate frames selected by self-consistency score. Since InterPose has not been publicly available, we reproduce its pipeline based on the details provided in their paper, and denote it as InterPose for subsequent comparisons. To further assess the impact of video generation quality, we also introduce two variant methods: our method without feature matching selector(Qurs, FMS), and InterPose without seff-consistency score (InterPose w/o scs)- In these cases, all of the synthesized frames are used for pose estimation, without specific selection.
4.3.1 Quantitative Comparison on PoseCrafter
Tables 2 and present comparative performance results of PoseCrafter against other meth- ods across four benchmark datasets. On outward-facing benchmarks (Cambridge Landmarks and ScanNet), PoseCrafter reduces mean rotation error by approximately 9.85" and increases R @ 30 by more than 10% against InerPose for data with yaw changes between [65'-90] of Cambridge Landmarks. These improvements demonstrate pipeline's 's superiority extreme pose estimation. Moreover, the comparison between InterPose scs and PoseCrafter,,s FMS further confirms that proposed hybrid video generation approach produces frames more suitable for pose estimation than the DynamiCrafter [6] used in InterPose
On center- facing benchmarks (DL3DV-10K 10K and NAVI) with larger image overlaps, PoseCrafter still yields modest but consistent reductions in rotation and translation error, alongside incremental improvements in AUC30. Overall, the statistical results across multiple standard benchmarks confirm that proposed pipeline based on Hybrid Video Generation and the Feature Matching Sclector, provides a stable, training-fres solution across diverse scenes and baseline conditions.
4.3.2 Qualitative Comparison on PoseCrafter
To intuitively assess the effectiveness of our approach, we present qualitative comparisons with DUSt3R and InterPose! on the previously mentioned benchmarks in Figure 3 In Figure 3 we visualize the input image pairs (with the start frame outlined blue and the end frame in yellow), point clouds reconstructed by DUS13R using only the input pairs, intermediate videos generated by InterPoset and PoseCrafter, and the corresponding estimated poses and reconstructions produced by DUS(3R for each video, respectively. Compared to DUS(3R, which only uses input image pairs,

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DUSIR (Input Image pains)
DUSIR (Ingut Genersted)
Input Images
Generated Video Frames
Cenbrigge
Ours
IntertPooll
Cambridges
Ours
IntesPooot
ScannNet
Ours
PP
InterrPose
SomPhe
Ours
Intee
TV 3EX33N---0 TO
Ours
InteeFoood
DLSOV- IOK
Ours
NAVI
Ours
Ours InterPPose
Bacoi
Figure 3: Qualitative comparisons on common benchmarks. Blue and yellow outlines indicate the start and end frames, respectively. In the DUS(3R reconstruction results, the visualized poses in yellow, and red represent the predicted pose of the start frame, predicted pose of end frame, and the fround-truth pose of the end frame, respectively.
blue,

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Table 2: Pose estimation Cambridge Landmarks. We report rotation recall (R @ 0 1), translation recall (T@ f), mean rotation error (MREL), and AUC30 f.
Yaw range [50"-65")
Yaw range (65"-90")
Method
Input
MREE
R@5
R@15
R@30
AUC
MRE.
R@5
R@15
R@300
AUCm
DUS:R
Puir
18.14
40.34
71.25
82.99
61.98
51.24
21.67
44.67
51.67
37.93
InterPose pio
DynamiCratter
16.11
42.70
75.70
87.35
65.72
42.51
30.67
42.51
61.33
47.18
InterPosel
DynomiCrafter
13.61
51.81
83,30
38.87
55.24
Ours
Hyhrid video
13.24
54.51
89.24
92.71
76. 13
34,87
31.33
68.33
77.67
56.29
Hybrid
Table 3: Pose estimation on ScanNet. We report rotation recall @R@@ 0 1), mean rotation error (MREL), mean translation error (MTE.), and AUCa0 1.
translation recall (T@ 0 f),
Yaw range Method
Input
R
R 15
R@:30
TW5"
TW15
T'6:3'
MREE
MIEE
AUC30
DUSL3
Pair
43,97
74.14
79.31
25.34
52.07
78.45
19.41
25.23
47.37
InterPose ses
DynamicCrafter
46.55
77.59
85.34
16.38
48. 10
64.74
17.51
35.25
42.69
50%-65"
InterPosef
DynamieCrafter
Our
Hybrid vidco
$1.72
87.07
93. 10
23.28
50.62
67.07
12.38
29.02
45.53
Ours
Hybrid video
53.45
88.79
94.83
33.62
65.52
77.69
10.77
22.14
57.03
DUSEAR
Pair
42.05
67.05
70.45
26.59
46.59
$3.86
30.82
29.99
36.50
InterPosel
DynomieCrafter
47.72
35.18
65"-90"
InterPose
DynamicCrafter
45.45
67.05
71.59
31.81
53.41
64.77
28.22
29.52
45.98
Our 19s
Hybrid video
57.95
22.61
Ours
Hybrid video
50.00
77.72
84.09
37.50
63.64
73.86
17.02
29.28
56.44
Table Pose estimation on DL.3DV10K with [50%-90" yaw range.
Method
Input
R@5
R@15"
R@30
T@5
15
T0"30"
MREJ
MTEJ
AUCaa T
DUS13R
Pair
34.33
63.00
94.66
27.00
75.00
92.67
13.36
10.88
55.58
InterPose
DynamicCrafter
36.33
64.33
95.00
26.00
76.33
92.67
11.27
55.68
InterPose
DynamicCratter
36.11
64.33
97.66
27.66
74.67
95.33
13.17
10.76
56.05
OurSus
Hybrid video
38.33
68.33
98.33
30.66
79.61
96.67
12.89
10.71
$7.16
Hyhrid video
38.10
10.28
$7.48
Table 5: Pose estimation on NAVI for the [509-909] yaw range.
Method
Input
R@S
R@15"
R@
T@5
1Ø15
T@30"
MRE.
MTEJ
AUC
DUS13R
Pair
64.69
95.72
98.05
62.37
97.28
98.22
7.30
7.82
82.37
InterPose SCS
DynamicCrafter
45.13
92.61
96.11
$7.86
91.44
96.50
8.81
78.63
InterPose!
DynamicCratter
66.53
97.28
98.83
67.70
96.89
98.84
6.61
6.26
82,80
Oursus
Hyhrid video
59.53
97.28
98.83
72.26
95.72
98.83
6.93
6.87
81.91
Hybrid video
70.82
97.67
98.83
75.10
98.44
5.46
83.98
Table 6: Runtime and Memory Cost.
Runtime
Memory Cost
Method
Video Generation
Pose Estimation
Video Generation
Pose Estimation
InterPose
3.2min
20.29min
14.6GB
3.1GB 3.6GB
Ours
3.8min
0m18nnn
22.8GB
PoseCrafter produces significantly cleaner and more complete geometry. addition, compared to InterPose method is able to correct blur and pose errors in the middle of the sequence, reducing drift and artifacts in the reconstructed point cloud, thus providing more reliable visual evidence for subsequent pose estimation.
4.3.3 Runtime and Memory Cost Discussion
Following the quantitative and qualitative evaluations, we next analyze the efficiency of our approach. Specifically, we compare runtime and memory cost with InterPose InterPose generates video sequences for each image pair and samples 11 frames from each sequence to compute the self- consistency score, resulting computationally heavy pipeline. contrast, our method adopts streamlined design: generate only single hybrid vidco and apply a deterministic frame selection

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Table 7: Ablation study on Hybrid Video Generation. SCS and FMS denote the frame selection strategies from InterPose [S] and ours, respectively.
Method
Input
MREJ
R@5
@ 15"
@@30
AUC30 1
DUS(R
Puir
18.14
40.34
71.25
82.99
61.98
InterPose +
DynamicCrafter
16.11
42.70
75.70
87.50
65.72
SCS
InterPose
DynamicCrafter
13.60
51.81
81.50
83.30
70.47
InterPose FMS
DynamicCrafter
13.02
52.08
85.76
90.63
73.93
ViewCrafteru.e FMS
ViewCrafter
13.80
52.78
82.29
88.54
71.12
ViewCrafterw FMS
ViewCrafter
12.45
53.82
84.03
90.28
72.82
Oursa FMS
Hybrid video
13.24
54.51
89.24
92.71
76.13
Ours sCs
Hybrid video
12.11
54.86
88.54
91.32
76.11
Ours
Hybrid video
11.40
55.21
89.93
93.75
77.41
Table 8: Ablation study on feature matching methods used in FMS.
Feature Matching Method
MRE.
5
R 15
R@30
AUCao 1
ORB
11.40
55.21
89,93
93.75
77.41
RoMa
13.89
53.13
88.89
91.67
74.76
LoFTR SIFT
12.41
54.51
89,24
93.40
76.30
13.89
53.13
88.89
92.67
74.77
SuperPoint
13.36
51.74
89.93
92.71
75.48
Table 9: Ablation study on different video interpolation models used in Hybrid Video Generation.
Method
Video Interpolation Model
MREJ
R@5"
R@ 15
R@ 1030
AUC30
DUSt3R
18. 14
40.34
71.25
82.99
61.98
Ours
DynamiCrafter ToonCrafter
11.40
55.21
89.93
93.75
77.41
Ours
12.08
53.99
87.16
89.19
73.11
Table 10: Ablation study on the number of intermediate frames selected by FMS.
#Frames
MRE.
R@5
R@ 15
R@ 30
AUC30 f
11.36
55.90
89.93
93.40
77.59
11.40
55.21
89.93
93.75
77.41
11.93
55.90
89.93
92.10
76.23
strategy based on feature matching. This not only avoids redundant video synthesis but also eliminates repeated self-consistency evaluations.
As summarized in Table 6, our approach substantially reduces runtime in the pose estimation stage while maintaining comparable even superior accuracy. Although the hybrid video generation incurs slightly higher memory usage than InterPose, the overall cost remains affordable for practical deployment. These results demonstrate that our framework strikes favorable balance between efficiency and accuracy, underscoring its scalability to real-world applications.
4.4 Ablation Studies
In this section, we present ablation studies on the two key components our framework: Hybrid Video Generation and the Feature Matching Selector. We further investigate the impact of different video generation models and the number of selected frames. All experiments are conducted on the Cambridge Landmarks benchmark under yaw changes of [509-659)
Hybrid Video Generation Table evaluates pose estimation performance for three video- generation variants without any frame selection: DynamiCrafter (InterPose: (nterPosely/osc)) w/os ViewCrafter Viewerafterw/ooxxS and our hybrid video generation (Oursw/o OurSw//FMS achieves the lower mean rotation error and higher recall than generated results from DynamiCrafter and ViewCrafter. This confirms that our hybrid video generation strategy, combining the motion-driven

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interpolation of DynamiCrafter with the eaeeconnitionedd novel-view synthesis of ViewCrafter, effectively addresses the limitations of each method, enhancing the fidelity of intermediate frames for improved pose estimation.
Frame Matching Selector In Table g evaluate the performance of different keypoint selection algorithms within the Frame Matching Selector (FMS), including RoMa (34), LoFTR [16), ORB (35), SIFT M. and SuperPoint [36]. We observe that ORB performs best in our case, which may be attributed to its robustness against color blurring and distortions in synthesized frames, making it more effective to select the most informative frames.
Impact of Video Interpolation Model To further validate the generality of our pipeline, we experimented with ToonCrafter [37], recent interpolation model designed for cartoon-style data but also known for handling large-motion scenarios efficiently. As shown in Table | ToonCrafter performs slightly worse than DynamiCrafter, which is likely due to its training bias towards non- photorealistic content. Nevertheless, it still achieves clear improvements over directly using the input image pair. These results confirm that our hybrid pose estimation framework is compatible with variety of video interpolation backbones, highlighting its flexibility and robustness.
Table 10 analyzes the impact of varying the number of frames & selected by the FMS module, where & is 4, 6, and 8 (excluding the input image pair). The results show that k 6 yields the best overall performance. Using fewer frames may not provide sufficient information for accurate pose estimation, while using more frames can introduce errors due to artifacts in the generated frames.
Impact of Selected Frame Count
5 Limitation
Although the effectiveness of our method has been validated across multiple diverse benchmarks, there are still certain cases where it may not perform well. For example, in our HVG stage, both the adopted video interpolation model (DynamiCrafter) and the novel view synthesis model (ViewCrafter) face challenges when the start and end frames exhibit significant illumination differences, resulting in synthesized frames with low confidence for subsequent pose estimation. This issue could potentially be mitigated by integrating relighting model, such as IC-Light [38, to harmonize lighting conditions. In addition, our FMS module also encounters difficulties when the image texture is overly uniform, which hinders reliable feature matching. We will explore these limitations in future work.
6 Conclusion
In this work, propose PoseCrafter, simple yet effective framework for estimating camera poses from input image pairs with small or no overlap commonly referred to as extreme viewpoint changes (5). Estimating poses under such challenging conditions remains a difficult problem, as minimal shared visual content often leads to poor matching and unreliable geometry. Rather than relying solely on video interpolation to synthesize intermediate frames, we introduce Hybrid Video Generation (HVG), which produces clearer and more geometrically consistent frames. HVG couples the vanilla interpolation model DynamiCrafter with a pose-conditioned novel view synthesis model, ViewCrafter, allowing it to refine and correct structural inconsistencies and reduce mid-sequence blur. To further improve pose estimation accuracy, we also introduce Feature Matching Selector (FMS). This module identifies the most informative frames by evaluating their feature correspondences with the imput image pair, ensuring that only geometrically meaningful frames suitable for pose estimation selected. Through extensive experiments multiple widely used benchmarks [5]. including challenging scenes with extreme viewpoint differences, we demonstrate that PoseCrafter consistently outperforms existing state-of-the-a methods in both accuracy and robustness.
Acknowledgement. This research project supported by the National Research Foundation (NRF) Singapore, under its NRF- Investtstiigatorhhip Programme (Award ID. HIRF-RRFI9-0000 and the China Scholarship Council under Grant Number 2023062901 43. also supported by the National Engineering Laboratory for Integrated Aero-Space- Sene-SpaceGGround--eeee Big Data Application Technology and by the National Natural Science Foundation of China (NSFC) under Grant Nos. U19B2037, 61901384, and 61971356.
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A Camera Pose Estimation Results based on MAS13R
To further validate the generality of PoseCrafter on different pose estimation backbones, we replaced DUSt3R [3] with MASt3R [ another transformer-basee framework augmented with dense local feature head and efficient sparse matching. We evaluated this configuration on four benchmarks with varying yaw ranges: Cambridge Landmarks (50--65) ScanNet (50%-659), DL3DV-IOK (50*-90)), and NAVI (50%-90"). As shown in Table and Table 22 using MASt3R directly for pose estimation results in lower accuracy compared DUSt3 possible explanation is that the sparse matching algorithm employed by MASt3R may be less effective for image pairs with small or no overlap. Nonetheless, our proposed framework consistently improves estimation accuracy over the baseline pose estimation models, including MASt3R. This demonstrates that our approach generalizes well across different pose estimation backbones by synthesizing intermediate frames that are more suitable for pose estimation.
Table 11: Camera pose estimation results on outward-facine datasets (Cambridge Landmarks and ScanNet) based on MASt3R. We report rotation recall (R @ 8 t), translation recall (T@ 0 1), mean rotation error (MRE),, mean translation error (MTE.). and AUCa0 1.
Cambridge Landmarks
ScanNet
Method
Imput
R@5
Ror15
R0:30
MRE
a
RØ5
RG11
TW25"
TG:15
T9300
MRE
MIE
ALCa
MASSIR
Per
9:03
43.75
57 99
$1.61
36.15
25.00
55.17
62.95
6.90
50.17
48.26
36 16.36
50.13
26.84
MASSMA
Hybri viden
25.00
39.83
69.10
41,92
49.83
31.90
45.52
77.59
18.97
45.69
63.79
20.97
38,50
49.66
Table 12: Camera pose estimation results on center-facing datasets (DL3DV-10K and NAVI) based on MASt3R. We report rotation recall @R@ 1), translation recall (T@ t), mean rotation error (MREL), mean translation error (MTE.), and AUC30 1.
Dataset
Method
Input
R@5
R@15
R@300
T@5
16:15"
T'030"
MRE
MIE
AUC1a
DL3DV-1OK
MAS13
Pair
7,00
63.67
97.00
26.67
72.67
91.67
15.18
13.31
$3.49
(MAS(M)
Hybrid video
7.67
68.67
97.33
30.00
76.33
92.00
14.09
12,91
55.36
MAS(3
Pair
43.97
93.39
96.89
50.97
89.49
97.67
8.30
7.43
76.54
NAVI
Ouss(MAS33R)
Hybrid video
43.97
93.77
97.28
51.75
91.83
97.28
7.71
6.98
77.26
Table 13: Additional comparison with VGGT on Cambridge Landmarks. We report mean rotation error (MRE4), rotation recall (R t), and AUC30 1.
Method
Input
MRE
R R@5
R@ 15c
R R@30
AUC30
Dust3R
Pair
18.14
40.34
71.25
82.99
61.98
Ours
Hybrid video
11.40
55.21
89.93
93.75
77.41
VGGT
Pair 20.17
40.00
70.17
82.29
60.54
VGGT
Hybrid video
17.88
42.43
84.40
85.76
65.15
B Additional Comparison with VGGT
To further analyze the generality of our pipeline, we conducted comparative experiments with VGGT 39 on the Cambridge Landmarks dataset under yaw changes of [509-65)].
We first evaluated the two models in challenging cases where only image pairs with small or no overlap were provided. As shown in the second and fourth rows of Table 13 DUS(3R consistently outperforms VGGT on such data.
We then integrate these two models into our pipeline and evaluate their performance. As shown in the third and fifth rows, both configurations achieve significant improvements over their directly estimated counterparts. The version using DUSt3R achieves a higher accuracy than the version using VGGT. These results demonstrate that our method compatible with different pose estimators and consistently enhances their performance.
21

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Overall, DUS:3R appears better suited than VGGT for small- or non-overlapping image pairs, and our framework provides more noticeable improvements when combined with DUSt3R. While fine-tuning VGGT on small-overlap data or adjusting the hyperparameters of our pipeline may further enhance its performance, we leave such extensions for future work.
C Comparison of DUSt3R Confidence-based Selection and FMS
We compare DUSt3R confidence-based frame selection with our proposed Feature Matching Selector (FMS) to evaluate whether confidence scores can serve as a viable alternative. In this setting, top- ranked frames were selected from hybrid vidcos using four different confidence thresholds (20%, 40%, 60%, and 80%). The results are summarized in Table [4
When the threshold was set to 20% or 40%, pose estimation accuracy decreased compared to using the full hybrid video sequence. Increasing the threshold 60% 80% improved performance, but the accuracy still remained lower than that achieved by our proposed FMS.
In terms of efficiency, confidence- must be processed by DUSt3R to compute confidence maps prior to selection. This step incurs significant time and memory costs, and higher thresholds further increase runtime more frames selected. By contrast, our FMS requires only single feature extraction step, immediately identifying the informative frames with superior efficiency and accuracy. These findings highlight the practical advantages of our proposed FMS over confidence-based selection in both computational efficiency and pose estimation performance.
selection introduces substantial overhead, as all video frames
Table Comparison of DUSt3R confidence- based selection and FMS on Cambridge Landmarks. We report pose estimation results under yaw changes of [60%- Frames selected from hybrid videos using four DUS:3R confidence thresholds (20%, 40%, 60%, and 80%). We report mean rotation error (MRE)), rotation recall (R @ 0 f).AUC30 f and pose estimation time.
Method
MREJ
R 5
R @ 15
R 30
AUC30
Pose Estimation Time
Conf(20%)
14.66
54.17
85.07
89.24
72.45
2. 2.79min
Conf(40% %))
14.36
57.30
87.15
90.97
74.24
2.91min
Conf(60%)
12.35
54.17
90.28
93.06
76.60
3.47min
Conf(80%
12.41
53.47
90.63
93.06
76.46
4. 4.13min
Ours FMS
13.24
54.51
89.24
92.71
76.13
2.56min
Ours
11.40
55.21
89.93
93.75
77.41
0.18min
D Visualization Results of Limitation
Although PoseCrafter achieves robust results across various benchmarks, certain challenging scenes still degrade its performance. In the hybrid video generation (HVG) stage, severe illumination differences between the start and end frames will introduce obvious artifacts in the synthesized intermediate views. The red-boxed regions in Figure 4 hiighlghh these artifacts, which would be marked as low confidence and discarded in the downstream pose estimation backbone, e.g., DUSt3R in this work. Such an obvious removal of information may significantly degrade the final performance. Moreover, in the feature match selector (FMS) module, scenes dominated by uniform or repetitive textures hinder the reliable extraction and matching of key points, resulting in fewer RANSAC [IO] inliers and lower pose accuracy. The red lines in Figure slshow examples of incorrect correspondences in such scenes.
E More Visualizations of Our Generated Videos
To further illustrate the superiority our proposed hybrid video generation(HVGG) we present additional side-by-side comparisons of intermediate frames produced by DynamiCrafter, ViewCrafter, and PoseCrafter. In F DynamiCrafter (6) delivers temporally smooth transitions but exhibits progressive blur and geometric drift in the middle of generated sequences. Although ViewCrafter [9]
22

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Generated Video Frames
Confidence Images
Generated Video Frames
Confidence Images
Generated
Video Frames
Confidence Images
Figure 4: Hybrid Video Generation artifacts under severe illumination differences. Blue and yellow outlines indicate the start and end frames, respectively. Confidence images denote images filtered with the predicted confidence map in the subsequent DUSt3R model. The red boxes highlight regions affected by artifacts in these cases. We can observe that these regions have quite low confidence.
Vidco Framess
Geverstel
Feature Matching Resulls
Video Frames
Gauratal
Festure Musching Results
Figure Feature Matching Selection failures in low-texture regions. Blue and yellow outlines indicate the start and end frames, respectively. The red and green lines indicate incorrect and correct correspondences, respectively. Incorrect correspondences lead errors in inlier counting, affecting the accuracy of subsequent frame selection.
can produce sharp results with minimal blur, using only input image pairs with small overlap often leads to structural artifacts and misalignments, To address these limitations, combine DynamiCrafter and ViewCrafter to complement each other's strengths. Specifically, we first use DynamiCrafter to synthesize intermediate "relay" frames, effectively augmenting the input image pair with frames that have larger overlaps. These relay frames are then passed to ViewCrafter to generate clearer and more geometrically consistent results. As shown in Figure 6 our proposed approach successfully produces frames that are both visually sharp and structurally reliable.
23

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Input Images
Generated Video Frames
De
mirm
vC
Onrs
DC
VC
PEXT
Figure 6: Comparative video synthesis results. Each row shows intermediate frames synthesized between the same start frame (blue box) and end frame (yellow box). generated by different methods: DynamiCrafter (DC), ViewCrafter (VC), and our Hybrid Video Generation (Ours). DC produces smooth motion but exhibits progressive blur and geometric drift in the middle of sequences (high- lighted in red circle). Since VC is sensitive to the pose of the input image pair, it tends to produce structural misalignments in our small-overlay setting (highlighted in red box). By coupling DC and VC together, our method delivers sharp, geometrically consistent video frames throughout, correcting both the blur of DC and the misalignments of VC.
24


Reviews:
[{"review_id": "T5AMPPuL3R", "reviewer": "Reviewer_7zgU", "summary": "The paper PoseCrafter introduces a method for camera pose estimation in scenarios with extremely sparse visual input, specifically when input images have little to no overlap. The proposed pipeline begins by generating intermediate \"relay\" frames between the sparse input views using a pretrained video generation model, DynamicCrafter. From the generated sequence, the frames adjacent to the original start and end images are selected as the most reliable relay candidates. These are further refined using ViewCrafter, resulting in a higher-quality intermediate video sequence.\n\nTo identify the most informative synthesized views for downstream pose estimation, the method employs a Feature Matching Selector. For each generated frame, local features are extracted and compared with those from the original start and end frames. The top-K frames with the highest feature match scores are selected. These selected frames, along with the original inputs, are then passed to DUSt3R for final pose estimation.", "questions": "1. Is the relay frame selection strategy ({I₀, I₁, I_T−1, I_T}) generalizable beyond the Cambridge Landmarks dataset?\n2. Clarify the frame sampling setup in Table 1?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "## Strengths\n- The paper is well written and easy to follow\n- Experiments show promising improvement over previous methods.\n\n## Weaknesses\n1. The design choice for selecting the \"most reliable relay frames\" was based on an ablation study on the Cambridge Landmarks dataset, which is also one of the evaluation benchmarks (Table 1). This raises a concern about generalization: the heuristic might be overly tuned to this dataset and not transferable to other settings with different scene dynamics or motion characteristics. Besides this, this is deterministic and simple, yet not justified theoretically. It could be too heuristic and ad hoc, lacking a principled foundation.\n2. The method looks like a combination between different off-the-shelf models. The contribution lies mainly in how these components are connected and the heuristics used (e.g., frame selection), which may be seen as an engineering effort rather than a fundamentally new approach.\n3. The description of the ablation (Table 1) is unclear. It does not clearly specify how intermediate frames beyond 4 are sampled (e.g., does #Frames=6 mean {I₀, I₁, I₂, I_T−2, I_T−1, I_T}?)\n\n## Minor Weaknesses\nThe method mainly combines existing models DynamiCrafter + ViewCrafter + Duster.", "was_revised": true, "final_justification": "I appreciate the detailed author response and I don't have further questions. I'd like to keep my original positive score.", "rebuttal": "We thank the reviewer for their positive feedback regarding the clarity of our paper and the performance improvements demonstrated in our experiments. We provide our response to all concerns raised by the reviewer as follows:\n\n**Q1: Concerns about generalization and the heuristic nature of the relay frame selection strategy.**\n\nWe thank the reviewer for raising this important point. We would like to clarify that although our relay frame selection strategy was initially inspired by empirical observations on the Cambridge Landmarks dataset, it is actually transferable to other data with different settings.\n\nWe have extended our evaluation beyond the Cambridge Landmarks dataset to include ScanNet, DL3DV-10K, and NAVI. These datasets feature diverse scene types and motion patterns. The updated results shown in the table below indicate that the selection of only the start and end frames along with their immediate neighbors {${I₀, I₁, I_{T−1}, I_T}$} as the four relay frames consistently produces the lowest mean rotation error and the highest stability. This trend holds across different datasets and settings. It supports our intuition that using more frames may introduce geometric inconsistencies, and too few relay frames can lack sufficient information for accurate pose estimation.\n\nAlthough the technique to select relay frames seems to be simple and deterministic, it has proven to be surprisingly robust and effective across datasets with varying settings. We can further explore its principled proof in our future work. We will also include these new experimental results and add more clarification in the revised version.\n\n| **Dataset**             |     |     |**#Frames (n)**|     |     |\n|:--------------------------|:----------------:|:-----:|:-----:|:-----:|:----:|\n|                         |         2         |   4   |   6   |   8   |  16  |\n| Cambridge Landmarks |20.56 | **14.47** | 16.66 | 16.87 | 17.83 |\n| ScanNet             |19.67 | **16.23** | 17.03 | 17.16 | 18.56 |\n| DL3DV-10K           | 15.22| **14.27** | 14.40 | 14.73 | 14.52 |\n| NAVI                | 7.78 | **6.94**  | 7.18  | 9.64  | 10.92 |\n\n\n**Q2: The method appears to be a composition of off-the-shelf components.**\n\nDespite building on existing components, our method addresses a fundamentally challenging and under-explored problem: **camera pose estimation under small or non-overlapping images**. Most existing methods fail under these conditions due to the lack of reliable visual correspondences.\n\nWe further clarify our core contributions in the following:\n\n- We couple the video interpolation model DynamiCrafter with the pose-conditioned NVS model ViewCrafter. This combination forms our Hybrid Video Generation (HVG) framework. Our HVG synthesizes high-quality intermediate frames to support more accurate subsequent pose estimation.\n- We incorporate robust feature descriptors to propose the Feature Matching Selector (FMS) module. Our FMS deterministically selects the most informative frames for pose estimation. This eliminates the need for expensive statistical self-consistency scoring used in existing methods.\n- By integrating these components, we propose a simple yet effective pipeline for reliable pose estimation between image pairs with small or even no overlap. Experiments confirm that our method consistently outperforms existing approaches in these challenging scenarios.\n\nOur work goes beyond a pure engineering effort. It reshapes the role of generative models in supporting geometric tasks and extends the applicability of existing pose estimators. We believe that our contributions to this work can benefit the community and inspire further research in related areas.\n\n**Q3: The description of the ablation (Table 1) is unclear.**\n\nWe thank the reviewer for pointing out the ambiguity. We apologize for the lack of clarity in Table 1 and will revise the table caption and description in the main text for better readability. Specifically, the setting #Frames=6 corresponds to {${I₀, I₁, I₂, I_{T−2}, I_{T−1}, I_T}$}, and #Frames=8 corresponds to {${I₀, I₁, I₂, I₃, I_{T−3}, I_{T−2}, I_{T−1}, I_T}$}."}, {"review_id": "LRmwGBNoLH", "reviewer": "Reviewer_8hif", "summary": "This paper introduces, PoseCrafter, a novel approach to improve extreme-view pose estimation using video models. Given a pair of input images with minimal visual overlap, PoseCrafter combines a video interpolation model and a pose-conditioned novel view synthesis model to provide informative context for pose estimation methods, e.g., DUSt3R. To select the most informative generated video frames, PoseCrafter uses feature matching between those generated frames and the input images. The video frames with most inlier matching are selected for pose estimation. The proposed method is verified on multiple benchmarks and compared preferably with prior method, e.g., InterPose.", "questions": "1. The improvements achieved by the proposed method appear to be marginal on NAVI and DL3DV compared to the other datasets. Do the authors have any intuition as to why this might be the case?", "limitations": "The authors discuss the technical limitations of the proposed method; however, its potential negative societal impacts are not addressed. Since the method uses video generative models to synthesize intermediate frames, users should be cautious about the content of the generated videos, as they may reflect biases inherent in the underlying video models.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The proposed method is intuitive and straightforward. It leverages the recent progress in video generative models to improve pose estimation under extreme viewpoints.\n2. The writing is clear and easy to understand.\n3. The proposed method compares preferably with prior method on four datasets.\n\nWeaknesses:\n1. Incomplete evaluation: (a) The proposed method is only tested on a single video interpolation model, i.e., DynamiCrafter, which may make the results biased. Could authors provide experiments on more (recent) models? (b) In terms of feature matching methods, could authors provide results on more recent dense matching methods, e.g., RoMa [1]? (c) The authors should provide an analysis of the inference time for frame selection in both the proposed method and the baseline, InterPose.\n2. An easy alternative to the proposed feature matching approach would be to simply run DUSt3R again on the generated hybrid video and keep the frames with high confidence. The reviewer is curious about how this approach would perform in practice (in terms of both accuracy and inference time).\n\n[1] Edstedt et al., RoMa: Robust Dense Feature Matching, CVPR 2024.", "was_revised": true, "final_justification": "The additional experiments provided by the authors during the rebuttal, including inference time analysis, alternative video models, and frame selection strategies, highlight the effectiveness of the proposed method and resolve my concerns. I therefore raise my score to 5.", "rebuttal": "We thank the reviewer for the thoughtful and constructive feedback. Our responses to the concerns raised by the reviewer are as follows:\n\n**Q1: The proposed method is only tested on a single video interpolation model. Could authors provide experiments on more (recent) models?**\n\nSince our task focuses on input image pairs with small or no overlap, we require video interpolation models that can handle large motion or significant viewpoint changes.\n\nTo validate the generality of our pipeline, we additionally experimented with ToonCrafter [1]. This recent model is known for its efficiency in handling large-motion interpolation.\nWe evaluated the model on the Cambridge Landmarks benchmark under yaw changes ranging from [50$^{\\circ}$-65$^{\\circ}$]. As shown in the table below, ToonCrafter [1] performs slightly worse than DynamiCrafter. This is likely because ToonCrafter is primarily designed for cartoon-style data. However, it still achieves clear improvements over directly using only the input image pair. These ***results confirm*** that our hybrid pose estimation framework ***is compatible*** with a variety of video generation backbones. We will include them in the revised version to emphasize the flexibility and robustness of our approach.\n\n| **Method**   | **Video Generation Model** | **MRE↓** | **R@5°** | **R@15°** | **R@30°** | **AUC₃₀↑** |\n|-------------|-----------------------------|----------|----------|-----------|-----------|-------------|\n| DUSt3R   |   None   | 18.14    | 40.34    | 71.25     | 82.99     | 61.98       |\n| Ours     | DynamiCrafter | **11.40**| **55.21**| **89.93** | **93.75** | **77.41**   |\n| Ours     | ToonCrafter | 12.08    | 53.99    | 87.16     | 89.19     | 73.11       |\n\n**Q2: Evaluation on more recent feature matching methods such as RoMa[2].**\n\nWe follow the recommendation of the reviewer to incorporate RoMa as an alternative feature matching method within our *Feature Matching Selector* and evaluated it on the Cambridge Landmarks benchmark under yaw changes of [50$^{\\circ}$-65$^{\\circ}$].\n\nAs shown in the table below, RoMa selected a different set of intermediate frames compared to ORB, while its performance falls behind ORB. One possible reason is that RoMa may not be robust enough to handle our synthesized intermediate views, as it was trained primarily on the natural high-quality images. In contrast, ORB demonstrates greater robustness in our scenarios and offers a significant advantage in computational efficiency. For these reasons, we adopt ORB for the Feature Matching Selector (FMS) in this work. We will include these comparative results and analysis in the revised version.\n\n| **Feature Matching Method** | **MRE↓** | **R@5°** | **R@15°** | **R@30°** | **AUC₃₀↑** |\n|-----------------------------|----------|----------|-----------|-----------|-------------|\n| RoMa                    | 13.89    | 53.13    | 88.89     | 91.67     | 74.76       |\n| **ORB**                     | **11.40**| **55.21**| **89.93** | **93.75** | **77.41**   |\n\n**Q3: Inference time analysis.**\n\nWe thank the reviewers for the valuable suggestions and have added a comparison of the inference time required for frame selection between our method and InterPose. For a fair evaluation, we define the pose estimation time as the duration taken to obtain the final pose. For **_InterPose_**, this includes the time spent computing the Self-Consistency Score. For **_our method_**, it includes the time spent using the Feature Matching Selector. We then compare the two methods based on this definition. Since the original implementation of InterPose is not publicly available, we re-implemented its pipeline based on the details provided in the paper. We refer to this reproduction as InterPose$^‡$ for subsequent comparisons.\n\nAs shown in the table below, our method offers a substantial advantage in computational efficiency. In contrast, InterPose requires the generation of 8 video sequences for each input image pair. From each sequence, it samples $m = 11$ frame subsets with 10 random samples and 1 uniformly spaced sample. It then computes pose estimates for each subset. A self-consistency score is evaluated across these subsets to determine the final pose. This process incurs significant computational overhead due to repeated inference and scoring steps.\n\nIn contrast, our method leverages a simple yet effective deterministic frame selection strategy based on feature matching. Features are extracted only once, and the most informative intermediate frames are selected directly for pose estimation. This significantly reduces time costs while maintaining highly competitive accuracy. These results highlight the practical benefits of our pipeline for large-scale applications. We will include this analysis in the revised version.\n\n| **Method**              | **Frame Selection Strategy**     | **Pose Estimation Time** |\n|-------------------------|----------------------------------|---------------------------|\n| Ours                | Feature Matching Selector        | **0.18 min**              |\n| InterPose$^‡$         | Self-Consistency Score           | 20.29 min                 |\n\n**Q4: Alternative: Using DUSt3R confidence to rank video frames.**\n\nWe thank the reviewer for suggesting the use of DUSt3R confidence scores as an alternative strategy for video frame selection. To evaluate its effectiveness, we conducted experiments by selecting top-ranked frames from hybrid videos based on four confidence thresholds (20%, 40%, 60%, and 80%). The corresponding results are shown in the table below.\n\nAnalysis shows that setting the threshold to 20% or 40% results in decreased performance of pose estimation compared to using the hybrid video sequence. Increasing the threshold to 60% or 80% improves the results, but the accuracy still lags behind our proposed feature matching selector (FMS).\n\nIn terms of computational efficiency, confidence-based selection incurs considerable overhead. Specifically, all video frames must first be processed by DUSt3R to compute the confidence map before any selection can be made. This step alone incurs significant time and memory costs. Furthermore, as the threshold increases, the number of selected frames increases, and the overall time continues to grow. In contrast, our FMS achieves superior efficiency and accuracy with only a single feature extraction step to immediately select the most informative frames. We will incorporate this analysis into the revised version to highlight the practical advantages of our approach in terms of computational efficiency and pose estimation performance.\n\n| **Method**               | **MRE↓** | **R@5°** | **R@15°** | **R@30°** | **AUC₃₀↑** | **Pose Estimation Time** |\n|--------------------------|----------|----------|-----------|-----------|------------|---------------------------|\n| Conf(20%)            | 14.66    | 54.17    | 85.07     | 89.24     | 72.45      | 2.79min                   |\n| Conf(40%)            | 14.36    | 57.30    | 87.15     | 90.97     | 74.24      | 2.91min                   |\n| Conf(60%)            | 12.35    | 54.17    | 90.28     | 93.06     | 76.60      | 3.47min                   |\n| Conf(80%)            | 12.41    | 53.47    | 90.63     | 93.06     | 76.46      | 4.13min                   |\n| Ours$_{w/o FMS}$         | 13.24    | 54.51    | 89.24     | 92.71     | 76.13      | 2.56min                   |\n| **Ours**                 | **11.40**| **55.21**| **89.93** | **93.75** | **77.41**  | **0.18min**               |\n\n**Q5: Smaller gain on NAVI and DL3DV-10K.**\n\nWe appreciate the reviewer’s observation. Our method is specifically designed for the challenging setting where the input images have small or no overlap. As a result, its improvements are particularly significant on outward-facing datasets such as Cambridge Landmarks and ScanNet, where the overlapping regions between widely spaced views are minimal. In contrast, center-facing datasets such as DL3DV-10K and NAVI present a different scenario. Although the camera angles vary significantly, the captured objects remain largely centered. This results in substantial visual overlap between frames. Consequently, baseline methods already perform relatively well on these datasets with little room for improvement. The smaller performance gains in these cases are therefore expected and consistent with the nature of the problem being addressed. We will include this explanation in the revised version to clarify the performance differences between dataset types and to better highlight the strength of our method in small-overlap scenarios.\n\n[1] Xing J, Liu H, Xia M, et al. Tooncrafter: Generative cartoon interpolation[J]. ACM Transactions on Graphics (TOG), 2024, 43(6): 1-11.\n\n[2] Edstedt J, Sun Q, Bökman G, et al. Roma: Robust dense feature matching[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 19790-19800."}, {"review_id": "kuPJl7HH1L", "reviewer": "Reviewer_6p5A", "summary": "The paper presents a novel method for pose estimation under extreme viewpoint changes. It builds upon a recent state-of-the-art method that proposes to solve the wide-baseline problem by using generative models to synthesize novel views closing the viewpoint gap between the query images. It introduces a two step method that uses DynamicCrafter as a prior on the interpolated trajectory, and then refines the generated views with ViewCrafter and keyframe selection scheme to achieve high fidelity images. Lastly, a robust feature matching scheme is used to select only reliable images before using Dust3r for the final prediction", "questions": "- although I acknowledge it is a concurrent work, I would be very curious of the performance of the novel VGGT[3] model in this context. Supposedly it improves a lot upon the baseline of Dust3r. I think authors should consider adding it either as a replacement for Dust3r in the proposed pipeline or a standalone competitor. I do not think it is mandatory but could make the paper more interesting\n- I am curious about the choice of using ORB descriptors. did the authors experiment with a dense comparison from a backbone as in [2]? or with a pairwise matcher such as RoMa, LoFTr\n-  some related works should be cited. [1, 2] employ a similar render&compare framework, although not for wide-baseline task. Especially the feature matching algorithm is very similar to the one employed in [2].\n[1] Zhang, Zichao, Torsten Sattler, and Davide Scaramuzza. \"Reference pose generation for long-term visual localization via learned features and view synthesis.\" International Journal of Computer Vision 129.4 (2021): 821-844.\n[2] Trivigno, Gabriele, et al. \"The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.\n[3] Wang, Jianyuan, et al. \"Vggt: Visual geometry grounded transformer.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n[4] Edstedt, Johan, et al. \"RoMa: Robust dense feature matching.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.\n[5] Sun, Jiaming, et al. \"LoFTR: Detector-free local feature matching with transformers.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The experimental evaluation is comprehensive and it shows convincing results with nice improvements over SOTA\n- the paper is well written and easy to follow\n-  The proposed method is simple, meant in a positive way. There is not a substantial novelty and it's a nicely engineered pipeline, that however shows remarkable results and it's a nice application of generative models for closing the wide baseline gap in pose estimation tasks.\n\nWeaknesses:\n- the method relies on the video generated by DynamicCrafter, which being a diffusion-based model is subject to variability to plausible hypothesis and might result in an early point of failure of the pipeline \n- given that one of the claims \"against\" the prior work InterPose is the cost of generating multiple video hypothesis, the theme of efficiency should be discussed. Since the paper is using multiple foundation models for video generation plus a refinement scheme, as a reader I am unable to estimate the runtime or memory requirements. Including such discussion is fundamental, in my opinion. even if it takes minutes for an image pair, it's ok, but it should be mentioned\n- the ablation on the feature of choice is not very comprehensive of methods", "was_revised": true, "final_justification": "I did not find any major concerns in the paper in the first place. The additional ablations and experiments on feature matching and inference time/memory footprint are useful for readers to better understand the problem and the proposed solution, strengthening the paper.\nI think the contributions are solid and experimental results convincing, thus I raise my score to 5.", "rebuttal": "We thank the reviewer for the thoughtful and constructive feedback. We give our response to each concern in the following:\n\n**Q1: Would the variability of diffusion-based DynamiCrafter causes early point of failure?**\n\nWe thank the reviewer for raising this important concern. To evaluate the impact of generative stochasticity in DynamiCrafter, we conducted experiments on the Cambridge Landmarks dataset under yaw changes of [50$^{\\circ}$-65$^{\\circ}$] using 5 different random seeds to generate intermediate video sequences. The results are shown in the following table. We can find that our method consistently outperforms other methods within a smaller range of variations. This indicates that while some variability is inherent to the diffusion-based generative process, our pipeline remains robust and stable across different runs. We will include this analysis in the revised version to better illustrate the reliability of our method.\n\n| **Method**                            |**Input**        | **MRE↓**           | **R@5°**             | **R@15°**            | **R@30°**            | **AUC₃₀↑**            |\n|:--|:--|:--|:--|:--|:--|:--|\n| Dust3R                              | Pair             | 18.14              | 40.34                | 71.25                | 82.99                | 61.98                  |\n| InterPose$_{w/o SCS}$$‡$            | DynamiCrafter    | 16.11              | 42.70                | 75.70                | 87.35                | 65.72                  |\n| InterPose$^‡$                       | DynamiCrafter    | 13.61              | 51.81                | 81.50                | 83.30                | 70.47                  |\n| Ours$_{w/o FMS}$                       |Hybrid Video     | 13.37±0.29              | 54.57±0.35                | 88.43±0.76                | 92.34±0.56                | 74.65±1.32                  |\n| **Ours**                            | Hybrid Video     | **11.46 ± 0.03**   | **55.38 ± 0.54**     | **90.94 ± 1.11**     | **94.54 ± 0.86**     | **77.49 ± 0.51**       |\n\n**Q2: Runtime and Memory Cost Discussion.**\n\nWe thank the reviewer for highlighting the importance of efficiency. Although InterPose generates 8 video sequences for each image pair and samples a subset of 11 frames from each video to compute the self-consistency score, our approach adopts a more streamlined and efficient pipeline. Specifically, we generate only a single hybrid video and apply a deterministic and efficient frame selection strategy based on feature matching.\n\nTo address the reviewer's concerns, we provide a detailed comparison of runtime and memory usage at different stages. For fair evaluation, we divide the pipeline into two stages: video generation and pose estimation. The video synthesis stage in both our method and InterPose is referred to as video generation. The subsequent process using the self-consistency score in InterPose or the feature matching selector in our method is referred to as pose estimation. Since the original implementation of InterPose is not publicly available, we re-implemented its pipeline based on the details provided in the paper. We refer to this reproduction as InterPose$^‡$ for all subsequent comparisons. As shown in the table below, our approach significantly reduces pose estimation time while achieving comparable or even higher pose accuracy. Although our approach incurs a slightly higher memory cost during video generation, it remains affordable for practical usage. We will include this analysis in the revised version to clarify the computational efficiency and scalability of our approach.\n\n| **Method**  |   Video Generation Runtime         |   Pose Estimation  Runtime   |   Video Generation Memory Cost    |  Pose Estimation Memory Cost  |\n|:--------------------------|:----------------|:-----|:-----|:-----|\n|  InterPose$^‡$ | 3.2min                        | 20.29min                      |14.6GB                        | 3.1GB                        |\n| Ours                | 3.8min                        | 0.18min                       |22.8GB                        | 3.6GB                        |\n\n**Q3: Evaluation with RoMa [4] and LoFTR [5].**\n\nTo evaluate the impact of different feature matching strategies, we conducted experiments on the Cambridge Landmarks dataset under yaw changes of [50$^{\\circ}$-65$^{\\circ}$], using RoMa and LoFTR as alternatives to ORB in our Feature Matching Selector (FMS). \n\nInterestingly, we observed that each matcher led to the selection of a different subset of intermediate frames, ultimately affecting the pose estimation outcome. As shown in the table below, both RoMa and LoFTR resulted in inferior results compared to ORB. We believe the possible reason lies in the difference in robustness across methods. Learning-based methods such as RoMa and LoFTR may perform less reliably on our synthesized intermediate views. This is likely because they are trained on high-quality natural images. In contrast, ORB is more appropriate in our setting due to its strong robustness and higher efficiency. We will include these comparative results and analysis in the revised version to further demonstrate the robustness and efficiency of our frame selection strategy.\n\n| **Method**  | **MRE↓**           | **R@5°**             | **R@15°**            | **R@30°**            | **AUC₃₀↑**            |\n|:-------------|:-------------|:-------------|:-------------|:-------------|:-------------|\n| RoMa    | 13.89    | 53.13      | 88.89       | 91.67       | 74.76        |\n| LoFTR   | 12.41    | 54.51      | 89.24       | 93.40       | 76.30        |\n| **ORB**     | **11.40** | **55.21** | **89.93**   | **93.75**   | **77.41**    |\n\n**Q4: How would VGGT [3] perform in our context?**\n\nWe sincerely thank the reviewer for the valuable suggestions. To evaluate the performance of VGGT in our pipeline, we conducted comparative experiments on the Cambridge Landmarks dataset under yaw changes of [50$^{\\circ}$-65$^{\\circ}$].\n\nWe first evaluate the two models in complex scenarios where only image pairs with small or no overlap were input. As shown in the second and fourth rows of the table below, DUSt3R consistently outperforms VGGT on our data with small or no overlap.\n\nWe then integrate these two models into our pipeline and evaluate their performance. As shown in the third and fifth rows, both configurations achieve significant improvements over their directly estimated counterparts. The version using DUSt3R achieves a higher accuracy than the version using VGGT. These results demonstrate that our method is compatible with different pose estimators and consistently enhances their performance.\n\nCompared to VGGT, DUSt3R is better suited for pose estimation on small or non-overlapping image pairs. Furthermore, our method also yields more noticeable improvements when applied to DUSt3R. Although we believe that fine-tuning VGGT on small-overlap data or adjusting the hyperparameters of our pipeline could further improve the performance of VGGT, these efforts are beyond the scope of this work and rebuttal. We plan to explore them in future work.\n\n| **Method**         | **Input**       | **MRE↓**           | **R@5°**             | **R@15°**            | **R@30°**            | **AUC₃₀↑**            |\n|:-------|:---|:---|:--|:--|:---|:--|\n| Dust3R         | Pair            | 18.14    | 40.34      | 71.25       | 82.99       | 61.98         |\n| **Ours**           | Hybrid Video    | **11.40**| **55.21**  | **89.93**   | **93.75**   | **77.41**     |\n| VGGT           | Pair            | 20.17    | 40.00      | 70.17       | 82.29       | 60.54         |\n| VGGT$_{Ours}$      | Hybrid Video    | 17.88    | 42.43      | 84.40       | 85.76       | 65.15         |\n\n**Q5: Related works and citations.**\n\nWe thank the reviewer for pointing out the relevant work [1, 2, 3, 4, 5]. Among them, [1, 2, 4, 5] explore different feature matching strategies and are highly relevant to our Feature Matching Selector module. We will include proper citations and discussion of these works in the revised version to better contextualize our contribution. Furthermore, we have already conducted experimental analyses of the matching methods proposed in [4] (RoMa) and [5] (LoFTR), as detailed in our response to ***Q3***. These empirical comparisons will also be incorporated into the main paper. VGGT [3] is a recently proposed method designed for camera pose estimation and is also highly relevant to our task. We have included comparative experiments using VGGT in our response to ***Q4***. , and will add the corresponding results and citations in the revised version.\n\n[1] Zhang, Zichao, Torsten Sattler, and Davide Scaramuzza. \"Reference pose generation for long-term visual localization via learned features and view synthesis.\" International Journal of Computer Vision 129.4 (2021): 821-844. \n\n[2] Trivigno, Gabriele, et al. \"The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024. \n\n[3] Wang, Jianyuan, et al. \"Vggt: Visual geometry grounded transformer.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025. \n\n[4] Edstedt, Johan, et al. \"RoMa: Robust dense feature matching.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024. \n\n[5] Sun, Jiaming, et al. \"LoFTR: Detector-free local feature matching with transformers.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021."}, {"review_id": "9sKNNMPqQe", "reviewer": "Reviewer_hdsC", "summary": "This paper proposes PoseCrafter, a framework designed for extreme pose estimation where input image pairs exhibit small or no overlap—a scenario in which existing feature-based and learning-based methods often fail due to the lack of reliable correspondences.\n\nPoseCrafter follows the general strategy of InterPose by leveraging pretrained video generation models to synthesize intermediate frames for downstream pose estimation. Beyond standard video interpolation, it further incorporates a pose-conditioned novel view synthesis model to generate more geometrically plausible intermediate frames. To ensure consistency with the input views, the framework introduces a Feature Matching Selector (FMS) that identifies the most informative synthesized frames based on RANSAC inlier counts.\n\nExtensive experiments on four benchmarks—Cambridge Landmarks, ScanNet, DL3DV-10K, and NAVI—demonstrate that PoseCrafter consistently outperforms previous state-of-the-art methods in extreme pose estimation, without requiring additional training or supervision.", "questions": "A runtime analysis or comparison would help assess its practical applicability.", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n- The use of a pose-conditioned video generation model, combined with feature matching for frame selection, is both intuitive and effective.\n- The training-free nature of the approach makes it highly practical and accessible for a wide range of users.\n- The method is evaluated on four diverse datasets with yaw ranges selected to represent small and no-overlap scenarios. PoseCrafter consistently shows significant improvements over strong baselines such as DUSt3R and InterPose. The ablation studies are thorough and well-structured.\n- The paper is clearly written and easy to follow.\n\n**Weaknesses**\n- Limited novelty in algorithmic components: While the overall system design is effective, the core components—video interpolation, pose-conditioned view synthesis, and RANSAC-based frame selection—are based on existing techniques.\n- Computational cost not addressed: The pipeline involves several sequential stages, including video interpolation, novel view synthesis, feature extraction, and RANSAC-based filtering, which may lead to high computational overhead. A runtime analysis or efficiency comparison would help clarify the method’s practicality for real-world use.", "was_revised": true, "final_justification": "This paper is technically solid and well validated. In terms of algorithmic novelty, I believe it is above the acceptance bar. Overall, I find it to be a valuable contribution that the community would be interested in reading.", "rebuttal": "We thank the reviewer for acknowledging the effectiveness and practicality of our approach. Following the suggestion of the reviewer, we provide detailed responses to address the reviewer's concerns.\n\n**Q1: Clarification about novelty.**\n\nThe novelty of our PoseCrafter lies elsewhere despite building on existing modules such as video interpolation, pose-conditioned novel view, and RANSAC filtering. **_Our main contribution_** is in the adaptation and integration of these components to holistically address the under-explored and challenging problem of pose estimation with small or no visual overlap.\n\n- We are the first to propose a **hybrid video generation (HVG)** module that combines video interpolation and pose-conditioned novel view synthesis to generate high-quality intermediate frames for subsequent pose estimation.\n- We introduce a **Feature Matching Selector (FMS)** that deterministically selects the most informative frames suitable for pose estimation. This eliminates the need for expensive statistical self-consistency scoring in existing works.\n- Our method requires **no additional training or fine-tuning**, yet achieves significant performance improvements over existing methods. This not only greatly reduces training costs but also makes our approach a flexible framework, where components can be potentially replaced with more effective alternatives in the future.\n  \nWe believe that the overall system design and its demonstrated impact across four benchmarks reflect a meaningful and practical innovation, which should be beneficial for the community and related researchers.\n\n**Q2: Memory Cost and Runtime Discussion.**\n\nWe thank the reviewer for their important points regarding runtime and computational efficiency. To address this concern, we provide a detailed comparison of the runtime and memory usage of different stages of our pipelines and those of InterPose. For fair evaluation, we divide the pipeline into two stages: video generation and pose estimation. The video synthesis stage in both our method and InterPose is referred to as video generation. The subsequent process to obtain the final pose using self-consistency score in InterPose or feature matching selector in our method is referred to as pose estimation.\n\nSince the original implementation of InterPose is not publicly available, we reimplemented its pipeline according to the details described in the paper. We refer to this reproduction as InterPose$^{\\ddagger}$ for subsequent comparison. The results are summarized in the table below.\n\nWe can see that our approach significantly reduces the pose estimation time while achieving comparable or even higher pose accuracy. This improvement comes from the fact that InterPose generates 8 video sequences for each image pair and samples a subset of 11 frames from each video to compute the self-consistency score. In contrast, our approach employs a leaner and more efficient pipeline. We generate only a single hybrid video and apply a deterministic feature-based frame selection strategy.\n\nAlthough our approach has a slightly higher memory footprint during the video generation phase,  the pose estimation phase remains lightweight and practical in terms of memory and speed.\n\nWe will include this analysis in the revised manuscript to better demonstrate the computational efficiency and scalability of our approach.\n\n| **Method**  |   Video Generation Runtime         |   Pose Estimation  Runtime   |   Video Generation Memory Cost    |  Pose Estimation Memory Cost  |\n|:-----|:-------|:-----|:-----|:-----|\n|InterPose$^{\\ddagger}$ | 3.2min                        | 20.29min                      |14.6GB                        | 3.1GB                        |\n| Ours                | 3.8min                        | 0.18min                       |22.8GB                        | 3.6GB                        |"}]

Author rebuttal:

Meta-review: This paper introduces Hybrid Video Generation (HVG), which combines video interpolation with pose-conditioned novel view synthesis to generate clearer intermediate frames for challenging image pairs with minimal overlap. It also proposes a Feature Matching Selector (FMS) that selects frames optimized for pose estimation based on feature correspondences. Together, these innovations—implemented in the PoseCrafter system—improve pairwise camera pose estimation performance across multiple benchmarks, especially in low-overlap scenarios.

The concerns raised in the reviews and that were discussed can be summarized as follows:
1. Unjustified design choices of the proposed HVG (R-7zgU);
2. Ad-hoc and novelty solution (R-7zgU, R-8hif, R-6p5A, R-hdsC);
3. Insufficient evaluations or experiments (R-8hif, R-6p5A); and
4. Computational cost (R-hdsC)

After the reviewer and author discussion, the reviewers felt satisfied after reading the clarifications and seeing additional experiments. As suggested by many reviewers in their final recommendations, I would encourage the authors to include clarifications discussed with the reviewers as well as adding experiments or evidence that can benefit the experiments in the final paper.
Decision: Accept (poster)