OmniZoom: A Universal Plug-and-Play Paradigm for Cross-Device Smooth Zoom Interpolation
Authors: Xiaoan Zhu
Yue Zhao
Tianyang Hu
Jiaming Guo
Yulan Zeng
Renjing Pei
Fenglong Song
Huajun Feng
Venue: NeurIPS 2025 poster ()
Keywords: Smooth Zoom
Frame Interpolation
3D Gaussian Splatting
Velocity Disambiguity
Cross-Device Dataset
Primary area: applications
TL;DR: We propose OmniZoom, a universal plug-and-play framework for cross-device smooth zoom interpolation.

Abstract: Dual-camera smartphones suffer from geometric and photometric inconsistencies during zoom transitions, primarily due to disparities in intrinsic/extrinsic parameters and divergent image processing pipelines between the two cameras. Existing interpolation methods struggle to effectively address this issue, constrained by the lack of ground-truth datasets and motion ambiguity in dynamic scenarios.
To overcome these challenges, we propose OmniZoom, a universal plug-and-play paradigm for cross-device smooth zoom interpolation. 
Specifically, we present a novel cross-device virtual data generation method utilizing 3D Gaussian Splatting. This method tackles data scarcity by decoupling geometric features via spatial transition modeling and correcting photometric variations with dynamic color adaptation. It is further enhanced by cross-domain consistency learning for device-agnostic semantic alignment. Additionally, we introduce a plug-and-play 3D-TPR (3D Trajectory Progress Ratio Mapping) framework that surmounts 2D spatial limitations. As components of our framework, a texture-focus strategy is introduced for high-frequency detail preservation, incorporating mask penalty constraints to suppress interpolation artifacts. Our pipeline exhibits broad compatibility with diverse interpolation methods and achieves good performance across multiple public benchmarks. Real-world evaluations on various smartphone platforms also reveal significant quality improvements after fine-tuning on our synthetic data, which underscores the robustness and practical effectiveness of our approach for cross-device zoom applications.

OmniZoom: A Universal Plug-and-Play Paradigm for Cross-Device Smooth Zoom Interpolation
Xiaoan Zhu" Zhejiang University xiaoanzhu@zju edu. cn
Yue Zha Huawei Noah 's Ark Lab
Tianyang Hu Zhejiang University hutyōzju. edu.
zhaoyue53@huawei. com
Jiaming Guo Huawei Noah's 's Ark Lab muojiamnnn5@hawei..... com
Yulan Zeng
Renjing Pei Huawei Noah's waa's Ark Lab
Sungkyunkwan University zengyulanšskku. edu
GeirenjingShuaweii com
Fenglong Song
Huajun Feng Zhejiang University fenghj @zju. edu. cn
Huawei Noah's 's Ark Lab
songlenggong@@uuwwii.ccm com
Abstract
Dual-camera smartphones suffer from geometric and photometric inconsistencies during zoom transitions, primarily due to disparities in intrinsic/extrinsie parame- ters and divergent image processing pipelines between the two cameras. Existing interpolation methods struggle to effectively address this issue, constrained by the lack of ground-truth datasets and motion ambiguity dynamic scenarios. To overcome these challenges, propose OmniZoom, universal plug-and-play paradigm for cross dross-device smooth zoom interpolation. Specifically, we present novel cross-device virtual data generation method utilizing 3D Gaussian Splat- ting. This method tackles data scarcity by decoupling geometric features via spatial transition modeling and correcting photometric variations with dynamic color adaptation. It is further enhanced by cross-domain consistency learning for aevict-agnostic semantic aligmment. Additionally, introduce a plug-and-play 3D Trajectory Progress Ratio (3D-TPR) framework that surmounts 2D spatial limi- tations. As components of our framework, a texture-focus strategy is introduced for high-frequency detail preservation, incorporating mask penalty constraints suppress interpolation artifacts. Our pipeline exhibits broad compatibility with diverse interpolation methods and achieves good performance across multiple public benchmarks. Real-world evaluations on various smartphone platforms also synthetic data,
reveal significant quality improvements after finetuning which underscores the robustness and practical effectiveness of our approach for cross-device zoom applications.
Introduction
In recent years, dual-camera systems 9 13 have become increasingly prevalent in modern smartphones, offering advanced imaging capabilities. Among these functionalities, zooming 58 plays crucial role by bridging the wide-angle and main cameras, allowing users to perceive changes in subject distance and scale without physical movements. However, performing optical zoom [10 28
"Equal contribution.
t "orresponniig author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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introduces significant geometric misalignment and photometric inconsistencies between images. Geometric misalignment arises from disparities in the intrinsic and extrinsic parameters of the dual-camera system 46 57 In contrast, photometric inconsistencies are caused by algorithmic divergences (e-g., white balance, color correction, and gamma correction) in Image Signal Processing pipelines (ISP) [36, 26) These differences lead to parallax, color shifts, and exposure mismatches between the paired images, severely affecting the smoothness and realism of zoom transitions.
Notably, the transition between fixed-focus dual-camera views can be naturally formulated as an intermediate frame synthesis task. Frame interpolation (FI) I7 25 27 53
3 is a promising technique in this context, as it aims to synthesize temporally consistent intermediate frames between given inputs. In this work, we define zoom interpolation (ZI) as a specialized sub-task of FI. where the objective is to synthesize an intermediate frame that is both geometrically and photometrically consistent, given two zoomed images captured from a dual-camera system. While ZI shares the core goal of Fl in generating intermediate content, it also presents several unique challenges that hinder the direct application of conventional Fl methods.
First, unlike FI tasks with dense temporal supervision, ZI lacks suitable ground-truth intermediate frames. This is because deploying a continuous-zoom{2 588 capture setup across dual-camera hardware is impractical in real-world mobile scenarios, making it difficult to train or finetune models in supervised manner. Second, ZI suffers from severe motion ambiguity. Traditional FI approaches rely on fixed timestep maps [29] 3 to model pixel displacement, assuming smooth linear motion. However, zoom transitions often introduce complex, nonlinear object motion and parallax effects. These distort assumptions in flow estimation, leading to motion blur and structural artifacts.
To address the unique challenges of ZI, we propose OmniZoom, a universal plug-and-play paradigm for cross-device smooth zoom interpolation. This paradigm comprises two key components specif- ically designed for this task: (1) Generation of cross-device smooth zoom virtual dataset. To overcome the lack of ground-truth intermediate frames in ZI, we build upon ZoomGS (50) and adopt 3D Gaussian Splatting (3DGS) X to synthesize high-quality data. While ZoomGS is limited to single-device scenarios, our method further resolves cross-device geometric and photometric incon- sistencies. We enhance this process with spatial transition modeling and dynamic color adaptation to decouple geometric and photometric differences between devices. Furthermore, a cross-domain consistency learning scheme ensures the generated virtual dataset maintains semantic alignment and photometric fidelity across different camera platforms. (2) 3D-TPR framework. To tackle spatial deficiencies and velocity ambiguity during interpolation, we propose a plug-and-pla -lay 3D-TPR module that encodes 3D motion-aware correspondence across frames. In addition, we incorporate texture-focus strategy to preserve high-frequency details and a mask uncertainty penalty to suppress ambiguous predictions. These components work jointly to improve perceptual sharpness and mitigate artifacts during zoom synthesis.
Our pipeline is specifically designed for ZI and exhibits strong plug-and and-play adaptability across variety of FI backbones and mobile devices. By integrating the 3D-TPR framework into various existing architectures, we observe remarkable fidelity improvements and superior subjective quality on multiple public benchmarks. To further validate its real-world applicability, we construct test datasets captured from various smartphones. Finetuning FI models on our ZI dataset leads to substantial quality gains across all models on the real-world benchmark. Results demonstrate the generalizability and robustness of our approach in addressing zoom-related challenges across diverse Fl models and mobile devices, providing a unified solution for achieving smooth and perceptually coherent zoom transitions in dual-camera systems.
In conclusion, our key contributions are as follows:
We propose novel pipeline for constructing a cross-device 71 dataset, which models geometric and photometric inconsistencies across devices. The resulting dataset enables high-quality, device-agnostic supervision, filling the gap in ground-truth data for ZI task.
We introduce a novel ZI framework, 3D-TPR, which enhances spatial modeling via disparity- aware encoding, complemented by texture-foeut strategy and mask penalty for better perceptual quality and robustness.
We propose OmniZoom, a universal plug-and-play paradigm for cross-device smooth zoom interpolation that seamlessly integrates with Fl networks. consistently delivers high-quality ZI results across diverse Fl models and smartphone devices in real-world scenarios.
2

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2 Related works
2.1 Frame interpolation methods
Frame Interpolation plays a critical role in high-resolution video synthesis, slow-motion rendering. editing, and AR/VR applications [30] 33 [6]. Recent advances have explored temporal mapping strategies to improve interpolation quality. For instance, Interpany [60] replaces fixed timestep maps with 2D flow-based distance-aware index. However, its reliance 2D optical flow limits robustness under complex 3D motion and large depth variation. We propose 3D geometric-aware encoding mechanism that captures spatial coherence more effectively. Unlike methods such as [I7] that suppress artifacts utilizing deep hierarchical features, our design handles mask uncertainty without additional architectural complexity. Despite advancements of recent learning- -baeed FI 25 restoring fine textures under large motion remains challenging.
models 3 53 27
We tackle this via a cexture-focus training strategy that reweights reconstruction loss based on gradient cues, offering a lightweight alternative to traditional perceptual losses (20] or multi-scale losses 44
2.2 3D reconstruction
3D reconstruction has evolved from classical geometry- methods such as Structure-fromt MrotMMooonn (SIM) (38) and Multi-View Stereo (MVS) [14], which enable scene recovery from unstructured images deep learning approaches that enhance robustness and generalization [12 43]. Neural Radiance Fields (NeRF) 31 and its variants (e.g.. Mip-NeRF 360 [6] achieve high-fidelity view synthesis but high computational cost. Recently, 3D Gaussian Splatting (3DGS) (33 has emerged as an efficient alternative with explicit point- based representation and real-time rendering capability. ZoomGS [50] leverages 3DGS to create virtual cameras between dual-camera pairs for interpolation supervision, but its device-specific calibration limits generalization. In this work, we extend 3DGS and ZoomGS into universal, cross-device virtual data generation pipeline, jointly addressing geometric and photometric inconsistencies to support consistent and smooth zoom synthesis across diverse mobile platforms.
Method
In this section, we first present a pipeline based on 3DGS 26 for constructing a cross-device smooth zoom virtual dataset. This dataset enables the generation of high-quality virtual images that are not limited to a specific camera module. Building upon this foundation, we propose the 3D-TPR framework, which enhances temporal consistency, preserves fine details, and improves spatial reliability during interpolation. The overall pipeline is illustrated in Figure I[a).
3.1 Generation of cross-device smooth zoom virtual dataset
ZoomGS [50] utilizes 3DGS 23 to synthesize virtual 3D camera models MM between the main camera Mn, and wide-angle camera Mws facilitating the generation of pseudo-supervised training images I via:
- R(M,K,P),
(I)
where denotes the 3DGS rendering process (23], and K., P represent the intrinsic and extrinsic parameters of the corresponding cameras. However, the dataset generated by ZoomGS is device- dependent and lacks generalizability across different smartphone platforms. To overcome this limitation, we extend the ZoomGS pipeline to construct cross- ZI datasets applicable to arbitrary smartphone configurations. As shown in Figure [[),, we introduce spatial transition modeling module and a color adaptation strategy that decouple the geometric and photometric characteristics of individual camera modules. Furthermore, we propose cross-domain consistency learning approach to enforce semantic alignment and visual coherence across devices and environments.
Spatial transition modeling. For each scene, capture N zoom image pairs comprising wide-angle images & { and main images { _ These images are then processed using COLMAP [38) to calibrate their intrinsic parameters {KW) {KC and extrinsic parameters (Pa) (
{Pa [p_]-=-- For each pair, we generate M camera poses, including the two original cameras and M interpolated virtual views in between. To achieve smooth spatial transitions, we interpolate
both intrinsic and extrinsic parameters between the two real cameras. For the intrinsic parameters Ke,,, we perform linear interpolations [15]:
i-1 M
1-1
Kr, (1
)
Knw
@ Km,
(2)
M 1
3

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(a)
3D Trajectory Progress Ratio Mapping Frameeork
3D-TPR timestop encoding
3D-TPR mask penalty
3D-TPR texture-facus
simlorrsy wap
Cross-device
Dataset
Fl Module
Texture
Generation
cupplasize region
Reweighting
Cross-Domaie Consistency Learning
Spatial Transition Modeling
Mr.
I.
COLMAP T. P. K.
/.
Color Adaptation
{ K.
Camera Embedding
M
1.
Strategy
M. (x,c,E,at)
Me,
( Kr..
Gaussian
M, Ex,
Mr
1.
Models
(c)
flow estimation
disparity estimation
3D ventor space
p-t-sllww
Disp.
simblarity
Inverse
Dep
Warp
AD,
Cval
Figure I: Overview our proposed OmniZoom. (a) Plug-and-play integration pipeline for cross- device ZI. (b) Dual-camera ZI dataset generation via spatial-color calibration and cross-domain optimization. (c) Construction of similarity map the 3D-TPR encoding module.
while the extrinsic parameters Ps, are interpolated in Euclidean space
21 5
Pm
Pe. Pw 2 exp
log
M
P...
where i E 1,2, M-1, This spatial transition module enables continuous and geometrically coherent virtual viewpoints by interpolating between COLMAP-estimale camera poses. It models zoom motion purely from relative transformations, without relying on any device-specific parameters.
Color adaptation strategy. To handle photometric inconsistencies across heterogeneous devices, we introduce dynamic color adaptation mechanism that operates the level of individual 3D Gaussians. Specifically, we represent each camera as 3DGS model M composed of set of 31) Gaussians. Each Gaussian parameterized
center position æ, color c, anisotropic covariance Σ (indicating its spatial extent), and opacity [23] To account for photometric shifts between devices, we assign each camera learnable embedding (e.g.. dw for wide-angle and dim for main camera), and interpolate the virtual camera embedding Φ.,, following Equation 2 A lightweight neural network, ColorNet C, then predicts per-Gaussian color offset based on both geometric and appearance attributes:
Ac = C(o, x, c, a).
This offset is applied to each Gaussian, resulting view-specific virtual model Mo, with updated color values {x, + Ac, E,a). a). This learned photometric correction is continuous and device- agnostic, enabling robust color alignment across diverse camera modules. Further architectural details and ablation studies of ColorNet are provided in the Appendix A.I A.]

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Cross-domain consistency learning approach. To ensure semantic and visual consistency across devices, we introduce cross-domale consistency learning strategy. During joint optimization of the color adaptation network C, the rendered images I and In are supervised by their corresponding ground truth image I and I via a reconstruction loss:
Lraat (1 X)L1 (Ireat, Ireat) + ALSSTM (Freat, reat),
(5)
where real E {m, w), and L1 LSSIM denote the l loss and SSIM loss 44), respectively. We follow the default hyperparameters from 3DGS and set À = 0.2. However, relying solely low-level pixel reconstruction is insufficient to ensure perceptual consistency across domains. To enhance semantic structure and texture realism, we introduce two additional feature-space constraints. We propose a semantic consistency loss Lade based on a pre-trained lightweight feature extractor F 37
Lede (I.,) F(rreat)ll,
(6)
Additionally, preserve texture consistency acToss domains, we incorporate a texture consistency loss Lienture, computed using a VGG-based texture encoder Trexture [3)
Liezture |/Tresture (Fe.) Ttexture (Frea! Il
(7)
The final objective for domain-consisten virtual image generation given by:
Liotal = Creat + Lede + Ltexture
(8)
3.2 3D-TPR framework
Traditional Fl models represent the interpolation time using a fixed timestep map that is concatenated with the input frames [1,, However, this static encoding lacks adaptability to scene content and motion dynamics. As illustrated in Figure [a), our 3D-TPR framework replaces the fixed timestep map with a pixel-wise similarity map computed using 3D-TPR encoding, which captures 3D geometric relationships between the input views. In addition, we introduce a texture-focus strategy and a mask-based uncertainty penalty to provide auxiliary supervision.
3D-TPR encoding. As shown in Figure c), (To, J1 1.} serves as the input to the core 3D-TPR en- coding module. Following InterpAny (60), we use RAFT 42 to estimate 2D pixel-wise optical flows [ymy--lanee V-2g-plane). }. We compute the corresponding backward flows {V:zy-plane m-ay-plane (Vo-st Vo->1 using an inverse warp module W
{Ve-00 1-30
(-):
plane W- "V
pla-neane W (V
Vi
(9)
Concurrently, we employ an efficient network Lite-Mono 55 to predict monocular disparity maps
(Dispo, Dispt, Disp1} from the input frames (lo, J,, I1). The predicted disparities { (Dispe, Dispi]
are then warped to the reference view In using the backward flows via simple warp operation W(-). yielding the warped disparities (Dispt mery, DDisp" p.rp]:
Disp, werr WDDsspe, Vi-
Displarry (Dispp V.yyplanc). V
(10)
We then compute the disparity residuals (o the warped disparity maps { (Dispt warp Disp1 warp) and the reference disparity map Dispo:
ADo-1 by measuring the differences between
D Dispp warp - Dispolle, ADo+1 = [Diisp warp - Dispalle:
(11)
where 11 denotes a robust error norm. These components jointly yield the disparity variation vector Var Dr D which reflects the disparity differential ratio of the target frame I, We hypothesize that this progress-ratio-lik scalar can serve as a learned timestep prior, replacing fixed interpolation index and enabling better temporal alignment under non-uniform motion. Consequently, we fuse the disparity residual A Do and the planar motion vector Vory into a unified 3D motion field A AV via cross-domain aggregation module A(-). This multimodal fusion is symmetrically extended to the temporal span 0 + 1. Each spatial location (i,j,k) E IR3 in 3D motion fields is defined
Vayy-laane), A A A(ADo-i Vo
(A ( A(ADom
Vo-1 Vegenlane).
(12)
The trajectory progress similarity at (i. is then computed via cosine similarity:
AV (t.j,. (i.j,k) Tyz
A AV (i.j.k)
Sim(i, = LV 0-41 (i,i,k) | VIR (i.j,k)
(13)
5

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This formulation effectively decouples perspective projection effects from true 3D motion, addressing a key limitation of conventional 2D optical flow methods while preserving temporal coherence across interpolated frames.
Gradient-coupled texture-focus training strategy. To mitigate the common tendency of Fl networks to oversmooth high-frequent content [52], we introduce texture-focus reweighting scheme. We identify texture regions in the ground-trutt truth frame 4 using multi-scale, kemee-bbased detector T:
M T(4) E {0, 1] ",
(14)
where H and W denote the height and width of 4. To enhance texture mask robustness via conventional detection [59], we adopt the Average Local Pixel Difference (ALPD) metric. Specifically, local and global texture metrics are computed as:
N₂ N
VALPD Σ
9 gl.
Uglobal
99
Iglobal
(15)
where gi the intensity of the i-th local pixel (i = I,..,, P), 9 is their mean, and Np is the pixel count in It. Scalar serves as sensitivity coefficient. A pixel is classified as textured if UALPD >
Vglohal- We then define a reweighted loss using this texture mask:
Lextuue-- focus
1 H W
Σ w(xx v) p(f((z.)) I((,,)),, J.
w(r,y) = AM(x, v). (16)
where À controls the emphasis textured areas and p(-) denotes the Charbonnier loss. fe y) and (x, y) indicate the predicted and ground-truth frames, respectively. This formulation encourages more accurate reconstruction of real high- figh-freueency details.
Mask uncertainty penalty limitations. Modern systems typically synthesize intermediate frames by fusing two consecutive inputs In and /1 using dual-branch formulation:
F F M ( + F o (1- (()
(17)
where F(() and (T(5) is the i-th scale features of the two input frames, @ is element-wist multiplication, and M [0, 1, is a learnable mask indicating the per-pixel dominance of cach source. We posit that under ideal conditions where 6 should converge to binary values (0 1) for confident fusion, ( around 0.5 often leads to ghosting or blur in synthetic frames. To address this, we introduce an uncertainty-awer regularization term applied to each mask level during training:
mask A HXW Σ 1 (1- 0 0< < <s.
(18)
where traverses all pixel positions, Y (0. controls the penalty growth rate, and Ae(i is i-th scale-specific weight. Lower-resolution scales are allowed to retain higher
1, 1, ... SS) .)) uncertainty better handle large motion and occlusion, while higher-resolution scales encouraged to produce more confident, near-binary decisions by assigning larger A;- Additional formulation details are provided in Appendix A.4
Experiments
4.1 Implementation
Datasets for virtual camera generation. We capture zoom image pairs from HuaweiPura77Ultra and RedmiK50Ultra (50) across 132 real-world scenes (79 indoor, 53 outdoor), covering diverse environments and illumination conditions. Based on Section 3.1 we generate 205 virtual sequences (16 frames each) for our ZI dataset. For ZI dataset construction details and representative samples, please refer to Appendix A.2 A.3) We compare our ZI dataset with ZoomGS and 3DGS in terms ability for real-world ZI. Visual comparisons and finetune
of supervision quality and generalizan results are provided in Appendix A.5
Similarity map encoding computation. During training, == Sim k). V(i,j, k). Sim(i,j, is derived from ground-truth frames using RAFT and Lite-Mono (see Eq 13 During inference, the

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Huswei
Redmi
Figure Sample triplets from our ZI dataset, each showing the wide-angle Lw the synthetic intermediate frame o, and the main camera image m. for two devices: Huawei and Redmi.
ground-truth frame is not accessible, and the exact similarity map cannot be computed. Following the strategy used in prior work such as InterpAny [60], it is sufficient to provide a = fixed limestep map in the same manner conventional time-indexing methods. The semantics of this indexing map have shifted from an uncertain timestep map to a more deterministic motion hint. Physically, this encourages the model move each object constant speeds along their trajectories. In practice, this constant-specd assumption serves as valid approximation for smooth zoom scenarios commonly found mobile photography.
Training configuration. For virtual camera generation, we adopt the Adam optimizer 24 in a two-stage scheme. We first train the Gaussian model MM. for 5k iterations using the learning rate from 3DGS [23]. Then, joint optimization is performed for 30k iterations on both M, and the ColorNet, with the learning rate decaying from 1e-3 to 1e-6 after 20k steps. Our 3D-TPR framework is integrated into RIFE (17). IFRNet (25), EMAVFI (53). and AMT [27]. Each model is initially trained on the Vimeo-90K (52) septuplet dataset 701 sequences 448x256 resolution) using its default hyperparameters. We then finetune them on our ZI dataset. All experiments are implemented by PyTorch (35 on hardware devices equipped with 40 GB of memory.
Evaluation configuration. We evaluate performance and generalization on two types of datasets. For benchmarks with ground-truth, including Vimeo90K, Inter4K [41] and UCF101 [40], use
full-reference image quality metrics: PSNR 18]. SSIM [49]. and LPIPS [55). For real-world
evaluation, we collect zoom image pairs from four smartphone platforms: HuaweiPura70Ullrr
IPhone16ProMaxx OPPOFindX8Ultra and RedmiK
Since ground truth is unavailable,
we adopt no-reference metrics including NIQE 322 PI M. CLIP-IQA (45). MUSIQ [22 and
FLOLPIPS [1I. For all comparisons, the 2D baseline refers to the distance indexing paradigm [60]
For real-world deployment without geometric priors, the model uses uniform map motion
guidance when inference. Real-world dataset details are provided in the Appendix A.7
4.2 Ablation studies
To assess the effectiveness and compatibility of each component in our 3D-TPR framework, we perform controlled ablation study on EMA-VFI in Table The 2D framework [60] serves as the baseline. We evaluate four configurations: (1) 3D-t-m, which applies only the 3D- TPR encoding; (2) 3D-m, which integrates the -octure-focusae strategy; (3) 3D-t, which incorporates the mask penalty constraint; and (4) 3D, the full model with all components enabled.
Although improvements on pixel-centric metrics (PSNR and SSIM) appear modest, we argue that in most practical ZI scenarios, the objective is not to produce pixel-aligned frames, but to synthesize perceptually plausible intermediate frames. Pixel-centric metrics are relatively insensitive to common ZI artifacts such as blur and ghosting (56), which our design explicitly targets by resolving velocity ambiguity. As such, pixel-centric metrics are less informative for perceptual assessment. While the improvements on pixel-centric metrics (PSNR and SSIM) appear numerically modest, we emphasize that in practical Zoom Interpolation (ZI) scenarios, the objective is not to reproduce pixel-aligned frames but to synthesize perceptually plausible intermediate frames. Moreover, pixel-centric measures are inherently insensitive to blur and ghosting artifacts -heee dominant
7

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Table I: Ablation study of the 3D-TPR framework. Bold values indicate the best results. Each component contributes incremental gains, and the full model yields the best performance.
Method
PSNR f
SSIM 1
LPIPS
PI
MUSIQU 1
FLOLPIPS +
2D
24.73
0.851
0.081
5.071
55.968
0.128
3D-t-m
24.78
0.852
0.081
5.041
56.615
0.119
3D-m
24.74
0.852
0.084
5.022
57.362
0.113
3D-t
24.80
0.853
0.081
4.934
56.946
0.116
3D (full)
24.86
0.853
0.080
4.802
58.725
0.091
Table 2: Comparing the impact of optical flow on Vimeo90K.
IFRNet
AMT
Benchmarks
Metries
1x
2x
4x
8x
PSNRT
27.25
27.22
26.01
24.94
Vimeu90k
SSIM
LPIPS
degradations caused by velocity ambiguity, which our framework is explicitly designed mitigate. Consequently, these metrics are less indicative of perceptual fidelity.
In contrast, perceptual metrics such as PI, MUSIQ, and FLOLPIPS reveal consistent and significant improvements across enhanced variants. Each component contributes distinct and complementary benefits: 3D-TPR encoding improves motion consistency (lower FLOLPIPS), texture-focus strategy enhances high- frequecyy details (higher MUSIQ), and mask uncertainty penalty suppresses occlusion artifacts (lower PI). When combined, the full model yield the best overall performance, confirming that our framework effectively enhances perceptual quality and motion coherence ZI. Furthermore, when high-quality disparity supervision is available (Appendix A0), the framework achieves notably larger gains, indicating its strong potential to exploit more accurate geometric priors.
To analyze the effect of optical flow accuracy on 3D-TPR, we downsample the RAFT inputs to 1x, 2x, 4x, and resolutions. As RAFT is resolution-sensitive this setup offers an effective proxy for flow fidelity variation. All estimated flows are bilinearly upsampled to original resolution for training on AMT and IFRNet. As shown Table lower-resolution flows (4x/8x) lead noticeable degradation, while 2x setting achieves comparable performance to the Ix resolution case while significantly reducing computation. We therefore adopt 2x as our default configuration.
4.3 Qualitative results
To demonstrate the visual quality of our synthetic intermediate supervision, Figure shows represen- tative samples from the ZI dataset. I, achieves smooth transitions photometrical and geometric structure, while faithfully preserving fine textures and high-free gigh-frequeccy details. Despite being generated without ground-truth supervision, the results exhibit strong visual consistency and realism, providing effective guidance for training ZI models.
In Figure 3 we present visual comparisons between 2D and 3D-TPR methods on Vimeo90K. Across multiple Fl networks, the 3D-TPR method consistently yields significantly better perceptual quality, producing clearer and sharper images. In contrast, 2D approaches often suffer from noticeable blur, while our 3D-TPR framework maintains fine-grained textures and structural consistency throughout. These results highlight the strong perceptual quality and robustness of the 3D-TPR framework.
Meanwhile, Figure offers a qualitative comparison of ZI results across FI networks and devices. -TPR consistently produces higher-quality outputs with fewer artifacts, less blur, and sharper structures. Notably, RIFE, IFRNet, and AMT exhibit significantly reduced blur, while EMA-VFI produces clearer and more coherent line structures. Visual results demonstrate the effectiveness of our data generation method in cross-device generalization for the ZI task. Building upon this, OmniZoom (3D) further exhibits robust performance across diverse Fl networks and mobile platforms, offering universal solution for real- world ZI. Additional visual results are provided in Appendix A.8 and comparisons of and 2D fine-tuning strategies are presented in Appendix A.9

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GroundTruus
RIFE
IFRNet
EMA VFI
AMT
2D
3D
2D
3D
Figure 3: Visual comparison of 2D and 3D-TPR results across networks. Each row shows interpolation results at the same timestep.
INPUTS
RIFE
IFRNet
EMA-VFI
AMT
Huawei
OPPO
iPhone
Redmi
3D-TPR 3D,TPR
3D-TPR
3D,TTP
3D-TPR
3D-TPR
3D-TPR
3D,TPR
Figure 4: Qualitative results on real-world data across four FI networks at timestep = 1/2. The subscript y denotes models finetuned our ZI dataset.
4.4 Quantitative results
As shown in Table 3 we conduct quantitative comparisons between the 2D and 3D-TPR frameworks on Vimeo90K, Inter4K, and UCF101. On Vimeo90K. 3D-TPR achieves clear gains in PSNR, SSIM, and LPIPS, indicating better reconstruction and perceptual quality. On the unseen Inter4K and UCF101 benchmarks, 3D-TPR still improves perceptual metrics (SSIM and LPIPS), demonstrating strong generalization. Although pixel-centric metric PSNR may not always improve, ZI focuses on perceptually plausible synthesis rather than pixel-aligned outputs. Also, PSNR is less sensitive to blur (55), which is common artifact in ZI. Combined with the visual results in Figure 3 it confirms the effectiveness and robustness of 3D-TPR for Joffrrsoffers quantitative results on real-world ZI tasks across four smartphone test sets under four FI networks. Due to space constraints, we use 3D in this table to represent our proposed 3D-TPR framework. 3D-TPR consistently achieves second-bert performance across most devices and metrics, clearly outperforming the 2D baseline and demonstrating its effectiveness in handling ZI tasks even without domain-specing tuning. After finetuning on our ZI dataset, OmniZoom (3Dy-TPR) achieves the best results across nearly all models and devices, highlighting both the strong cross-device generalization capability of the dataset and the plug-and and-play adaptability of the framework.
ZI.

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Table 3: Comparisons on Vimeo90k, Inter4K and UCF101 using 2D and 3D-TPR FI frameworks. Bold indicates the best metric.
Benchmarks
Merries
RIFE
IFRNet
EMA VEI
AMT
3D-IPR
3D-TPR
3D-IPR
3D-TPR
PSNR1
27.40
27.51
27.13
27.21
24.73
24.86
27.17
27.22
Vimeo9Ok
SSIM
0.901
0.902
0.899
0.901
0.851
0,853
0.902
0.902
LPIPS
0.086
0.081
0,078
0.074
0.081
0.080
0.081
0.084
PSNR
33.92
34,06
33.73
33.72
inter-IK
SSIMT
0.951
0.952
0.952
0.952
0.903
0.904
0.953
0.955
LPIPS
0.048
0.046
0.046
0.045
0.044
0.044
0.048
0.047
PSNR
35.54
35.42
UCFI0I
SSIM
LPIPS
0.017
Table 4: Comparisons real-world data across four Fl models. Subscript f denotes models finetuned on our ZI dataset. Bold indicates the best performance, and underline is the second best.
Device
Metries
RIFE
IFRNet
EMA-VFI
AMT
2D
3D
3D,
2D
3D
3D,
2D
3D
3D3
2D
3D
3D
NIQEL
3,8464
3,8651
3,7422
3.6801
3,4852
3,5035
3.6363
3.8470
3.834
3,5665
3.6459
3.4547
Hunwei
PI
4,2505
4.1537
3,9360
3,9570
3,3150
3,2520
3,7240
3.8506
3.8467
3,5147
3.6132
3.3121
CLIP-IQAT
0.3691
0.4939
0.5233
0.5422
0,5784
0.5909
0.5612
0.5621
0.5684
0.5428
0,5498
0.6018
MUSIQT
44.8268
58.8362
60.8585
57.4632
73:0233
74.1220
61.7263
02.7403
63.5048
71.2016
73.7092
NIQEL
4,2031
4.1027
3.8601
3.8786
3.6923
3.6953
3.8155
4.0555
4.0934
3,5932
3,7053
3,5087
PIJ
4,5340
4,3503
4,0657
4.2090
3.3942
3.3165
3.9994
4,1304
4.1123
3.4293
3.5367
3.2661
CLIP-IQAT
0.4821
0.5366
0.5492
0,5240
0,5829
0.5949
0.5352
0.5387
0.5503
0,5931
0.5930
0.6211
MUSIQU
53.0577
59.6800
60.7988
57,4088
73.3569
73.8292
1422
58.6204
60.1002
71,9805
72.1243
73.6150
NIQEL
4.6364
4.6100
4.3968
4.5820
5,2039
5.1133
4,5568
4.5166
4.5071
4.9042
5,3017
4.6699
OPPO
PIJ
5,0848
5,0104
4,6815
4,9749
4,9603
4.7144
4,9569
4.9022
4,8752
4,9676
5,3808
4,5702
CLIP 1QA1
0.4989
0.5363
0.5830
0.5825
0.5212
0,5461
0.5622
0.5590
0.5591
0.4798
0.4518
0.5595
MUSIQI
63,2645
65.7912
67.1494
67.2635
75,2718
75.4545
67.4327
67.5921
67.6444
73.0842
1563
75.2187
NIQEL
5.0138
4.8764
5.0098
4.2837
4.0695
4,4088
4.3424
3.8852
5.1925
4.7426
Redmi
PTJ
5,3615
5.0247
4.5195
5.1108
3,5993
3,3880
4.6708
4.5974
4.5519
5,4335
4.0235
3.3548
CLIP-IQAT
0.4077
0.4664
0,4930
0,4219
0.4915
0,5152
0.4599
0.4807
0.4947
0,4336
0.4754
0,5383
MUSIQI
56.3870
61.9235
63.6990
57.5453
73.7573
74.3871
57.0371
59.6610
60.7579
57.6671
71.9982
74.3325
Conclusions
This paper tackles the ZI task that aims to synthesize geometrically and photometrically consistent intermediate frames between dual-camera images. We propose OmniZoom, a universal solution com- prising two key components. First, we introduce a cross-device ZI dataset construction pipeline that enables device-agnostic supervision through spatial transition modeling, dynamic color adaptation, and cross-domain consistency learning. Second, we develop a plug-and-play 3D-TPR framework that enhances ZI accuracy and perceptual quality by leveraging 3D trajectory encoding and geometry- aware feature similarity. Experiments on public benchmarks and real-world test sets show that OmniZoom consistently improves performance across diverse Fl models and devices. We believe this exploration bridges the gap between pre-trained interpolation models and real-world dual-camera systems, paving the way for more practical and adaptable ZI solutions.
Limitations
While OmniZoom designed to be plug-and-play across various Fl networks, achieving optimal performance may still require adjustments to the training schedule, such as tuning the learning rate warm-up strategy, rebalancing loss weights, or modifying batch size. These lightweight adjustments reflect the need for architecture-wwant training when applying our framework to diverse settings.
10

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A Appendix
The content of the supplementary material involves:
Details of ColorNet in Appendix A.I
Dual-camera data sources for the ZI dataset in Appendix A.2
Details of the synthetic 7I dataset and computational cost in Appendix A.3
Formula reasoning of 3D-TPR framework in Appendix A.4
Comparative evaluation of virtual view synthesis method for ZI task in Appendix A.5
Additional experiment on disparity map sources in Appendix A.6
Details of the real-world multi-device ZI sets in Appendix A.7
Additional visual results of OmniZoom on the real-world ZI test sets in Appendix A.8
Benchmarking ZI dataset across ID, 2D, and 3D-TPR frameworks in Appendix A.9
Upper bound analysis of 3D-TPR framework in Appendix AA
Additional visual results 3D-TPR framework Appendix A.11
Broader impacts in Appendix A.12
We provide project page at https: // moom. github. lo/0mniiooom/ where visual results
are available, and the code/dataset will be released soon.
A.1 Details of ColorNet
A.1.1 Architecture of ColorNet
Leaky ReLU
Leaky ReLU
Leaky ReLU
Legend:
FC
FC
FC
camera encoding
2: position
color
Σ: spatial
opacity
Figure 5: Architecture ColorNet.
As illustrated in Figure ColorNet a lightweight multilayer perceptron (MLP) designed to estimate color residuals Ac for neural 3D rendering. The network takes input a five-dimensional feature tuple: the camera encoding o, the center position the base color the anisotropic covariance Σ, and the opacity a. These inputs are first projected into latent feature space through fully connected (FC) layer, which is then processed by three consecutive FC blocks, each followed by a LeakyReLU activation to ensure non-linearity and mitigate the risk of neuron inactivation. Each FC layer contains 5 hidden units. Finally, the output layer maps the intermediate representation to a 3-dimensional color offset vector Ae a linear transformation. This residual is then added the original color c, yielding the refined output color used for rendering.
A.1.2 Ablations of ColorNet
We perform no-reference image quality comparison between our proposed ColorNet and several common photometric calibration baselines: (1) No Calibration, (2) Mean-Sid Normalization 48 and (3) Reinhard Color Transfer [5T]. The evaluations are conducted on rendered intermediate frames using four widely adopted metries: NIQE, PI. CLIP-IQA, and MUSIQ.
15

---

Table 5: No-reference image quality comparison of ColorNet against common photometric calibration baselines on rendered intermediate frames. Lower NIQE and PI indicate better perceptual quality, while higher CLIP-IQA and MUSIQ denote better alignment with human perception. ColorNet consistently achieves the best performance across all metrics, demonstrating its effectiveness in modeling fine-grained photometric variations.
Method
NIQE 4
P
CLIP-IQA f
MUSIQ t
No Calibration
3.5832
3.8378
0,5193
68.4036
Mean-Ste Std Norm
3.3193
3.7757
0.4577
63.0220
Reinhard Transfer
3.2357
3.7345
0,4588
64.3218
ColorNet (Ours)
2.8885
3.1515
0.6099
69.3328
Huawei
Redmi
WTT BIEVR
Figure 6: Representative dual-camera data sources. The top displays samples from HuaveiPura70Ulrra, while the bottom row presents examples from the publicly available Red- miK5OUltra dataset (50). Each scene contains paired wide-angle (lw) and main-camera (Im) images, covering diverse content and geometric configurations.
As reported in Table ColorNet consistently outperforms all baseline methods, demonstrating its superior capability capturing fine- fineggrained photometric variations that simpler approaches fail to model. These results highlight the effectiveness of ColorNet in enhancing color consistency and perceptual quality across intermediate frames.
A.2 Dual-camera data sources for the ZI dataset
As mentioned in the main paper, our dataset comprises dual-camera zoom image pairs from HuaweiPura70Ultra and RedmiK50Ulrr. Specifically, we collect Huawei data ourselves across variety indoor and outdoor scenarios, while the Redmi sequences are adopted from the publicly available dataset introduced in (50). The image resolutions vary by device: 2133x1600 for Huawei and 1632x122 1224 for Redmi, consistent with their native imaging pipelines.
In total, the combined dataset covers 132 real-world scenes, consisting of 79 indoor and 53 outdoor environments. Indoor scenes include structured environments such as classrooms, shopping malls, and cafeterias, while outdoor scenes cover playgrounds, parks, and other open spaces with greater geometric variation. For each scene, we capture 15 pairs of main-wide camera images, along with 6 additional wide-angle views from peripheral viewpoints to enhance spatial diversity. These supplementary images increase the geometric baseline and play a key role in stabilizing 3DGS optimization. Representative examples from both devices are shown in Figure o
16

---

Huawei
Redmi
Frame 4
Frame 5
Frame 6
Frame 4
Framm
Frame 6
Figure 7: Representative frames (Frames 4, 5, and 6) from our synthetic ZI dataset. Each row illustrates three temporally adjacent frames sampled from a 16-frame virtual zoom sequence, demon- strating smooth transitions in both appearance and geometry. Examples are shown from Huawei and Redmi devices, covering diverse indoor and outdoor scenes with varying structural complexity.
A.3 Details of the synthetic ZI dataset and computational cost
We generate 205 synthetic zoom sequences, each consisting of 16 interpolated frames. These sequences serve high-quality supervision for training and evaluation in ZI tasks. Representative examples of the rendered dataset are shown in Figure
The training duration for each scene depends its visual complexity and structural content, with an average approximately 1.5 hours per scene. Rendering a complete zoom sequence with 16 interpolated frames typically takes less than 20 seconds. All procedures are conducted on workstation with 40 GB of memory, consistent with our main experimental setup.
A.4
Formula reasoning of 3D-TPR framework
A.4.1 Mathematical modeling and depth constraints for projective ambiguity
Modeling the projection from 3D space to 2D image plane: Let P (X, Y. 2)" denote 3D point in the scene, which is projected onto 2D image coordinates (z, y) via the pinhole camera model. The projection is formulated as:
fX Z y= Z
JY
[X Z -²
=(P)
(19)
where denotes the 3D-to-2D projection function, and f represents the camera's focal length. When an object moves through 3D space, its instantaneous displacement over a time interval At is governed by its 3D velocity vector (ux, By, T
AP vA At, by At, Az)r)).
(20)
where AP denotes the change in 3D position during At. Let ((P.t) denote the projection of 3D point P = (X, Y. Z)" onto the image plane at time t. When the object is in motion, the position of P evolves over time as:
P(++A)) P(( AP P(L) + VAL
(21)
The corresponding image plane displacement 8 (Ax, Ay) induced by 3D motion can be approxi- mated via first-order Taylor expansion:
Ôm
= (P vdt) (PP)
vât.
(22)
ap
17

---

where On is the projection Jacobian matrix Jx. which characterizes how variations in 3D positions affect the projected 2D coordinates. The term våt represents the instantaneous 3D displacement, and its transformation through Jx yields the corresponding image plane motion. By substituring the partial derivatives of the projection function, the Jacobian matrix Jz can be explicitly written as:
J -
0
fx
(23)
Equation 23 constitutes an underdetermined system of equations, whose solution space forms a straight line. Mathematically, this can be expressed as:
X Y Z
T
Vo + kn,
(24)
Z Z'''
where Vo represents a particular solution, n denotes the direction corresponding to the viewing ray, and & E R denotes an arbitrary real number. This formulation implies that infinitely many 3D velocity vectors, which differ only in their components along the line sight, can result in the same 2D image plane displacement. As a concrete example, consider 3D scene point located at P = 0,, 2)
Scene 1. Suppose the object moves purely along the horizontal axis with the velocity vector V1 = (1, 0, 0)" The resulting image plane displacement ó1 can be approximated via the projection Jacobian Jx as:
1 0+1.00+ 0+ (-
81 = - Al
2
A = 0 AL.
(25)
0-1+ 0+
fY
Scene Now consider the case where the object moves along the optical axis (i.e., depth), with the velocity vector V2 (0, 0, - -/ For the 3D point P displacement d2 is computed as:
(f. 0, Z)T, the resulting image plane
1x 0 0.0+ 4 0+ 0+ 2
1 0.00000+
82 = Jzz V2 At
At
At = 0
At.
(26)
The distinct 3D motions illustrated in Scene 1 and Scene 2 become indistinguishable when projected onto the 2D image plane, thereby leading to the non-uniqueness of motion estimation and highlighting the necessity of dipth-direetion constraints. By enforcing a constraint along the depth direction, the solution space is reduced from a continuous line single, physically plausible point, effectively eliminating the ambiguity and suppressing implausible variations along the depth axis.
A.4.2 Gradient reallocation with texture-focus strategy
Let ST = (x.y) M(x. y) 1 denote the set of texture pixels, where M is the binary mask defined in Equation [4] and let Se denote its complement. According to Equation [6] the expected value of gradient norm over texture regions satisfies the following for À > 0:
E
V s. = (1 + A) E [ [IVeelaells,
(27)
This formulation indicates that the texture-focus loss reweights the gradient flow to amplify super- vision in structurally informative regions. By scaling the gradient magnitude in Sy by a factor of
(1 + A). the network is encouraged to prioritize learning from texture-rich areas, which are typically more perceptually salient and semantically meaningful.
While expected value of the gradient norm within the texture region Sy is magnified by factor of (1 + A), the gradient norm the non-texture region Sc remains unchanged:
E || V Creetureeffo lss E e
(28)
Consequently, the texture-focus strategy effectively redistributes the gradient energy in the parameter space by selectively amplifying directions associated with texture regions, thereby enhancing supervi- sion in structurally informative areas. Under coomentum-based stochastic gradient descent (SGD), the network parameters are updated as:
0x+1 02 1/Vk, V& =3V=-1 + (1 B) Cestturee-ooouuuoeeee (0x))
(29)
18

---

where 9g denotes the network parameters at the k-th iteration, and 0k+1 represents the updated parameters after upplying weighted update in the direction of the momentum term Vy- Here, 1) is the learning rate, and Va is the accumulated momentum, which integrates the current gradient with past updates. We set the momentum coefficient to $ = 0.9, which determines the proportion of the previous momentum Va-1 retained in the current step. The gradient term V VoLtexture-foww (0x) is computed with respect to the texture-focus loss at iteration A:
Since the gradient of Ltezture- focus the corresponding entries in the gradient tensor exhibit proportionally larger magnitudes. This induces an optimization bias toward texture-dense regions. thereby encouraging edge sharpening and the recovery of high-frequency details. In contrast, gradients within the non-texture region Sc remain unchanged. This targeted reallocation strategy prioritizes learning in structurally salient areas, ultimately enhancing the perceptual fidelity and fine-detail quality of the interpolated frames.
focus within the texture region ST is scaled by a factor of (1 (1+A) X),
Our proposition formally demonstrates that explicitly amplifying momentum components along high-frequency texture directions offers two key benefits: (a) reduces the number of iterations required to reach local optima by modulating the effective learning rate, and (b) it suppresses oscillatory behaviors in the loss surface caused by the competing objectives of texture underfitting and background overfitting.
Under the flat- mat-minimm hypothesis for over-parameterized networks, this gradient reallocation strategy further flattens the loss landscape during finetuning, acting as an implicit regularizer that improves generalization to unseen texture distributions through curvature-driven optimization dynamics. Unlike perceptual losses that entangle high- and low-frequency signals within the shared feature space of pretrained encoders, our texture- focus strategy achieves frequency disentanglement via pixel-level gradient modulation in an encoder-agnostic manner, allowing more precise control over structural detail reconstruction only with an ancillary supervision.
A.4.3 Additional proof of mask penalty strategy
Normalization coefficient 4 M(1 M) E [0, 1], reaching the maximum peak value at M = 0.5. As M 0 or 1, the normalization coefficient value tends to zero. From Bayesian perspective, the mask value M is modelled as the parameter 035)) of the posterior Bernoulli distribution Q:
(M 0(-) (o (ernoulli(
(30)
where ~ represents the model-inferred confidence at pixel position p of the i-th scale, which is calculated by the middle layer of the network and equivalent to 0²² 86² is the parameter of the Bernoulli distribution, representing the probability that the mask value at position p. indicating the network's s confidence that the current pixel is dominated by the features from To-
The case of 1 indicates that the mask value at position p is fully dominated by To. whereas 0f implies complete dominance by I,. 0f5) = 0.5 shares the equal contributions from both two reference frames.
The interpolation network dynamically adjusts the weights by learning the distribution of 8/.) Since e5²) is directly adopted as the parameter of the Bernoulli distribution, its entropy H((B()) quantifies the uncertainty in the fusion decision:
H((Q) = H(((() ( log g89)) (1 0(() log(1- ((()).
(31)
A higher entropy value indicates greater uncertainty in the network's 's fusion decision at the current pixel, typically when 03²² approaches 0.5. In contrast, lower entropy represents higher confidence, corresponding to 0f.) values close to or 1. Assume that an implicit Beta prior distribution P is applied to p8²)
P(O()) 05- -0)) -1))--1
(32)
where we adopt a symmetric, U-shaped configuration by setting a = 3 < 1. The probability density is higher at f₂ 0 or 0p- = 1 and lower at median 8, 0.5, thereby encoding prior knowledge that induces a binarization tendency. Minimizing the KL divergence between the posterior distribution Q and the prior distribution P yields:
DRI(O]I)) H( P) H()),
(33)
19

---

where P) -Q log P() measures the cross-entropy between the posterior distribution Q and the prior distribution P. reflecting how well the posterior aligns with the prior. A smaller value indicates that Q aligns more closely with the distributional preferences encoded in P. H( () quantifies the intrinsic uncertainty of Q. with a higher value indicating increased ambiguity in decision-making.
By designing C x 012 ) derived in Equation 18 we implicitly impose a gradient direction consistent with the low-entropy Beta prior distribution P. When og approaches to 0.5, the gradient of H(Q, P) dominates the optimization direction since log? logP(6)))
((()) tends to be negative infinity. In that case, 0g²) will be forced to flee quickly from high-uncertainty areas, in alignment
with the gradient of ( mast- Once 0z²) enters the vicinity of or 1, the gradient of H(Q, P) decays, and the gradient of H(Q) begins to dominate. At this point, the gradient of - remains persistently operative throughout the optimization process, thus ensuring stable convergence of the model. Consequently, minimizing C effectively approximates the minimizing KL divergence D (Q)PP). This design is theoretically anchored in the Bayesian framework and promotes training stability through principled simplification of the target loss.
To sum up, the overall training target loss is:
Loverall = Orr Ltexture- focus + O.m Σ E
F + Go Loriginals
(34)
where af, Om, and On denote the weighting coefficients for the texture-focus loss, mask regular- ization loss, and baseline supervision loss, respectively. These weights be manually specified or adaptively optimized during training, depending on the architectural design and optimization dynamics of the model.
A.5
Comparative evaluation of virtual view synthesis method for ZI task
A.5.1
Comparison of virtual frame generation quality
To evaluate the effectiveness of synthetic supervision for the ZI task, we compare three virtual frame generation methods: ZoomGS [50], 3DGS [23], and our proposed pipeline. For a fair comparison, all baselines are reproduced using their official implementations and default hyperparameters. As shown Figure[8] our method yields noticeably higher-quality intermediate views, exhibiting sharper textures, reduced artifacts, and more accurate geometric consistency. In contrast, ZoomGS and 3DGS often exhibit geometric distortion and texture degradation, particularly in scenes with complex spatial layouts or tigh-frequency surface details. These findings highlight the superiority of our pipeline in producing visually reliable and semantically aligned supervision tailored for ZI training.
A.5.2 Evaluating virtual supervision via ZI finetuning
To assess the effectiveness of our synthetic supervision in real-world ZI scenarios, we conduct a downstream finetuning evaluation against two baselines: ZoomGS (50) and 3DGS 23 For each method, the corresponding virtual dataset used to finetune four representative FI networks, all integrated into our unified 3D-TPR framework. The resulting models are then evaluated on real-world test sets captured from four distinct smartphone platforms, enabling a comprehensive assessment of cross-device generalization performance.
Quantitative results. Table 6 reports results on Huawei and Redmi devices, both of which are included in all three synthetic ZI datasets. Our method consistently outperforms ZoomGS and 3DGS across all networks and metrics, demonstrating strong supervision quality in seen-device scenarios. While ZoomGS performs reasonably due to its device-specific training coverage, it lacks explicit spatial and color modeling. contrast, our spatial transition and dynamic color adaptation modules effectively handle cross cross-camera misalignment and photometric variation, resulting in sharper textures and more coherent frame synthesis, reflected by gains in CLIP-IQA and MUSIQ. 3DGS also employs Huawei and Redmi data, but builds on monocular reconstruction assumptions and ignores dual-camera geometric or ISP discrepancies. Consequently, the composite supervision shows less consistency, which universally degrades finetuning efficacy on all tasks.
20

---

INPUTS
3DGS
ZOOMGS
Ours
Figure 8: Visual comparison of virtual frame generation methods for ZI supervision. Compared to ZoomGS [50] and 3DGS [23], our method produces intermediate frames with fewer artifacts and better detail preservation, demonstrating superior supervision quality for training ZI models.
Table 6: Quantitative comparison on real-world dual-camera test sets captured by Huawei and Redmi devices, which are included in the synthetic dataset construction. Each FI model is evaluated under four supervision variants: original 3D-TPR pretrained (Base), and finetuned on 3DGS, ZoomGS, and our proposed supervision f. Bold indicates the best result, and underline denotes the second best.
HuaweiPurs70Pro
RedmiKSOUlta
Networks
Methods
NIQEL
CLIP-IQAT
MUSIQU
NIQEJ
CLIP-IQAT
MUSIQU
Base
3.8651
4.1537
0,4939
58.8362
4.8764
5,0247
0.4664
61.9235
RIFE
3DGS
3.8309
4,2570
0.5183
60.2724
4.8457
5.1787
0.3459
52.4562
Z
3.7902
4,0084
0.5013
59,0780
4.5598
4.7340
0.4716
61.2694
Oursy 3.7422
3,9360
0.5233
60.8585
4,4720
4.5195
0.4930
63,6990
0.5784
73.0233
0,4913
73.7573
IFRNet
3DGS,
3,5310
3,2570
0.5183
70.2725
4.2232
3.5776
0.4259
70.0659
ZuomGSy
3.5132
3,2769
0.5867
74.0218
4.0879
3.3928
0.5127
74.2311
Oursy
3.5035
3.2520
0.5909
74.1220
4.0695
3,3880
0.5152
74.3871
Base
3,8470
3,8566
0.5621
62.7403
4.3424
4,5974
0.4807
59 6666610
EMA-VEL
3DGS,
3 8888
3.4184
0.5540
62.7141
4.2464
4.4593
0.4908
59. 99.23122
ZoomGS
3.6360
3,8499
0.5229
61. 65,2508
4.2106
4,4610
0.4718
60. 1914
Oursy
3,8341
3,8467
0.5684
63.5048
3,8852
4.5519
0.4947
60.7579
3.6459
0.5498
4.7426
4.0235
0.4754
71.9982
AMT
3DGS f
71.4254
ZoomGS
3,3395
0.5847
73.7058
3.4472
0.5209
74.4339
Ours
3,4547
3.3121
0.6018
73.7092
0.5383
74.3325
Table 7 evaluates generalization to iPhone and OPPO: two unseen devices that are not involved in training synthetic data generation. Despite the domain gap, our method consistently achieves the best performance across all networks and evaluation metrics. In contrast, ZoomGS and 3DGS exhibit clear performance degradation due their limited capacity model cross-device geometric and
photometric variations. These results highlight the strong generalization ability of our pipeline, which provides robust, device-agnostic supervision across heterogeneous smartphone platforms.
Our method consistently outperforms existing virtual frame generation pipelines across both seen and unseen device scenarios. These gains are attributed the explicit modeling of cross-device geometric
21

---

Table 7: Quantitative evaluation of cross-device generalization on iPhone and OPPO, two unseen devices excluded from the synthetic dataset generation. Despite the domain shift, models finetumed with our supervision consistently outperform those trained with 3DGS and ZoomGS. Bold indicates the best result, and underline denotes the second best.
IPronx
OPPOFindXXSUta
Networks
Methods
NIQEL
PLY
CLIP-IQAT
MUSIQI
NIQEL
PIL
CLIP-IQAT
MUSIQ1
Base
4.1027
4.3503
0.5366
$9,6800
4,6100
5,0104
0.5366
63.7912
RIFE
3DGSS
3.9618
4,2914
0.4687
54,5013
4,4927
4.9957
0.4408
60.6637
ZnomGS
3 8950
4,1679
0.5411
59.63.39
4.4543
4.8111
0.5779
66 66.6404
Oursy
3.8601
4.0657
0,5492
60.7988
4,3968
4,6815
0,5830
67.1494
Base
3,6923
3.1942
0.5829
73.3569
5,2039
4,9603
0.5212
75.2718
IFRNet
3DGS,
3.6233
3,3400
0.5583
71.7621
0,4755
74.3894
Zuom(iS
Oursy
0.5949
73,8292
0.5461
75.4545
Base
4.0555
4,1304
0.5387
58.6204
4.5166
4,9022
0.5590
67.5921
EMA-VFI
3DGS
3.6258
3.8183
0.5418
59.2020
4.8225
4,9720
0.5868
66.8282
7oomGS
3 3,808
3,9542
0.5377
59:5872
4.5455
4.8818
0.5551
68.2806
Oursy
4,0934
4.1123
0,5503
60.1002
4.5071
4,8752
0.5591
67.6444
Base
3,7053
3,5367
0,5930
72.1243
5.3017
5,3808
0.4518
72.1563
AMT
3DGS 3.6991
3,4205
0.5644
71.6435
4.9155
5,0089
0.4639
72.5701
7 3,5192
3,4044
0.5985
71,6655
4.6889
4.7544
0.5581
72.6175
Oursy
3.5087
3.2661
0.6211
73.6150
4,6699
4,5702
0.5595
75.2187
transitions and photometric inconsistencies, two key factors for ensuring structurally accurate and perceptually consistent zoom interpolation in real-world.
Qualitative results. We further perform qualitative comparison of interpolation outcomes across four networks and four smartphone platforms in real-world ZI tasks. As shown in Figure g visualize the results of models finetuned with three types of synthetic supervision: ZoomGS. 3DGS, and our proposed approach, alongside the original 3D-TPR pretrained baselines without finetuning. ZoomGS, performs reasonably on Huawei and Redmi devices, which included in its training dataset. However, it still suffers from noticeable blurring and diminished structural fidelity, particularly around object boundaries and fine textures. 3DGS,, which lacks explicit device-specing modeling, exhibits limited generalization and introduces geometric distortions and color artifacts across all devices. In contrast, our method consistently generates sharper structures, more coherent textures, and significantly fewer artifacts. For instance, text contours appear more defined on Huawei, linear boundaries
better preserved on Redmi, and perceptual blurring is substantially reduced on OPPO and iPhone. These qualitative observations further underscore the importance of modeling device geometric and photometric characteristics. and highlight the superior visual fidelity enabled by our synthetic supervision pipeline.
Overall. Both quantitative and qualitative results consistently demonstrate the superiority of synthetic supervision across a wide range of networks and smartphone platforms. By explicitly modeling cross-device geometrie misalignment and photometric variation, our method enables more effective finetuning, yielding improvements in perceptual quality, structural fidelity, and generalization unseen devices.
A.6 Additional ablation experiment disparity map sources
To rigorously evaluate the robustness of disparity cues in our proposed 3D-TPR encoding, we systematically ablate the disparity map sources that constitute the critical foundation for similarity computation. Employing EMA-VFI 53 backbone with RAFT-based warping 42], we conduct comparative analyses between our lightweight Lite-Mono S4 architecture and the sophisticated DepthPro The experimental results are shown in Table8
is crucial emphasize that this ablation study does not directly assess the disparity variation vector itself, but rather evaluates the quality and reliability of source similarity maps utilized in constructing the 3D trajectory vectors. Experimental results on the Vimeo90K benchmark demonstrate that both
22

---

RIFE
Base
3
ZOOMGS,
Oursy
Base
3DGSS
ZOOMGS,
Ours
EMA-VFI
AMT
Huawei
Base
DDGS,
ZOOMGS,
Oursy
Base
3DGS
ZOOMGS,
Oursy
RIFE
IFRNet
Base
3DGS,
ZOOMGS,
Oursy
Base
3DGS,
ZOOMGS,
Oursy
EMA-VFI
AMT
Redmi
Base
3DGS,
ZOOMGS,
Oursy
Base
3DGS,
ZOOMOS,
Ours,
IFRNet
Basc
3DGS,
700MGS,
Ours,
Base
3DGSS,
ZOOMGS,
Ours
EMA-VFI
AMT
iPhone
Hase
3DGSS
Z0OMGS,
Ours,
Base
3DG3
ZOOMGS,
Ours
RIFE
IFRNet
Base
3DGS,
ZOOMGS,
Oursy
Base
3DGS,
ZOOMGS,
Ours,
EMA-VFI
AMT
OРPO
Base
3DGS,
ZOOMGS,
Ours,
Base
3DGS,
ZOOMGS,
Ours,
Figure 9: Qualitative comparison of finetuned interpolation results on real-world ZI tasks across four smartphone platforms and four FI networks. We compare models finetuned with virtual supervision from 3DGS, ZoomGS, and our proposed method, alongside unfinetuned baselines ("Base") imple- mented within the unified 3D-TPR framework. Our method consistently produces sharper structures and fewer artifacts. Notably, text contours are better preserved on Huawei, boundary lines are more clearly delineated on Redmi, and perceptual blurring is substantially reduced on iPhone and OPPO.
Table 8: Ablation study disparity map sources.
Method
PSNR 1
SSIM 1
LPIPS
2D
24.73
0.851
0.081
IPR(Lite-Mono)
24.86
0.853
0.080
3D-TPR JD-TPR(Depth-pro)
24.90
0.852
0.079
23

---

Huswei
Redmi
iPhone
1,
Figure 10: Representative dual-camera samples from our real-world multi-device Zl test sets. Each row corresponds to different smartphone platform (Huawei, Redmi, iPhone and OPPO), showing paired zoom images captured under diverse scenes and lighting conditions. For layout consistency, RedmiK50UItra samples rotated 90 degrees counterclockwise.
3D-TPR implementations consistently surpass the 2D baseline, validating the stability and gener- alizability of our motion priors across different disparity estimators. Notably, while DepthPro (7) generates perceptually sharper outputs, its computational complexity yields only marginal perfor- mance gains. Consequently, we adopt Lite-Mono [54] as our preferred disparity estimator, achieving an optimal balance between training efficiency and reconstruction quality.
A.7 Details of the real-world multi-device Zl test sets
To comprehensively evaluate the generalization ability of different ZI methods, construct real- world multi-devic test sets spanning four mainstream smartphone platforms: StuawiiPura70Ulie RedmiK OPPOFindX7Ultee and iPhonel 66rroMax. For each device, collect substantial number of dual-camera zoom sequences under diverse environmental conditions and usage scenarios.
The complete test sets consist of 499 sequences, including 160 from Huawei, 69 from OPPO, 170 from iPhone, and 100 from Redmi. These sequences span a broad range of real-world scenes, such indoor environments (e.g.. offices, shopping malls, cafés) and outdoor locations (e.g., parks, sidewalks, playgrounds). with variations in illumination, motion intensity, and zoom transitions to reflect practical deployment conditions.
Image resolution varies by device: Huawei (2580x1560) Redmi (1216x1632)) iPhone (2016x1512), and OPPO (2048x1536) 536). Each sequence captures paired wide and main camera images along zoom trajectory, providing a challenging and diverse benchmark for evaluating Zl methods. Representative samples from the test sets are shown in Figure 10

---

INPUTS
RIFE
IFRNet
EMA-VFI
AMT
Huawee
Huawei
Redmi
Redmi
iPhone
iPhone
OPPO
OРPO
3D
3D,
3D
3D,
3D
3D,
3D
3D,
Figure 11: Additional visual comparisons real-world ZI test data from four smartphone platforms. Subscript y indicates models finetuned on our dataset. Finetuned models produce sharper structures and improved geometric consistency across diverse scenes.
A.8 Additional visual results of OmniZoom the real-world ZI test sets
To further evaluate the generalization capability of our method, we present additional visual com- parisons the real-world ZI test sets. Specifically, compare 3D-TPR models before and after finetuning on the ZI dataset across four smartphone platforms. As shown in Figure TT the finetuned model produces more temporally consistent and perceptually sharper interpolations, particularly in challenging regions involving parallax, fine textures, or low-light conditions. These results further demonstrate the effectiveness of our ZI dataset enhancing cross-device generalization for real-world zoom interpolation.
A.9 Benchmarking Zl dataset across 1D, 2D, and 3D-TPR frameworks
The ID-indexed models are directly adopted from publicly released versions each corresponding FI network. The 2D-indexed counterparts follow the interpolation strategy introduced in [60]. For each framework, we finetune the models using our proposed ZI dataset and evaluate their performance on the real- world multi-device test sets. Both qualitative and quantitative comparisons assess the improvements enabled by ZI supervision under different indexing paradigms.
conducted to
Quantitative comparisons. We conduct comprehensive evaluation of the 1D, 2D, and 3D-TPR in- dexing frameworks across four models and four real-world smartphone platforms, considering both the base models and those finetuned our ZI dataset. As shown in Table 9 the 3D-TPR framework
25

---

Table 9: Quantitative result of ID. 2D, and 3D-TPR frameworks across four FI models and four devices. All models are evaluated before and after finetuning on our ZI dataset. Bold indicates the best performance after finetuning, and underline marks the best result before finetuning. 3D-TPR framework reliably achieves superior perceptual quality, and its finetuned variant (3D,) outperforms others in most cases, demonstrating the effectiveness of our 7.1 dataset and the 3D-TPR design.
RIFE
IRRNet
Device
Metrics
ID
2D
3D
ID5
2D,
3D,
ID
2D
3D
ID;
2D/
3D
NIQEL
3,9181
3.8464
3.8651
3.8678
3,8170
3.7422
3.6995
3.6801
3,4852
3.6044
3.6455
3,5035
Huawei
PLU
4,2567
4,2505
4.1537
4,0698
4.0599
3,9360
4.0141
3,9570
3.3150
3.6448
3.6136
3,2520
CLIP CLIP-IQAT
0.4078
0.3691
0.4939
0.4573
0.4858
0.5233
0.4910
0.5422
0.5784
0.5649
0.5770
0.5909
MUSIQI
49.4314
44,8268
58,8362
58,4623
58,7098
60.8585
$1,2033
57,4632
73.0233
61,2380
63.1001
74.1220
NIQEL
5,2253
5.0138
4.8764
4.6925
4.5118
4.4720
5.3165
5.0098
4,2837
4.6223
4.5292
4.0695
Redmi
PLU
5,6387
5.3615
5.0247
4.8289
4.6331
4.5195
5.5147
5.1108
3.5993
4.6454
4.5053
3,3880
CLIP-IQAT
0.3661
0.4077
0.4664
0,4559
0,4851
0.4930
0.3691
0,4219
0.4913
0.4765
0,4947
0.5152
MUSIQU
51.7491
56.3870
.235
60.6873
62,0812
63.6990
51.3791
57.5453
73.7573
61.7805
62.4252
74.3871
NIQEL
4.3433
4,2031
4. 4.1027
3,9339
3,9532
3.8601
4,0136
3.8786
3,6923
3.7891
3.8169
3.6953
iPhone
PLP
4.6718
4,5340
4.3503
4.1674
4.2135
4.0657
4,4137
4,2090
3.3942
4.0014
3,9636
3.3165
CLIP-IQAT
0.4476
0.4821
0.5363
0,5371
0,5345
0,5492
0.4814
0,5240
0,5829
0.5666
0,5778
0.5949
MUSIQU
52,2715
55,0577
59,6800
$9,2367
58,8978
60.7988
52,7781
57,4088
73,3569
60,8860
61,9734
73.8292
NIQEL
5,2103
4,6364
4.6100
5,0142
4.5575
4.3968
5.9164
4.5820
5.2039
5.6720
5,7078
5,1133
OPPO
PIJ
5,4442
5,0848
5.0104
5.0965
5,0650
4.6815
5,1909
4,9749
4,9603
5,0287
4.8435
4.7144
IQA1
0.4270
0.4989
0.5366
0.5450
0.5699
0,5830
0.5181
0.5525
0.5212
0.5207
0,5667
0.5461
MUSIQI
54.8990
63.2645
65.7912
63.3583
65.0204
67.1494
54.8757
67.2635
75.2718
64,9371
67.2739
75.4545
Device
Metrics
EMA-VFI
AMT
1D
2D
3D
IDy
2D
3D,
ID
2D
3D
ID;
2D,
3D
NIQEL
4.6877
3.6363
3.8470
3.7737
3,4852
3.8341
4,7231
3.5665
3.6459
3,9866
3.6246
3,4547
Huawei
3,7060
3,7240
3.8566
3,638
3,7186
3.8467
4,9499
3,5147
3.6132
3,9974
3,4639
3.3121
CLIP-IQAL LUP-IQQA
0.5619
0,5612
0.5621
0.5588
0.5535
0.5684
0.3127
0.5428
0.5498
0.5898
0.5944
0.6018
MUSIQU
60.4395
61.7263
62.80
8008
63.0230
63.5048
53.6305
71.4369
71.7016
73,4803
72.7456
73.7092
NIQEL
4.5060
4,4088
4.3424
4.4864
4.3537
3.8852
5,9112
5.1925
4.7426
4,9700
5,1082
3,9835
Redmi
PL
4,6410
4.6708
4.5974
4.5829
4.5665
4.5519
5.0623
5.4335
4.0235
4.8863
5.0036
3.3548
CLIP-IQAT
0,4651
0.4599
0.4807
0,4809
0,4812
0.4947
0.4191
0.4336
0.4754
0.5082
0,4944
0,5383
MUSIQI
55.3260
57.0371
39.6610
60,2753
59,9715
60.7579
49,2505
57.6671
71,9982
63.6003
61.2496
74.3325
NIQEL
4,9410
4,8155
4.0555
4.7661
4,2139
4.0934
5,0436
3.5932
3.7053
3.5792
3,5883
3.5087
iPhone
PL
5,0171
3,9994
4.1304
4.5345
3,8462
4,1123
5,0410
3.4293
3.5367
3.3379
3,3233
3.2661
CLIP IQA1
0.4603
0.5352
0.5387
0.5367
0.5462
0.5503
0.3723
0.5931
0.5930
0.6064
0.6120
0.6211
MUSIQI
55,5380
58.1422
58.6204
57.6496
19.7
60.1002
57.8120
71.9805
72.1243
73.5609
72.9853
73.6150
NIQEL
5.0104
4,5568
4.5166
4.9700
4.5293
4.5071
5.7811
4.9042
5.3017
4.7389
4,7885
4.6699
OPPO
PT]
5.1828
4,9569
4.9022
5.0729
4.9006
4.8752
5.8036
4.9676
5.3808
5.0513
4.9911
4.5702
CLIP-IQAT
0.5820
0.5622
0.5590
0.5071
0.5502
0.5591
0.3499
0.4748
0,4518
D.4673
0.5461
0.5395
MUSIQ1
67.0395
67. 4327
67,5921
67.1736
67.5282
67.6444
54.9076
73.0842
72,1563
72,8543
74.7868
75.2187
consistently outperforms its 1D and 2D counterparts after finetuning, achieving the best performance (highlighted in bold) across nearly all perceptual quality metrics. Notably, even without finetuning, 3D-TPR frequently ranks second (underlined), demonstrating strong generalization capability and architectural robustness. In contrast, ID and 2D variants exhibit only limited performance gains, particularly under challenging cross-device settings. These results support two conclusions: (I) while our ZI dataset provides performance gains across all indexing paradigms, the improvements are most pronounced under the trajectory-awant 3D-TPR framework; and (2) OmniZoom (i.e., -TPR offers a universal, plug-and and- play solution for real-world cross-device zoom interpolation, effectively bridging domain gaps and enhancing perceptual quality across diverse hardware platforms.
Qualitative comparisons. Beyond quantitative evaluation, we provide qualitative results in Fig- ures 12 and [3 illustrating ZI outputs on Huawei, Redmi, iPhone, and OPPO devices across four Fl networks. Each figure presents six variants per network: ID, 2D, and 3D, along with their finetuned counterparts (ID,, , 2D,, and 3D,).
26

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Hunwei
RIFE
Hunwei IRRNet
Hurwei EMA-VEE
Hunwei
AMT
Redmi RIFE
Redmi IFRNet
Rodmi EMA -VII
Rodmi AMT
ID
2D
3D
ID,
2D,
3D,
Figure 12: Qualitative comparisons Huawei (top four rows) and Redmi (bottom four rows). While ID and 2D models exhibit noticeable blurring and detail loss across networks, 3D, effectively restores fine details, producing sharper structures and improved local contrast.
Several key observations emerge from these visualizations. First, the 3D configuration consistently produces sharper and more coherent results than its ID and counterparts, highlighting the effectiveness of the 3D-TPR framework in real-world ZI tasks. Second, across all indexing paradigms, all finetuned models show clear perceptual improvements, demonstrating the broad applicability of our ZI dataset across diverse Fl architectures.
27

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RIFE
IFRNet
EMA VFI
ANT
OPPO
RIFB
OPPO
IFRN
EMA-VFI
OPPO
OPPO AMT
ID
2D
ID,
2D,
3D,
Figure 13: Qualitative comparisons on iPhone and OPPO test sets under rich structural conditions. While ID and 2D variants exhibit edge instability and perceptual artifacts, 3D, produces noticeably clearer results with sharper contours and reduced blurring, effectively preserving structural fidelity.
Notably, 3D, achieves the highest perceptual quality in nearly all cases, with fewer artifacts and enhanced texture fidelity. For example, on Redmi sequences, 3Dy recovers sharper character contours and fine line structures compared to other variants. On iPhone and OPPO, where scenes contain more intricate textures and geometric structures, ID and 2D variants frequently introduce ghosting and edge artifacts, issues that substantially mitigated by the 3D, configuration.
These results underscore the strength of OmniZoom as a unified, cross-device solution for plug-and- play zoom interpolation, offering robust perceptual quality across various hardware platforms.
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Table 10: Upper bound evaluation of the proposed 3D-TPR framework on four FI networks using ground-truth 3D supervision. The 2D framework serves as baseline 60] All experiments on 3D use the 3D trajectory progress ratio as timestep map. 3D-t-m indicates using only 3D-TPR encoding;
3D-m adds texture-focus strategy; 3D-t includes mask penalty constraint; and 3D integrates all
components. Bold indicates improvements over 2D baseline.
RIFE
IFRNet
EMA-VFI
AMT
Network
PSNRT
SSIMT
LPIPS
PSNRT
SSIMT
LPIPS
PSNRT
SSIMT
LPIPS
PSNR1
SSIMT
LPIPS
2D
27,4050
0.9010
0.0863
27.1276
0.8994
0,0780
24.7313
0,8514
0.0810
27.1726
0.9017
0.0807
3D---m
28.7510
0.0773
28,2484
0.0715
25,4078
0.8618
0.0780
28,4850
0.9269
0.0817
3D-m
28.7311
0.0764
28,3486
0.9241
0.0690
25.4705
0.8631
0.0800
28.5942
0.9285
0.0767
3D41
28.5518
0.9234
0.0764
28.1917
0.9206
0.0686
25,3557
0.8618
0.0781
28.5895
0.9277
0.0810
3D
28.6548
0.9255
0.0773
28.2967
0.9220
0.0656
25.4984
0.8633
0.0770
28.6271
0.9286
0.0768
A.10
Upper bound analysis of 3D-TPR framework
To evaluate the performance ceiling of the 3D-TPR framework, we adopt ground-truth 3D geometry as the timestep map, thereby ensuring perfect consistency between training and inference. As shown in TO, we conduct a complementary ablation study under this setting for fair comparison.
Results indicate that utilizing consistent 3D similarity maps during both training and testing leads to substantial gains across all metrics and networks: PSNR improves by over 1.0 dB on average, SSIM consistently increases, and LPIPS is significantly reduced. These results reveal the latent capacity of the 3D-TPR framework when accurate geometry is available throughout.
This upper bound analysis provides valuable insight into the theoretical capacity of our method. While full 3D supervision may not be available in practice, our empirical findings suggest that leveraging lightweight geometry estimation modules or substituting with physically obtainable priors (e.g., depth from stereo, SLAM-based pose, monocular depth) can approximate similar benefits. This observation offers practical guidance for future extensions that aim to balance reconstruction quality and computational feasibility.
A.11 Additional visual results of 3D-TPR framework
We present additional qualitative comparisons between the proposed 3D-TPR framework and con- ventional 2D baselines, as shown in Figure 14 The results demonstrate that 3D-TPR effectively restores degraded regions in 2D-based outputs, particularly in areas suffering from motion-induced blur where structural details are severely corrupted or entirely missing.
Moreover, 3D-TPR alleviates misalignment artifacts commonly encountered in 2D models when handling textures with high levels of repetition and self-similarity, such as linear structures dot-like patterns, by incorporating trajectory-awant aware spatial priors. In addition to these localized corrections, method consistently produces globally improved visual quality, exhibiting sharper details, better structural coherence, and a more realistic overall appearance. These findings further highlight the advantages of integrating 3D spatial awareness into temporal interpolation tasks.
A.12 Broader impacts
This work is motivated by the goal of enhancing frame interpolation in zoom scenarios, with direct benefits for wide range of applications in mobile photography, video confereneing, augmented reality (AR), and digital content creation. Our proposed framework improves the temporal smoothness and spatial consistency of interpolated video frames across heterogencous smartphone platforms, contributing to better visual quality in real-world capture workflows. In particular, it enables smoother zoom transitions without requiring any hardware-level changes due to zero cost, offering cost-effective enhancements for existing consumer devices.
Such capabilities are especially impactful in mobile videography, where users often experience abrupt visual discontinuities during zoom. Our method can help reduce motion artifacts and maintain semantic coherence in challenging scenes, which is beneficial for video-based communication,
29

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GroundTTruth
RIFE
IFRNet
EMA VFI
AMT
2D
3D
2D
3D
2D
2D
Figure 14: Visual comparison of 2D and 3D interpolation results across networks at the same timestep. The 3D method consistently yields less-blurreds and neater outputs across all networks.
content sharing on social platforms, and immersive AR experiences that rely on high frame fidelity. Furthermore, cross-device compatibility and plug-and-pla integration facilitate broader adoption across hardware vendors, making ad advanced interpolation accessible without retraining for each model.
Nevertheless, with other generative video technologies, improvements in visual realism may pose risks misused, such as enabling the creation of misleading or manipulated video content. While our framework is restricted to supervised frame interpolation under dual-camera settings and does not support arbitrary video generation, we emphasize the importance of responsible use. To mitigate misuse, do not release general-purposs generative models, and our dataset is limited to constrained zoom interpolation scenarios. We encourage future efforts to pair such technologies with appropriate safeguards and transparency mechanisms ensure ethical deployment.
30

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37


Reviews:
[{"review_id": "AVWQgoh1H4", "reviewer": "Reviewer_Wduj", "summary": "OmniZoom presents a universal plug-and-play paradigm for cross-device smooth zoom interpolation. This paper defines zoom interpolation as a critical task for dual-camera smartphones to synthesize geometrically and photometrically consistent intermediate frames during zoom transitions. The primary challenges addressed are: 1) the significant geometric and photometric inconsistencies between cameras, 2) the lack of suitable ground-truth intermediate frames for training, and severe motion ambiguity caused by complex, and 3) non-linear object motion and parallax effects inherent in zoom transitions. OmniZoom tackles these issues through two key design insights: first, a novel cross-device virtual data generation method utilizing 3D Gaussian Splatting. Second, it introduces a plug-and-play 3D Trajectory Progress Ratio (3D-TPR) framework that encodes 3D motion-aware correspondence, integrating a texture-focus strategy for high-frequency detail preservation and mask penalty constraints to suppress interpolation artifacts. The evaluation highlights leading performance on various smartphone platforms.", "questions": "What is the supported input resolution of OmniZoom? Based on the details of FI models, it seems the FI models are pre-trained on a resolution of 448x256. Is this the target output resolution of OmniZoom? If so, please explain why this relatively low resolution is suitable for mobile photography. If not, please specify the target resolution and explain if there is any mismatch between the pretraining resolution and fine-tuning resolution. If the mismatch exists, I would also like to know if that could lead to any model performance issues.", "limitations": "One key component of the proposed work is the generated synthetic data. Based on the paper, there were 205 sequences generated, and\n16 frames each. Considering the paper aims to support cross-device smooth zoom support, it is hard to be convinced that this data scale can truly support the goal. A deeper discussion about the data generation details would be great. I am also curious if any potential data augmentation steps could be integrated.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "- The work shows promising testing results on real-world images from various mobile phones.\n- The framework is built on top of established dual-camera smooth zoom designs and provides extended support in visual qualities.\n- The paper is well structured and written.", "was_revised": true, "final_justification": "The author has sufficiently discussed the limitations and missing technical details. Given the potential broad impact of the introduced work, I'm still leaning accepting. My final rating score remain unchanged.", "rebuttal": "- **Questions:**\n\nThank you very much for this insightful question. We sincerely appreciate your attention to detail and your concern about the resolution settings, which are indeed crucial for evaluating the practicality and scalability of our framework in real-world mobile photography scenarios.\n\nWe would like to clarify that during pretraining, we used the Vimeo-90K septuplet dataset with a native resolution of 448×256. During finetuning, our synthetic Zoom Interpolation dataset contains higher-resolution samples: 1632×1224 for Redmi and 2133×1600 for Huawei. However, we adopt **a patch-based training strategy** in both stages, consistent with the original design of each FI model: we use 256×256 patches for EMAVFI, and 224×224 for RIFE, AMT, and IFRNet, identical to their official training protocols. Therefore, there is **no resolution mismatch** between the pretraining and finetuning stages.\n\n*At inference time*, **OmniZoom supports arbitrary-resolution inputs and can seamlessly adapt to the native resolutions of real mobile devices**, including 1632×1224 (Redmi), 2133×1600 (Huawei), 2016×1512 (iPhone), and 2048×1536 (OPPO). Although these resolutions are higher than the training patches, we observe **no noticeable degradation in performance or visual quality** for two key reasons:\n\n  - **All FI models used are fully convolutional**, and therefore generalize naturally to higher resolution inputs without any structural modification or performance degradation.\n\n  - **The Zoom Interpolation task exhibits continuous and smooth camera motion**, with predominantly local spatial variations. As such, models trained on moderate-sized patches are capable of capturing high-resolution local correspondences effectively at test time.\n\nWe greatly appreciate this question as it allowed us to better explain this design detail, and we will revise the paper accordingly to ensure clarity for readers.\n\n---\n\n- **Limitations:**\n\nWe sincerely thank the reviewer for raising this important point regarding data scale and its role in enabling cross-device smooth zoom interpolation.\n\nWhile our dataset consists of 205 synthetic zoom sequences (16 frames each) captured from two devices, we emphasize that its design **prioritizes structural diversity and cross-device generalizability over raw volume**. Specifically, we focus on capturing **geometric and photometric variations** that commonly arise in real-world zoom operations across devices.\n\nOur device-agnostic supervision pipeline includes:\n\n  - **Device-agnostic spatial transition modeling:** We interpolate both intrinsic and extrinsic parameters between wide and main camera views to simulate smooth and geometrically coherent virtual viewpoints, independent of device-specific assumptions.\n\n  - **Photometric decoupling via color adaptation:** A learnable ColorNet predicts per-Gaussian color offsets to adapt to photometric shifts between devices, ensuring robust rendering across camera styles.\n\n  - **Cross-domain consistency constraints:** We enforce semantic and textural consistency between rendered and real images, enhancing realism and semantic alignment.\n\n  - **Extensive data augmentation:** We apply random resizing, cropping, horizontal/vertical flipping, rotation, temporal reversal, channel permutation, and pose jittering to enrich the diversity of spatial and photometric variations. These augmentations empirically improve the model’s robustness under diverse conditions.\n\nIn addition, all models are initialized from strong pretrained FI backbones (e.g., RIFE, EMAVFI) and **finetuned with our synthetic supervision**. This significantly reduces the demand for large-scale data while ensuring effective adaptation to zoom-specific motion.\n\nAs shown in Table 2 and Figure 4, our models **generalize well to unseen devices** (e.g., iPhone, OPPO), validating the transferability of our supervision design. In future work, we plan to expand the dataset to include a wider variety of devices and optical settings."}, {"review_id": "WfUg0hJuOu", "reviewer": "Reviewer_9EWN", "summary": "The paper proposes a deep learning framework for smooth transitions across differently configured cameras, such as dual cameras in smartphones. While prior work on frame interpolation partially addresses smooth transitions between frames, key challenges remain: (i) zoom interpolation lacks suitable ground-truth intermediate frames, as collecting such data requires continuous zoom using dual-camera hardware; and (ii) motion ambiguity, which has been addressed by prior work such as InterpAny. To overcome data scarcity, the proposed framework leverages 3D Gaussian Splatting to synthesize virtual intermediate frames. The second issue (motion ambiguity) is addressed by a 3D Trajectory Progress Ratio (3D-TPR) mechanism that uses a pixel-wise similarity map to reduce artifacts. Experimental results show some improvement on real-world data.", "questions": "Thank you for submitting your work to NeurIPS 2025.\nThe challenge addressed in this paper is important, particularly for enabling seamless experiences on multi-camera mobile devices, which are increasingly ubiquitous.\nHowever, there are three major concerns related to the novelty of the proposed framework, the result, and the clarity of the writing.\n\n1. The core technique —using a pixel-wise similarity map-- appears closely related to the approach introduced in InterpAny. To highlight the novelty of this work, it would be helpful to clearly articulate how the proposed method differs conceptually or technically from InterpAny. In particular, I suggest authors to clearly explain how the \"motion ambiguity\" dealt in this paper is different from that dealt in InterpAny, and how the core technique resolves this challenge.\n2. A key limitation is that the proposed framework integrates multiple components--3D-TPR encoding, texture-focus strategy, and mask penalty constraint--yet the overall performance gain is modest and not the best among ablations (Appendix Table 5), especially compared to the improvement achieved by InterpAny. Furthermore, the results do not include error bars or statistical significance measures, making it difficult to assess the robustness of the improvements. Clarifying the rationale for including all components and providing evidence of statistical significance over baselines would strengthen the empirical claims.\n3. The paper uses several under-explained terms, which makes it difficult to fully understand the proposed approach:\n  - \"Velocity ambiguity\" is mentioned without definition or citation. Although it appears once in the InterpAny paper, the lack of a direct reference and explanation makes it unclear what the challenge entails or how 3D-TPR addresses it.\n  - \"Fixed-timestep map\" is another term not found in the referenced literature and should be briefly clarified.\n  - The paper also does not explain the justification for how decoupling geometric and photometric characteristics remove device-dependent parameters; an explaination would help clarify this important design choice.", "limitations": "yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "- Strengths:\n  - The paper introduces a novel and interesting problem named Zoom Interpolation.\n  - The paper includes thorough experiments, including ablation studies, to demonstrate the framework's performance under diverse conditions.\n  - The proposed method effectively integrates with and improves four different frame interpolation modules.\n\n- Weaknesses:\n  - The \"motion ambiguity\" that the authors aim to address in Zoom Interpolation is not clearly explained, making it difficult to fully understand the specific challenge and how the proposed techniques effectively resolve it. As a result, the perceived novelty of the techniques is diminished.\n  - The integrated components offer limited performance gains without statistical evidence, and their inclusion lacks clear justification.\n  - Several terms (e.g., velocity ambiguity, fixed-timestep map, ...) are insufficiently explained, making the method hard to interpret.", "was_revised": true, "final_justification": "I raised my final rating as 3 from 2. Revision version adressed my concerns about the method's minimal performance gains by providing additional evaluations on perceptual quality.", "rebuttal": "- **W1 & Q1: Clarification of \"Motion Ambiguity\" and Novelty Compared to InterpAny.**  \n\nWe thank the reviewer for this important question.  While both our method and InterpAny [1] adopt pixel-wise similarity maps, our core idea is to reformulate motion progress estimation **in the full 3D space** by leveraging monocular depth, offering a more **physically grounded** and **geometrically discriminative** alternative to tackle velocity ambiguity. We clarify the terminology and highlight the key differences below.\n\n**1. Clarifying Terminology:**\n\n**Fixed-timestep map:**  The fixed-timestep map is typically used as **the temporal input for interpolation models**, where the start and end frames are assigned time values of 0 and 1, respectively. It represents the specific time value for the frame to be inserted. For example, interpolating the middle frame corresponds to a map filled with 0.5. This 1D time prior, however, lacks object-specific motion awareness.\n\n**Velocity ambiguity:**  As defined in InterpAny, velocity ambiguity refers to **the inherent uncertainty in object motion trajectories between two frames**, due to the absence of intermediate observations. A given pixel may follow multiple plausible paths (e.g., curved vs. linear motion) or exhibit non-uniform speeds (e.g., due to foreshortening). Since fixed-timestep maps provide uniform temporal priors, interpolation models cannot resolve such ambiguity per object.\n\n**2. Comparison with InterpAny**\n\n**InterpAny's approach:**  InterpAny tackles velocity ambiguity by replacing the fixed-timestep map with a 2D distance index map, computed from the similarity between optical flows. This encourages the model to infer spatially adaptive motion progress during training.  \nHowever, this formulation is limited to motion in the xy-plane. As shown in Table 2 and Figure 3 in the main text, relying solely on 2D optical flow leads to **indistinguishable distance indices for geometrically distinct 3D motions**, thereby **failing to resolve velocity ambiguity in realistic scenes**.\n\n*For instance, in zoom scenarios where objects exhibit foreshortened motion (i.e., moving toward or away from the camera), InterpAny’s 2D similarity map fails to differentiate fronto-parallel versus depth-variant trajectories, leading to ghosting or temporal drift. **Our 3D-TPR explicitly addresses this case.***\n\n**Our 3D-TPR approach:**  We extend the 2D distance index into 3D space by incorporating depth variation. Specifically, we estimate monocular disparity map and compute the 3D trajectory vectors between the target and end frames. Their cosine similarity yields a **3D trajectory progress ratio (3D-TPR)**, which better **reflects true physical motion and resolves velocity ambiguity in both spatial and depth dimensions.** This 3D similarity map replaces the fixed-timestep map, just as in InterpAny, but provides richer geometric priors. The formulation is fully differentiable and **can be plugged into existing VFI models without architectural changes**, applicable to existing VFI models as a plug-in.\n\n**3. Additional Contributions Beyond InterpAny**\n\n- We propose a **gradient-coupled texture-focus strategy** that enhances sharpness in high-frequency regions by reweighting the loss using image gradients. This design allows texture regions to receive stronger supervision without relying on pre-trained perceptual networks (Appendix A.2.2).\n\n- We introduce a **mask uncertainty penalty** to regularize ambiguous fusion masks in bidirectional interpolation. This leads to improved sharpness near motion boundaries and promotes faster and more stable convergence during training (Appendix A.2.3).\n\n**4. Summary of Differences**\n\nThe table below compares InterpAny and our 3D-TPR framework across major aspects. Our method introduces three main improvements:  \n(1) We extend motion modeling into **3D space** using 3D-TPR encoding, which better distinguishes depth-related motions.  \n(2) A **texture-focus strategy** that enhances detail preservation in high-frequency regions.  \n(3) A **mask uncertainty penalty** to incentivize perceptually sharper frame synthesis.\n\n|**Method**|**1D temporal indexing**|**2D spatial indexing**|**3D depth indexing**|**Preserves texture details**|**Suppresses occlusion ambiguities**|\n|---|:---:|:---:|:---:|:---:|:---:|\n|InterpAny|✓|✓|✗|✗|✗|\n|**3D-TPR**|✓|✓|✓|✓|✓|\n\n- **W2 & Q2: On the necessity of all components and the significance of overall performance gain.**\n\nWe sincerely thank the reviewer for these valuable comments. Although improvements on pixel-centric metrics such as PSNR and SSIM appear numerically modest, the qualitative results (e.g., reduced ghosting and enhanced sharpness in Fig. 3 and Fig. 4) demonstrate **clear perceptual benefits**.  \nWe argue that in most real-world applications, the goal of video frame interpolation (VFI) is not to produce pixel-aligned ground truth reconstructions, but to **synthesize plausible intermediate frames with high perceptual quality**. Furthermore, PSNR and SSIM are less sensitive to common VFI artifacts such as blur and ghosting [2], which our design explicitly targets by **resolving velocity ambiguity**. As such, pixel-centric metrics are not always informative in evaluating perceptual quality.\n\nTo better reflect perceptual quality, we evaluate three additional perceptual metrics: **PI**, **MUSIQ**, and **FLOLPIPS** [3] on the Vimeo90K benchmark using EMA-VFI. Due to space constraints, we include results from one representative network here, while full results will be provided in the revised paper. These metrics are more aligned with human judgment and highlight the strengths of each component.  \nNotably, **FLOLPIPS is specifically designed for video frame interpolation,** and it uniquely incorporates **motion consistency priors** via flow-guided weighting. This makes it particularly appropriate for evaluating perceptual quality and temporal coherence in smooth zoom interpolation.\n\nAs shown below, our method consistently outperforms InterpAny (2D baseline) across all metrics.  \n**Importantly, our design is not a naive stacking of modules.** Each component is intentionally designed to target specific and well-known limitations in Zoom Interpolation: **motion ambiguity**, **texture degradation**, and **occlusion ambiguities**. Their effects are distinct, measurable, and complementary:\n\n- The **3D-TPR encoding** (3D-t-m) enhances motion consistency over the 2D baseline, reflected by a clear improvement in **FLOLPIPS** (0.128 → 0.119). This reduction suggests that incorporating 3D trajectory cues helps mitigate motion ambiguity and improves flow-aligned perceptual coherence.\n\n- The **texture-focus strategy** (3D-m) achieves the most significant drop in **FLOLPIPS** (0.128 → 0.113) and the largest gain in **MUSIQ** (55.968 → 57.362) among individual modules, demonstrating its strong ability to preserve high-frequency details, particularly those poorly captured by 2D baselines.\n\n- The **mask uncertainty penalty** (3D-t) contributes the most to reducing **PI** among single components (5.071 → 4.934), indicating its effectiveness in suppressing occlusion artifacts and improving perceptual sharpness.\n\nWhen integrating all three modules into the complete 3D-TPR design (**3D full**), the results in the best overall performance across all metrics. This confirms that each module contributes uniquely, and their combination yields a **more perceptually robust and accurate interpolation pipeline**. These improvements are **statistically significant ($p < 0.05$)**, as verified by paired $t$-tests over all test samples.\n\n|**Method**|**PSNR ↑**|**SSIM ↑**|**LPIPS ↓**|**PI ↓**|**MUSIQ ↑**|**FLOLPIPS ↓**|\n|---|---|---|---|---|---|---|\n|2D|24.73|0.851|0.081|5.071|55.968|0.128|\n|3D-t-m|24.78|0.852|0.081|5.041|56.615|0.119|\n|3D-m|24.74|0.852|0.084|5.022|57.362|0.113|\n|3D-t|24.80|0.853|0.081|4.934|56.946|0.116|\n|3D (full)|**24.86**|**0.853**|**0.080**|**4.802**|**58.725**|**0.091**|\n\n- **W3 & Q3: Explanation of Key Terms:**\n\nVelocity Ambiguity and Fixed-timestep Map explanation see in W1 & Q1.\n\n**Justification for Geometric–Photometric Decoupling**\n\nOur decoupling of geometric and photometric characteristics is a central component for enabling cross-device generalization in zoom interpolation. Below, we clarify how this design removes device-dependent parameters:\n\n**Geometric decoupling**  \nOur spatial transition module operates entirely on *relative camera transformations*, using estimated intrinsics and extrinsics from the input wide and main images. It does not assume any actual device-specific parameters such as fixed baselines, known calibration, or distortion models. Thus, geometry is modeled purely through camera pose interpolation, independent of the device configuration. This ensures generalizability across devices with varying hardware setups.\n\n**Photometric decoupling**  \nInstead of relying on handcrafted calibration curves or device-specific tone mappings, our ColorNet module learns *content-adaptive color corrections* from the input pair. Each image is encoded into a latent embedding, and ColorNet predicts per-Gaussian color offsets to align appearance across views. This data-driven design allows the system to adaptively handle color differences without assuming any pre-defined device behavior.\n\n**Summary**  \nBy disentangling geometric transitions from hardware configuration, and photometric alignment from device-specific color priors, our framework avoids hardcoded assumptions and achieves **device-agnostic generalization** (see Table 2 and Figure 4). We will further clarify this rationale in the revised manuscript.\n\n[1] Clearer frames, anytime: Resolving velocity ambiguity in video frame interpolation.\n\n[2] The unreasonable effectiveness of deep features as a perceptual metric.\n\n[3] Flolpips: A bespoke video quality metric for frame interpolation."}, {"review_id": "cfNaLcsrRh", "reviewer": "Reviewer_qPjC", "summary": "The paper presents a method by which view interpolation can be tailored to better address the multi-camera view interpolation problem for small baseline capture devices.  In particular, this work focuses on extending existing feedforward models by proposing a replacement positional encoding scheme based on flow and depth information.  \n\nThis work also generalizes the data generation and data modeling approaches from ZoomGS to be applicable to more than just previously calibrated devices.  In particular, a color calibration network better models the variation in color calibration between lenses for better training data generation.  \n\nThe authors claim the following contributions:\n* Novel pipeline for ZI data generation\n* 3D-TPR - encodings and losses that can be inserted into sota ZI model frameworks\n* OmniZoom - the synthesis of these components into a common framework", "questions": "I need clarification on how the similarity map encoding is computed with 3D-TPR at time t if I_t is not known until after generation at test time.", "limitations": "The limitations are relegated to the appendices and are not substantially explored.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "+ The paper is quite well written.  The flow definitely relies on knowledge of ZoomGS early on and some exposition on how it relates would have been helpful.  In particular, I felt Figure 1 has difficulty standing on its own.\n\n+ Ablations are presented, albeit relegated to the appendix.\n\n= I think all the numbers don't do the task justice.  Clearly the qualitative results show reduced ghosting, but the numbers have very small differences.  We're maybe getting to the point where some additional statistical analysis is required to prove these results are significant.\n\n- The disparity variation vector's definition seemed arbitrary and alternatives were not compared in the paper.  \n\n- I'm having a difficult time understanding what I_t is at test time in order to give it as input to 3D-TPR.  This is necessary to compute V_{0->t} and Disp_t.  Perhaps V_{0->1} is scaled and Disp_0 and Disp_1 are interpolated using the flow field?\n\n- There's no ablation comparing ColorNet to simpler photometric calibration methods.", "was_revised": true, "final_justification": "My concerns have been addressed.  I will keep my rating of accept.", "rebuttal": "- **W1: Regarding the clarity of relation to ZoomGS and Figure 1.**  \n\nThank you for the helpful comment. While our data generation is inspired by ZoomGS’s use of 3DGS for intermediate frame synthesis, this influence is limited to **the use of 3DGS as a data generation tool**. Our framework is independently designed and introduces three novel components:  \n(1) spatial transition modeling,  (2) color adaptation, and  (3) cross-domain consistency learning,  which are absent in ZoomGS.\nFigure 1 illustrates our proposed pipeline. Its current version may not be fully self-explanatory. Due to NeurIPS rebuttal constraints, we will revise this figure in the camera-ready version with clearer module labels and updated captions.\n\n- **W2: On the significance of quantitative gains and the need for statistical analysis.**  \n\nWe thank the reviewer for the insightful suggestions. Due to page limits, ablation results were initially in the appendix. We appreciate your attention and will move expanded results to the main paper for better visibility and clarity in the camera-ready version.\n\nAlthough improvements on pixel-centric metrics such as PSNR and SSIM appear numerically modest, the qualitative results (e.g., reduced ghosting in Fig. 3-4) demonstrate **clear perceptual benefits**.  We argue that in most real-world applications, the goal of VFI  is not to produce pixel-aligned ground truth reconstructions, but to **synthesize plausible frames with high perceptual quality**. Furthermore, PSNR and SSIM are less sensitive to common VFI artifacts such as blur and ghosting [1], which our design explicitly targets by **resolving velocity ambiguity**. So, pixel-centric metrics are not always informative in evaluating perceptual quality.\nTo better assess perceptual quality, we evaluate three perceptual metrics: PI, MUSIQ, and FLOLPIPS [2], on Vimeo90K using EMA-VFI. These human-aligned metrics highlight each component’s strengths. Notably, **FLOLPIPS is specifically designed for video frame interpolation,** and it uniquely incorporates **motion consistency priors** via flow-guided weighting.\n\nAs shown below, our method consistently outperforms 2D baseline across all metrics.  \n**Importantly, our design is not a naive stacking of modules.** Each component is intentionally designed to target specific and well-known limitations in Zoom Interpolation: **motion ambiguity**, **texture degradation**, and **occlusion ambiguities**. Their effects are distinct, measurable, and complementary:\n\n- The **3D-TPR encoding** (3D-t-m) enhances motion consistency over the 2D baseline, reflected by a clear improvement in **FLOLPIPS** (0.128 → 0.119). This reduction suggests that incorporating 3D trajectory cues helps mitigate motion ambiguity and improves flow-aligned perceptual coherence.\n\n- The **texture-focus strategy** (3D-m) achieves the most significant drop in **FLOLPIPS** (0.128 → 0.113) and the largest gain in **MUSIQ** (55.968 → 57.362) among individual modules, demonstrating its strong ability to preserve high-frequency details, particularly those poorly captured by 2D baselines.\n\n- The **mask uncertainty penalty** (3D-t) contributes the most to reducing **PI** among single components (5.071 → 4.934), indicating its effectiveness in suppressing occlusion artifacts and improving perceptual sharpness.\n\nWhen integrating all three modules into the complete 3D-TPR design (**3D full**), the results in the best overall performance across all metrics. This confirms that each module contributes uniquely, and their combination yields a **more perceptually robust and accurate interpolation pipeline**.\n\n|**Method**|**PSNR ↑**|**SSIM ↑**|**LPIPS ↓**|**PI ↓**|**MUSIQ ↑**|**FLOLPIPS ↓**|\n|---|---|---|---|---|---|---|\n|2D|24.73|0.851|0.081|5.071|55.968|0.128|\n|3D-t-m|24.78|0.852|0.081|5.041|56.615|0.119|\n|3D-m|24.74|0.852|0.084|5.022|57.362|0.113|\n|3D-t|24.80|0.853|0.081|4.934|56.946|0.116|\n|3D (full)|**24.86**|**0.853**|**0.080**|**4.802**|**58.725**|**0.091**|\n\n- **W3: Regarding the rationale and alternatives of the disparity variation vector.**\n\nWe thank the reviewer for the thoughtful question. First, we clarify that the disparity variation vector $Var^{z} = \\Delta D_{0\\rightarrow t} / \\Delta D_{0\\rightarrow 1}$ is **not used during training**, nor does it directly affect training or inference. Its role is purely conceptual: to illustrate the notion of normalized motion progression in the depth dimension, complementing the 2D motion ratio $Var^{xy} = V\\^{xy-plane}\\_{0\\rightarrow t} / V\\^{xy-plane}\\_{0\\rightarrow 1}$  in the xy-plane. Our actual 3D-TPR computation does not explicitly use this vector. Instead, we compute cosine similarity between 3D displacement vectors constructed from estimated depth and flow.\n\nTo evaluate the robustness of disparity cues in our 3D-TPR encoding, we **ablated disparity map sources** – the critical factor for similarity computation. Using fixed EMAVFI backbone and RAFT warping, we compared our lightweight Lite-Mono against advanced DepthPro [3].\n\n|**Method**|**PSNR ↑**|**SSIM ↑**|**LPIPS ↓**|\n|---|---|---|---|\n|2D|24.73|0.851|0.081|\n|3D-TPR (Lite-Mono)|24.86|0.853|0.080|\n|3D-TPR (Depth-pro)|24.90|0.852|0.079|\n\n*Note that this ablation does not evaluate the disparity variation vector per se, but instead assesses the source disparity maps used in constructing the 3D trajectory vectors.* Vimeo90K results show both 3D-TPR versions consistently outperform the 2D baseline, confirming stable, generalizable motion priors regardless of disparity estimator. Despite sharper DepthPro outputs, its complexity yields only marginal gains. We adopt Lite-Mono for optimal training efficiency-quality trade-off.\n\n- **W4 & Q: Clarification on similarity map input at inference.**\n\nWe sincerely thank the reviewer for this thoughtful question.  \n**During training**, the similarity map (replacing fixed-timestep map) is derived from ground-truth frames using RAFT and Lite-Mono (see Equation 13).\n\n**During inference**, $I_t$ is not accessible, and the exact similarity map cannot be computed. Following the strategy used in prior work such as InterpAny [4], it is sufficient to provide a *uniform map* (mentioned in line 237 in the main text). In the same manner as conventional time-indexing methods, a fixed-timestep map at time $t$ can be used.  \nHowever, the semantics of this indexing map have shifted from an uncertain timestep map to a more **deterministic motion hint**. Physically, this encourages the model to move each object at constant speeds along their trajectories. In practice, this constant-speed assumption serves as **a valid approximation for smooth zoom scenarios commonly found in mobile photography.**  \n\nAs shown in Section 4, although this may lead to pixel-level misalignments relative to ground-truth, it still yields significantly improved perceptual quality. This suggests that the model, once trained with motion-aware 3D-TPR supervision, can robustly generalize to uniform motion priors during test time.  Indeed, this training-inference strategy is not unique to FI task, but for a wide range of machine learning problems. In some areas, researchers have come up with similar methods [5][6].\n\nWe will clarify this design choice in the revised version. Moreover, we have observed that if exact similarity map is used as input for inference(Appendix A.8), frame interpolation can yield gains. Therefore, we also aim to employ more lightweight methods in our subsequent work to obtain a coarse map to enhance performance. \n\n- **W5: Ablation on ColorNet.**\n\nThank you for the helpful suggestion. We conducted a no-reference image quality comparison between our ColorNet and common photometric calibration baselines:  \n(1) No Calibration,  (2) Mean-Std Normalization [7], and  (3) Reinhard Color Transfer [8].  \nEvaluations were performed on rendered intermediate frames using NIQE, PI, CLIP-IQA, and MUSIQ.\n\nAs shown below, **ColorNet consistently outperforms all baselines**, highlighting its advantage in modeling fine-grained photometric variations that simpler methods cannot capture. We will include these ablations in the revised manuscript to support our design choice.\n\n|**Method**|**NIQE ↓**|**PI ↓**|**CLIP-IQA ↑**|**MUSIQ ↑**|\n|---|---|---|---|---|\n|No Calibration|3.5832|3.8378|0.5193|68.4036|\n|Mean-Std Norm|3.3193|3.7757|0.4577|63.0220|\n|Reinhard Transfer|3.2357|3.7345|0.4588|64.3218|\n|**ColorNet (Ours)**|**2.8885**|**3.1515**|**0.6099**|**69.3328**|\n- **Limitations: Clarification on limitations' placement and content:**  \n\nWe thank the reviewer for pointing this out. Due to the page limit of the main paper, we included the limitations in Appendix A. We will integrate them into the main paper in the camera-ready version.\n\nOur main limitations are:  \n(1) while OmniZoom supports plug-and-play usage across different FI backbones, mild adjustments to training schedules may improve results in practice; and  (2) our dataset can be further enriched to improve robustness under extreme device variations.\n\nReference\n\n[1] The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition\n\n[2] Flolpips: A bespoke video quality metric for frame interpolation. \n\n[3] Depth pro: Sharp monocular metric depth in less than a second. In International Conference on Learning Representations.\n\n[4] Clearer frames, anytime: Resolving velocity ambiguity in video frame interpolation. In European Conference on Computer Vision.\n\n[5] Style tokens: Unsupervised style modeling, control and transfer in end-to-end speech synthesis, in International conference on machine learning.\n\n[6] Dmv3d: Denoising multi-view diffusion using 3d large reconstruction model.\n\n[7] Perceptual losses for real-time style transfer and super-resolution. In European Conference on Computer Vision.\n\n[8] Color transfer between images. IEEE Computer Graphics and Applications."}, {"review_id": "yCn2Pt3V1h", "reviewer": "Reviewer_Viu6", "summary": "Target at disparities in intrinsic/extrinsic parameters and different image processing pipelines, this paper proposes a cross-device virtual data generation method. Experimental results verify the effectiveness of the proposed method.", "questions": "1. Why Spatial transition modelling do not rely on device-specific assumptions? More explanation or visual illustrations should be given.\n2. For 3D-TPR framework, how to set the parameter t?\n3. From Fig. 2, the differences among the wide-angle image, the synthesized intermediate frame and the main camera image are not clear.\n4. From Fig. 3, 2D seems to achieve better performance than 3D-TPR for some small parts, or says that I cannot easily find the difference between them. The authors should give more explanations.\n5. How about the complexity and running speed of the proposed method? Can the proposed method be applied in reality directly?\n6. 3D-TPR uses RAFT to estimate 2D pixel-wise optical flows. How much impact will the quality of optical flow have on the experimental results? Is the time cost of optical flow estimation acceptable?", "limitations": "Yes. I think this method has no negative societal impact .", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. The paper is generally well-organised and easy to follow.\n2. The targeted problem is valuable, a plug-and-play paradigm for zoom interpolation is of practical value.\n3. This paper effectively articulates the issues associated with interpolation, providing a clear and comprehensive explanation from my point.\n4. A dataset is proposed by the authors, which can benefits the research community, and the experimental parts are thorough.\n\nWeaknesses\n1. The proposed modules in the paper are somewhat fragmented, and the innovations in each module should be strengthened.\n2. It is difficult to directly see the advantages of the method from the resulting images. The author could consider using more representative graphs or zooming in on local details.\n3. Some details of visual results can be enlarged to make them clearer, such as Fig. 3 and Fig. 4.\n4. A limitation part can be included in the paper to make this research more comprehensive.", "was_revised": true, "final_justification": "My concerns have been addressed in the rebuttal period, so I decided to raise my final rating to Borderline accept. I want to point out that the orgnaisation of the paper, esepicially the tables and figures should be refined in the final version.", "rebuttal": "- **W1: On the fragmentation and innovation of proposed modules**\n\nThank you for the insightful comment. While our framework involves multiple components, they are **tightly coupled** to address a core challenge in cross-device zoom interpolation: **(1)** the lack of high-quality, device-agnostic supervision, and **(2)** the inherent motion ambiguity in standard FI pipelines.\n\n**To address (1)**, we design a device-agnostic data generation pipeline with three coordinated modules. We enhance this process with **spatial transition modeling** and **dynamic color adaptation** to decouple geometric and photometric differences between devices. Furthermore, a **cross-domain consistency learning scheme** ensures the image maintains semantic alignment and photometric fidelity across devices. Together, these modules form a unified pipeline for generating high-quality, device-agnostic supervision. The key advantages of our data generation strategy are summarized in the table below:\n|**Method**|**Intermediate frame synthesis**|**Color transition (device-agnostic)**|**Spatial transition (device-agnostic)**|**Semantic consistency**|**Cross-device generalization**|\n|---|:---:|:---:|:---:|:---:|:---:|\n|3DGS|✓|✗|✗|✗|✗|\n|ZoomGS|✓|✗|✗|✗|✗|\n|**Ours**|✓|✓|✓|✓|✓|\n\n**To address (2)**, we propose 3D-TPR to enhance zoom interpolation's motion reasoning and perceptual quality, introducing a **3D trajectory encoder** for monocular-disparity-aware correspondence, a **texture-focus** loss emphasizing high-frequency details, and **mask uncertainty penalty** suppressing ambiguous predictions in occluded/low-confidence regions, collectively improving robustness and perceptual fidelity. The innovations of our strategies are shown in the following table.\n\n|**Method**|**1D temporal indexing**|**2D spatial indexing**|**3D depth indexing**|**Preserves texture details**|**Suppresses occlusion ambiguities**|\n|---|:---:|:---:|:---:|:---:|:---:|\n|Conventional FI|✓|✗|✗|✗|✗|\n|InterpAny|✓|✓|✗|✗|✗|\n|**3D-TPR (Ours)**|✓|✓|✓|✓|✓|\n- **W2 & W3: Clarification on visual advantages and figure improvements.**\n\nWe thank the reviewer for this valuable suggestion. We would like to clarify that the visual improvements, particularly ghosting reduction and structure preservation, are already evident in the current results. For example, in Fig. 4, our method produces noticeably clearer outputs in the Huawei power outlet, OPPO grass, and fine lines in the Redmi and iPhone samples. These perceptual gains are also supported by improved no-reference metrics in Table 4.\n\nWe agree that enlarging key regions would better highlight these improvements. Due to rebuttal constraints, we cannot update the figures here, but in the camera-ready version, we will revise Figures 3 and 4 by adding zoomed-in patches and more representative examples for clarity.\n\n- **W4: Regarding the presentation of limitations.**  \n\nThank you for the suggestion. Due to space constraints, we included our limitations in Appendix A.10. We agree that making them more visible improves transparency, and will move the key points into the main paper in the camera-ready version.\n\nOur main limitations are: (1) minor adjustments to training settings (e.g., batch size or loss weights) may be needed for different FI backbones; and (2) while our dataset spans multiple smartphones, it could be further expanded to handle more extreme device variations. That said, our method still generalizes well, as shown by its strong performance on unseen platforms.\n\n- **Q1: Regarding the device-agnostic nature of spatial transition modeling.**  \n\nWe thank the reviewer for raising this important point. Our spatial transition module is explicitly designed to be device-agnostic. It models zoom-related camera motion solely based on the **relative transformation between the wide and main cameras**, without relying on any actual device-specific assumptions such as known baselines, factory calibration data, or distortion priors. We estimate camera intrinsics and extrinsics via COLMAP and interpolate between the two estimated poses to construct a continuous trajectory. As shown in Table 2 and Figure 4, this design leads to strong cross-device generalization.\n\n- **Q2: Clarifying *t* in 3D-TPR framework.**  \n\nWe sincerely thank the reviewer for this thoughtful question. \n\n**During training**, `t = Sim(i, j, k), ∀ (i, j, k)`. `Sim(i, j, k)` is derived from ground-truth frames using RAFT and Lite-Mono (see Equation 13).\n\n**During inference**, the ground-truth frame is not accessible, the exact similarity map cannot be computed. Following the strategy used in prior work such as InterpAny, it is sufficient to provide a *t = fixed-timestep map* (mentioned in line 237 in main text) in the same manner as conventional time-indexing methods.  \nHowever, the semantics of this indexing map have shifted from an uncertain timestep map to a more **deterministic motion hint**. Physically, this encourages the model to move each object at constant speeds along their trajectories. In practice, this constant-speed assumption serves as **a valid approximation for smooth zoom scenarios commonly found in mobile photography.**  \n\nAs shown in Section 4, although this may lead to pixel-level misalignments relative to ground-truth, it still yields significantly improved perceptual quality. This suggests that the model, once trained with motion-aware 3D-TPR supervision, can robustly generalize to uniform motion priors during test time. Indeed, this training-inference strategy is not unique to FI task, but for a wide range of machine learning problems. In some areas, researchers have come up with similar methods [1][2]. We will clarify this design choice in the revised version. \n\nWe will clarify this design choice in the revised version. \n\n- **Q3: Regarding the clarity of Figure 2.**\n\nThank you for the observation. We agree that the transitions may not be immediately noticeable due to resolution and layout constraints in Figure 2. To clarify, one clear example is the first triplet from the Redmi row. For example, in the first Redmi triplet, the green plant in the background shows a gradual color shift, and the scarf’s position changes consistently across views, indicating a plausible transition path.\n\nIn the camera-ready version, we will enlarge key regions and add annotations in Figure 2 to better highlight these transitions.\n\n- **Q4: Clarifying visual differences between 2D and 3D-TPR in Fig. 3.**  \n\nWe appreciate this opportunity to clarify: while overall frames may appear similar, 3D-TPR consistently improves challenging regions (fine structures / motion boundaries / occlusions) where ghosting or blurring artifacts occur.\n\n**Fig. 3 examples:**\n\n- **RIFE (row 1):** 3D-TPR better preserves tree stripe patterns vs 2D blur.\n- **IFRNet (row 2):** More natural fingers in 3D-TPR vs 2D smearing.\n- **AMT-S (row 3):** Guitar strings remain sharp and aligned in 3D-TPR vs 2D misalignment.\n- **EMA-VFI (row 4):** Clearer bracelet reflections in 3D-TPR vs 2D detail loss.\n\nWe also provide additional examples in Appendix Fig. 14. In the revised version, we will clearly highlight these areas to better guide the reader’s attention. These localized enhancements reflect our 3D-aware design's strength, even when full-frame metrics may not show large differences.\n\n- **Q5: Complexity and Real-world Applicability**  \n\nThank you for this important point. Our method is **plug-and-play**: all components apply only during training. As shown in Q2, we revert to a uniform map at inference, introducing **no extra inference cost** and **preserving the base model's original inference speed/memory footprint**, ensuring full mobile/cloud compatibility.\n\n- **Q6: On the impact of optical flow quality on the experimental results.**  \n\nWe thank the reviewer for this valuable question. To analyze optical flow quality's impact on our 3D-TPR framework, we conduct a controlled ablation by downsampling RAFT inputs to different resolutions (1×/2×/4×/8×). As RAFT is resolution-sensitive, this setup offers an effective proxy for flow fidelity variation.  All estimated flows are bilinearly upsampled to original resolution for training.\n\nOn Vimeo90K with AMT and IFRNet, lower-resolution flows (4×/8×) lead to noticeable degradation (e.g., up to 1.2dB PSNR drop), while 2× performs nearly on par with full-resolution 1× but reduces memory and compute significantly. We thus adopt 2× as a practical efficiency and accuracy trade-off.\n\nImportantly, RAFT is used only during training, with an average cost of 27.5ms per image pair at 2×. No flow estimation is needed during inference. These results confirm 3D-TPR’s robustness: it maintains strong performance even under moderately degraded flow input.\n\n\n|Benchmarks|Metrics|IFRNet 1x|IFRNet 2x|IFRNet 4x|IFRNet 8x|AMT 1x|AMT 2x|AMT 4x|AMT 8x|\n|---|---|---|---|---|---|---|---|---|---|\n|Vimeo90k|PSNR ↑|27.21|27.21|26.01|25.01|27.25|27.22|26.01|24.94|\n||SSIM ↑|0.901|0.901|0.867|0.847|0.902|0.902|0.849|0.832|\n||LPIPS ↓|0.070|0.074|0.085|0.094|0.083|0.084|0.106|0.127|\n\n**Reference**\n\n[1] Y. Wang, D. Stanton, Y. Zhang, R.-S. Ryan, E. Battenberg, J. Shor, Y. Xiao, Y. Jia, F. Ren, and R. A. Saurous, “Style tokens: Unsupervised style modeling, control and transfer in end-to-end speech synthesis,” in International conference on machine learning. PMLR, 2018, pp.5180–5189.\n\n[2] Y. Xu, H. Tan, F. Luan, S. Bi, P. Wang, J. Li, Z. Shi, K. Sunkavalli, G. Wetzstein, Z. Xu et al., “Dmv3d: Denoising multi-view diffusion using 3d large reconstruction model,” arXiv preprint arXiv:2311.09217, 2023."}]

Author rebuttal:

Meta-review: The paper received mostly positive but divergent reviews (1 borderline reject, 2 borderline accepts, 1 accept).

The main concern from the borderline reject primarily centered around the paper's relation to InterpAny as well as the presentation of the paper. In the rebuttal the authors directly addressed this concern noting that InterpAny's reliance on 2D optical flow can lead to difficulties with 3D scene motion and that their 3D trajectories directly attempt to overcome these problems. They additionally discuss the gradient-coupled texture-focus strategy and a mask uncertainty penalty as novelties wrt InterpAny. The other reviewers were mostly positive, noting the promising results as well as overall organization. Though it seems like there is certainly room for improvement with respect to things like the ablation studies as well as understanding how each module interacts with each other.

After reading the review, rebuttal, and discussion, I advocate for acceptance but encourage the authors to address the presentation issues identified by Reviewer Viu6.
Decision: Accept (poster)