Arbitrary Generative Video Interpolation
Authors: Guozhen Zhang
Haiguang Wang
Chunyu Wang
Yuan Zhou
Qinglin Lu
Limin Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Video Frame Interpolation
ROPE
Video Generation
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A novel generative VFI paradigm that enables interpolation at any timestamp and of any length.

Abstract: Generative Video Frame Interpolation (VFI), which synthesizes intermediate frames from a given pair of start and end frames, plays a pivotal role in video creation. However, existing generative VFI methods are constrained to producing a fixed number of intermediate frames, which significantly limits the flexibility in adjusting the frame rate or duration of videos during the creation process. In this work, we present \textbf{ArbInterp}, a novel generative VFI framework that enables efficient interpolation at any timestamp and of any length. Specifically, to support interpolation at any timestamp, we propose the Timestamp-aware Rotary Position Embedding (TaRoPE), which modulates positions in temporal RoPE to align generated frames with target normalized timestamps. This design enables fine-grained control over frame timestamps, addressing the inflexibility of fixed-position paradigms in prior work. For any-length interpolation, we decompose long-sequence generation into segment-wise frame synthesis. We further design a novel appearance-motion decoupled conditioning strategy: it leverages prior segment endpoints to enforce appearance consistency and temporal semantics to maintain motion coherence, ensuring seamless spatiotemporal transitions across segments. Experimentally, we build comprehensive benchmarks for multi-scale frame interpolation (2× to 32×) to assess generalizability across arbitrary interpolation factors. Results show that ArbInterp outperforms prior methods across all scenarios with higher fidelity and more seamless spatiotemporal continuity. Video demos are provided on the website: https://mcg-nju.github.io/ArbInterp-Web.

Published as a conference paper at ICLR 2026
ARBITRARY GENERATIVE VIDEO INTERPOLATION
Guozhen Zhang- Haiguang Wangl Chunyu Wang² Yuan Zhou
Qinglin Lu
Limin Wang
'State Key Laboratory for Novel Software Technology, Nanjing University
Teenentt Humyuan Shhnnhai AI Laboratory
z zgzaacm@gmaill com Imwang@nju. www.nngenneeuuu.... edu. en
ABSTRACT
Generative Video Frame Interpolation (VFI), which synthesizes intermediate frames from a given pair of start and end frames, plays a pivotal role in video creation. However, existing generative VFI methods are constrained to producing a fixed number of intermediate frames, which significantly limits the flexibility in ad- justing the frame rate or duration of videos during the creation process. In this work, we present ArbInterp, a novel generative VFI framework that enables efficient in- terpolation at any timestamp and of any length. Specifically, to support interpolation at any timestamp, we propose the Timeestmp-awaae Rotary Position Embedding (TaRoPE), which modulates positions in temporal RoPE to align generated frames with target normalized timestamps. This design enables fine-grained control over frame timestamps, addressing the inflexibility of exed-position paradigms in prior work. For any-length interpolation, we decompose uengseeuencce generation into segment-wise frame synthesis. We further design a novel sepearance-motine de- coupled conditioning strategy: it leverages prior segment endpoints enforce appearance consistency and temporal semantics to maintain motion coherence, ensuring seamless spatiotemporal transitions across segments. Experimentallyy we build comprehensive benchmarks for multi-scale frame interpolation (2x to 32x) to assess generalizability across arbitrary interpolation factors. Results show that ArbInterp outperforms prior methods across all scenarios with higher fidelity and more seamless spatiotemporal continuity. Video demos are provided on the website: https: //mcq htt.ennj.uuuuuuuu.. github. poo/AbInnterry /elb/
INTRODUCTION
With the advancements enabled by large-scale pretraining, text-to-video and image-to-video gen- eration models (Kong et al. 2024; Wang et al., 2025 Yang et al. 2024; Chen et al. 2025) Zhou eet al. 2024) have demonstrated remarkable prowess synthesizing realistic videos for complex scenes, enabling a wide spectrum of downstream applications. Among these, generative Video Frame Interpolation (VFl)-the task of generating coherent intermediate frames from given start and end frames-remains a fundamental and widely used function video creation. Recent work has explored adapting pretrained video generation models for generative VFI, such as leveraging image-to-vides models to produce high-quality interpolations conditioned on start and end frames (Wang et al. 2024
Feng et al. 2024 Xing et al. 2024b Zhang et al., 2025 Xing et al., 2024a).
However, all existing works strictly adhere to the fixed interpolation paradigm, as shown in Figure I where predetermined number of intermediate frames is generated from given start and end frames. This paradigm inherently hinders practical flexibility, as it precludes users from dynamically adjusting frame counts or frame rates (FPS) to meet specific needs. Moreover, unlike other conditional tasks (c.g., video prediction), VFI imposes unique requirements: it not only demands temporal consistency between intermediate and input frames but also requires fine-grained semantic understanding of input frames to generate plausible in in-between frames. The fixed-frame paradigm, however, fails to fully model continuous motion dynamics- this is because it only accommodates fixed-fri frame-rate inputs.
'Work is done during internship Tencent Hunyuan.
Guuzzhnn Zhang and Haiguang Wang contributed equally to this work.
1 'Corresponding author.

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Published as a conference paper at ICLR 2026
Noiay Luizet Condition Latent
Fixed RoPE
TaRoPE
Langth
Any Lengli
DiT
DiT
(a) Previous methods
(b) Ours
Figure 1: A comparison between the fixed interpolation paradigm (a) and our proposed ArbInterp (b). Arbinterp enables flexible control of the temporal positions of generated intermediate frames by specifying any timestamps between 0 and I.
This limitation thereby restricts the model's ' capacity to reason about coherent motion fields and synthesize smooth spatiotemporal transitions.
To overcome the aforementioned bottleneck. we introduce ArbInterp, a novel generative frame interpolation paradigm that enables frame generation specified at any timestamps and of any length, as illustrated in Figure (b). Specifically, by defining the start frame at timestamp t t= 0 and the end frame at & 1, ArbInterp aims to synthesize intermediate frames for any target timestamp t within this interval. This design allows flexible control over the temporal position of generated frames: for example,
[0, 0.5, 1] generates single middle frame for 2x interpolation, while t t= [0, 0.25, 0.5, 0.75, 1] achieves 4x interpolation. What's 's more, by sampling only small set of timestamps each time rather than requiring full video sequences, our approach substantially reduces computational costs while enhancing the generative model's fine-grained temporal awareness.
The key challenge lies in effectively injecting timestamp information into the generative model. To achieve this, we propose T'mestamp-awes Rotary Position Embedding (TaRoPE), built on the insight that in existing DiT-based (Peebles & Xie 2023) video generative models, each frame determines its relative position within the video sequence exclusively through temporal RoPE İZhao et al. 2025). curreniframe frume perceives its actual method allows
This means that adjusting the temporal RoPE directly alters how the current
position in the sequence. By adapting the temporal RoPE to target timestamps, the model to perceive custom positions without introducing additional parameters. In practice, this requires only minimal fine-tuning transfer pre-trained generative models the frame interpolation task, demonstrating remarkable efficiency and generality.
Theoretically, treating the interval between start and end frames continuous motion field enables our approach to generate an infinite number of interpolations. Moreover, by specifying timestamps be decomposed into sequential segments. For example,
incrementally. long-term frame generation a long video interpolation with timestamps [0, 0.01, 0.02.... 0.99, 1] can be divided into multiple segments, such as [0, 0.01...., 0.10, 1]. [0, 0.11. ... 0.20, 1]. and so on. However, the inherent stochasticity of generative models can cause discontinuities in motion and appearance across segments. To address this, we further introduce a motion aapeoraae decoupling conditioning strategy to enhance spatiotemporal continuity between segments. Specifically, we feed the last frame of the previous segment as a conditioning input to preserve appearance consistency and inject motion information extracted from the last N frames into the DiT forward process to maintain motion coherence. This dual injection mechanism significantly enhances cross-segment consistency, enabling high-fidelity infinite frame interpolation with seamless transitions in both visual appearance and motion dynamics.
Experimentally, we validate the effectiveness of the ArbInterp framework on the recently open- sourced video generation model of Wan (Wang et al. 2025). We find that fine-tuning for only 20,000 steps using GPUs (96GB) is sufficient to achieve our goals. To rigorously assess generalizabilityy construct comprehensive benchmarks spanning multi-scale frame interpolation tasks (e.g., 2x, 8x, 16x, and 32x interpolation). Experimental results show that ArbInterp outperforms prior methods across all tested scenarios: it achieves higher fidelity and seamless spatiotemporal continuity in different interpolation ratios from 2x to 32x. These results underscore our framework's 's superiority in balancing flexibility and generative quality for real-world VFI.
Contributions. In summary, the contributions of this paper are as follows:
We propose a novel generative frame interpolation paradigm, ArbInterp, which can control the generated frames by specifying any continuous timestamps. By integrating timestamp
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controllability into generative modeling, our approach demonstrates superior flexibility and ability to model continuous dynamics.
To achieve long-term frame interpolation, we design a novel motion-appeaarnce decoupling strategy to efficiently enhance the spatiotemporal continuity between adjacent segments.
We meticulously constructed comprehensive benchmarks encompassing multi-scale frame interpolation. Experimental results demonstrate that ArbInterp significantly outperforms previous models in both flexibility and performance.
2
RELATED WORK
2.1 VIDEO FRAME INTERPOLATION
Current video frame interpolation methods are broadly categorized into deterministic and generative approaches. Deterministic methods (Huang al. 2022, Zhang et al. 2023 Reda et al. (022). dominated by flow-based frameworks, typically predict intermediate optical flows between input frames and synthesize intermediate frames via warping operations. In these works, timestamps are typically integrated via optical flow scale factors or fed into networks as feature. However, constrained by limited data scales and model capacities, these methods struggle accurately model motions in complex scenes, In contrast, generative models (Zhang et al. 2025: Jain et al. 2024 Feng
al. 2024 Wang al. 2024] Xing al. ( generation models adapted for interpolation tasks exhibit superior generalization in challenging scenarios. These generative strategies can be further classified into two paradigms: (1) leveraging image-to-video conditional models (e.g., SVD) to generate videos conditioned on start and end frames separately and then merge the results into one video; and (2) latent-space conditioning methods, which integrate input frame information by concatenating or replacing latent codes and fine-tune models to guide intermediate frame synthesis. Despite their advancements, both categories adhere to ixed-interpolation paradigm, restricting output to predefined number of frames with uniform temporal spacing. In contrast, our method, ArbInterp, overcomes these limitations by enabling the generation of frames at arbitrary continuous timestamps between input frames.
those leveraging pre-trained video
2.2 ROPE IN VIDEO GENERATION
Rotary Position Embedding (RoPE) (Su al. 2024) has become the prevailing approach in con- temporary DiT-based video generative models (Zhuo et al. 2024 Wang et al. 2025 Yang et al.) 2024), thanks to its precisely defined relative position information and outstanding performance. In most scenarios, the temporal position of each frame is simply its position within the current clip. For long-term generation, some methods endow frames with their true indices throughout the entire video (Guo et al.) 2025 Zhang & Agrawala (025; Wu et al.) (2024] Li et al. (2025] 2026). A recent study, RIFLEx (Zhao et al. 2025), demonstrates effective training- free length extrapolation by adjusting the intrinsic frequency in ROPE, highlighting the significance of RoPE in the temporal sequencing of generated videos. In this work, we introduce Timestamp-awant assigns each frame a continuous timestamp within the range of 0 to 1 as its temporal position, rather than its index. This innovative design empowers the model to capture motions between input frames with infinite fine-grained precision and generate any frame at any timestamp.
RoPE (TaRoPE), which
2.3 LONG-TERM VIDEO GENERATION
Current approaches for long-vide video generation can be categorized into two distinct types. The first type involves decomposing long video generation into the generation of multiple consecutive short segments (Henschel et al. 2024 Kim et al., 2024, Yin al.) 2024 Weng et al. 2024, Zhang & Agrawala 2025) Villegas et al. (022). The mainstream of such methods employs an autoregressive manner akin to frame-by-frame or segment-by by-eemen generation, with the key challenge lying in ensuring overall coherence and quality. For example, the recent work FramePack (Zhang & Agrawala 2025) balances efficiency and performance by compressing the tokens of historical frames by manual strategy. The second type first generates anchor frames and then generates videos segmentally. Nuwa- XL Yin et al., 2023), for instance, adopts a divide-and -n---conduueerrrre approach to achieve coarse-to- fine video generation. Notably, our proposed ArbInterp can seamlessly accommodate both of these two
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0
Start End Frame
Indices 14
Timestamps
0.7
TaRoPE
Previous Frames
16
0.8
Prefix Frame (w/ w/o)
0.9
1
Stage Training
Scale & Shitt
Scale Shift
Selt Attn
Cross Attn
MLP
DIT block
x
steps
Pre-motion Tokens
In-clip-motion Tokens
Motion Semantic
Motion
Semantic
Extractor
Extractor
Stage 2 Training
Overall architecture of ArbInterp. Our framework enables arbitrary- interpo- lation with continuous timestamps using Timestep-aware Rotury Position Embedding (TaROPE). Additionally, we introduce appearance-motion decoupling conditioning strategy to enhance the performance of long-term interpolation. This strategy ensures appearance consistency via prefix frame guidance and enforces motion continuity through motion tokens.
Figure 2:
designs, thus enabling effective and stable long-term video frame interpolation. Meanwhile, we further introduce novel appearance-motion decoupled strategy to explicitly disentangle content and motion information, which effectively improves the spatiotemporal coherence between consecutive segments in long-term interpolation scenarios.
METHOD
3.1 ARBINTERP
Given the first frame 26 and the last frame æ1, the goal of video frame interpolation is to generate intermediate frames. Previous works on generative frame interpolation (Wang et al., 2024 Feng et al. 2024 Xing et al., 2024b) can only produce a fixed number of frames, lacking flexibility and ability to model continuous motion between frames a fine-grate ane-grained level. To address these limitations, we propose novel framework, ArbInterp, which can generate intermediate frames at arbitrary temporal positions specified by continuous timestamps. Formally, given timestamp list
T = [0, (,...,, fn, and input frames æn and æı ArbInterp generates corresponding intermediate frames as follows:
t. = ArbInterp (æo, 11, T),
(I)
where &ę can be any value between 0 and To leverage the powerful generative capabilities of pre- trained models, we build Arbinterp upon the recent open-source video generation model Wan (Wangl 2025). As shown in Figure 2 ArbInterp introduces two novel designs. First, we enable the denoising network to generate frames corresponding to specific timestamps by introducing Timestamp-aware Rotary Position Embedding (TaRoPE), achieving fine-grained temporal modeling capabilities, detailed in Section 3.3 Subsequently, to enhance the quality of long-term video interpolation, we design an appearance-motion decoupling conditioning strategy to strengthen the spatio-temporal coherence across different segments, explained in Section 3.4, Based on these two designs, ArbInterp supports generating frames of arbitrary length at any continuous timestamps.
3.2 TRANSFERRING VIDEO GENERATION MODELS FRAME INTERPOLATION
For video σ, Wan Wang et al. 2025 first performs opatio-tempora compression via tok- enizer (Kingma & Welling 2022) to obtain video latents z. During training, for any sampled timestep [0, 1J, Wan adds Gaussian noise et (O0 to produce noisy latents z", and trains
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0
6
62
FA-1 6x
fa-Je1 FA
Exz
4
Arbinterrp
Arbtnterp
Arbintarp
Arbantrrp
Aetentrr
Arbisterp
(a) Direct Interp.
(b) Segment-by- by-Segmen Interp.
(c) Hierarchical Interp.
Figure 3: Different interpolation strategies in ArbInterp. ArbInterp supports multiple interpolation strategies: (a) Direct Interpolation for short-range interpolation, and two for long-term scenarios: (b) Segment-by-Segmen Interpolation and (c) Hierarchical Interpolation.
denoising network using the flow matching framework (Lipman et al. 2023
Liu et al. 2023
v, ug(2", n, 1) where V,,
2,
where y denotes additional conditional information such text, and up represents the denoising network. The denoising network is composed of L diffusion transformer blocks, each encompassing Adaptive Layer Normalization (AdaLN) (Peebles & Xie, 2023), self-attention, cross-attention, and a multi-layer perceptron (MLP). Self-attention facilitates information exchange within the video, while cross-attention incorporates auxiliary conditional information. To adapt Wan to the frame interpolation task, we adopt the token replace method in Open-Sora (Zheng et al. (2024), substituting the noisy latents of the first and last frames with the ground-truth latents guide the prediction of intermediate frames. Additionally. to simplify the correspondence between each latent and its timestamp, we perform only spatial compression during tokenization. Since our method does not require predicting all frames simultaneously, the training cost remains computationally feasible.
3.3
TAMESTAMP-AWARE ROPE
3.3.1
DEFINITION
To distinguish the spatio-temporal positions of each token, Wan applies 3D RoPE (Su al. 2024 each token in z. Specifically, Wan partitions the channels of each token into three equal parts and applies RoPE for each dimension respectively. Since our work primarily focuses on temporal RoPE, we omit spatial RoPE in the following discussion. For the k-th latent z in the given video latent the original temporal RoPE rotates it by 0g, as shown in Equation 3
Vanilla Temporal RoPE.
b RoPE( k) 20
0% k-
By applying temporal RoPE to both query (Q) and key (K) self-attention, the attention score between any two latents at indices & and j is influenced by their relative positions:
Ak.j Re 1 Re((2 2z)e (k-))(ns
(4)
Notably, temporal RoPE is the sole component in the denoising model that enables each frame's latent to perceive its temporal position, as all other operations pogision-agnostic. This implies that altering the temporal RoPE assigned to each frame can modify its temporal position in the final generated sequence.
Introduction of Timestamp. In the default temporal RoPE, each latent's temporal position is represented by an absolute index, meaning the range of [k - is fixed and always integer-valued when training with a fixed sequence length. For frame interpolation tasks, this leads the model to over-rely on latents at specific fixed positions. For example, when trained on 16 frames, the model tends to depend on the latents at positions 0 and 15, as these frames provide the actual conditional information. This rigid dependency restricts the model's 's ability to generalize to sequences of varying lengths. To address this issue, propose Timestamp-aware RoPE (TaRoPE), which specifies the temporal position of frames using continuous timestamps. Instead of using the latent's position in the input sequence, adopt its real relative position between the first and last frames as the
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saur Fead frames
maly fianes
previous
Appearance Flow
franz
Croso
Cross
Semantic Extractor
Cross
MLP
%1
HLP
X
Motion
HLP
Tocara
(8) Latent Cond.
(b) Cross-atto Cond.
(c)
Figure 4: Comparison of different conditioning strategies: (a) direct latent conditioning, (b) cross- attention conditioning, and (c) our proposed appearance- motion decoupling conditioning strategy.
timestamp for temporal RoPE. For a sampled video with N frames, the timestamp tx of the k-th frame is computed as:
tk = N - SLICKSN.
(5)
As illustrated in Figure 2 the timestamp of the first frame is fixed at O, and that of the last frame 1. This approach normalizes frame interpolation for videos of arbitrary lengths into a continuous interval from 0 to 1. Consequently, the network learns utilize information from latents at timestamps 0 and 1 to model the continuous motion field in a length-ine invariant manner.
3.3.2 TRAINING AND INFERENCE
To fully train the model's sensitivity to continuous timestamps, we adopt a segment- wise training strategy. During each sampling step, the model only predicts segment of the complete video sequence. In practice, because TaRoPE treats videos of arbitrary length as continuous segments within J0, 1], we can fully train timestamps of varying granularities during training. For example, for 200-frame video, we set the first and last frames as timestamps O and 1. When predicting frames 100 and 101, the input timestamp list becomes [0, 1/2, /20, 1]. As described in the appendix, our training involves predicting to 19 intermediate frames, with maximum interval of 2 seconds between the first and last frames. Given that the training includes videos with frame rates ranging from 30fps to 120fps, the training timestamps naturally cover values from 1/2 to 1/240. Through this approach, the denoising network can effectively learn generate corresponding frames from continuous timestamps. Concurrently, training costs are significantly reduced as there is no need to process entire video sequences.
In inference, timestamps are always normalized to [0, 1), regardless of test length. For example, pre- dicting [0, 0.2, 0.4, 0.6, 0.8, can be decomposed into [0, 0.2, 0.6, 1] then [0.6, 0.8, 1]. ensuring test the design described above, Arbinterp
sequences never exceed the training maximum. Building supports multiple adaptive inference strategies to accommodate varying sequence lengths, as shown in Figure For short sequences, employ direct interpolation, generating the entire interpo- lated sequence in a single forward pass. For longer sequences with high computational demands, interpolation, where target timestamps partitioned into non-overlapping segments processed sequentially; and hierarchical interpolation, which first predicts sparse anchor frames at coarse temporal intervals then refines the sequence by interpolating between these anchors. Hierarchical interpolation can better orchestrate global motion trajectories. However, segment-by by-semeet interpolation offers stronger real sal-time responsiveness in latency-sensitive scenarios (e.g., gaming). Both strategies reduce the computational complexity in self-attention from O(N2) to O
introduce two approaches: segment-by-segmeet
NZ
when dividing a sequence of length N into M segments.
M
3.4 APPEARANCE-MOTION DECOUPLING CONDITIONING
Despite the effectiveness of segment-wist wise inference in ArbInterp for handling long-term interpola- tion, the stochastic nature of generative models introduces spatio-temporal inconsistencies between adjacent segments due to the indeterminate motion patterns between input frames. To address this challenge, specifically,
objective is to ensure spatio-temporal coherence between the current segment s; and its preceding segment $;-1 by leveraging information from s;-1. A straightforward solution, shown in Figure 4 (), is concatenate the latent of 8;-1 directly into the input. Al-

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Table 1: Quantitative comparison with the state-of-the-art methods on MultinnterpBench. The boldfaced and underlined colors indicate the best and second best performing methods, respectively. i indicates higher is better, & indicates lower is better (applied to individual metrics in the header).
VDlench Metrics
Interp. Rate
FVD (1)
LPIPS (1)
CLiP <f)
Method
(1)
Suryec
Tragpral
Mirice
Antheriz
Traging
Ovresll
Cazua.
Ptak.
Smootts
Quality
Quality
Anarage
LOMNI
85.8
0.297
0.863
0,9212
0.9198
0.9120
0.9403
0.4752
0.5901
0.7928
TRF
108.5
0.435
0.879
0.8958
0,9060
0,8714
0.3744
I.4736
0.6224
0.7739
Gl
2x
90.8
0.496
0.893
0.9202
0.9156
0.8495
0.8527
0.4755
0/6522
0.728
mofhrr
K1.6
0.249
0.877
0.9228
09219
0.9199
0.357
0.4829
0.614
0.7956
Arhlnterp SVD
59.1
0.152
0.912
0.9294
0.9473
0.9211
0.9475
1.4925
0.6314
03114
Athlitzsp
44.9
0.176
0.913
0.9890
0.9719
0,9333
0.9644
1150027
0,632
0.3286
LOMVE
62.2
0.232
0.351
0.9198
0.9311
0.9369
0.9699
0.4725
0.0056
0.8035
TRF
90.0
0.40
0.877
0.8074
0,9335
0.9800
05378
014551
0,5952
0.7225
GI
Xx
62.5
0.505
0.387
0,9190
0.946
0.117
05264
IL4584
06118
0.7933
InffemiCrafier
51.8
0.282
0.576
0,9246
0.922
09987
0.646
0.4201
0,6199
0.006
Arblurery SVD
10.6
0.9497
0.411
0.5055
03193
Atblatero
33,0
0
03485
0.9499
0.3144
49.6
397.2
0.281
0.997
0.930
09342
0.4477
0.700
0.4681
0.4057
0
530.4
0.407
0.571
0,8997
09955
09331
0968
0,5800
16s
534.2
09335
0056
0
DyttmmCraaheer
968.5
09515
Arhlstop-SVD
Arhinterp
25.4
211.3
8.945
8.553
0.9877
0,5077
0.334
753.6
1011.5
0.875
09132
09274
09528
0,453
0.5582
0.7901
32x
54.3
956.7
0.557
09106
09921
0980
0,556
0.7855
DynamiCratter
46.4
732.7
0.374
0.8955
0.934
0.9538
05750
0.756
0.0009
0.0050
Arhlntrey-SVD
81.8
400.3
0.174
097
Abbattrr
36,5
319.9
0.145
0.965
0.9441
0.9628
0.9624
0.8877
0.5023
0.6411
0.533
though effective, this method significantly increases computational overhead during both training and inference. Alternatively, another simple approach is to inject prior segment information through cross-attention (Figure({(b) While efficient, this approach yields weaker appearance consistency compared to direct latent concatenation (Zhang al. 2024).
To balance efficiency and performance, we introduce the appearance-motion decoupled conditioning strategy, as shown in Figure 4c.. For appearance coherence, we use the last frame of 8 prefix frame in the input to guide the generation of 8,. This minimal modification ensures visual continuity while incurring an acceptable computational cost. For motion coherence, inspired by the fact that modern generative models can control video motion using text prompts (c.g.. "rotatitni movement"). ) we extract semantic-level motion tokens from the last N frames of s using a Motion Semantic Extractor (MSE) to regulate the motion of the current segment. The MSE first employs a temporally enhanced CLIP model (Radford et al., 2021b) to extract spatio-temporal features aligned with the text semantic space. These features are then compressed into fixed number of motion tokens via the Q-Former (Li et al. 2023a) to reduce redundancy. We enhance the temporal modeling of CLIP by adapting last L layers of the original CLIP into ppatioltemporal full attention augmented with temporal embeddings. Additionally, we also extract in-clip motion tokens from the start and end frames of 8; using shared MSE, enabling the model to better align generated motions with input constraints. Both types of motion tokens are injected into the network through the original cross-attention that interact with text prompts. When the cross-aftention parameters are frozen, the MSE would learn to extract semantic information capable of controlling video motion, ensuring consistent motion dynamics across segments. More structure details and ablations of the MSE are provided in the supplementary materials.
EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
We initialize ArbInterp based on the latest open-source video generation model Wan2.1-T22 1.3B (Wang et al.) 2025). Our training dataset comprises 50,000 videos meticulously curated from Vid (Nan et al. 2024). ArbInterp is trained on 8 GPUs (96GB) with a total batch size of 16. Inspired by He et al. (2025), we divide the training into three stages: In the first stage, we transfer the generation model to the frame interpolation task and finetune all parameters of DiT. The input includes start and end frames, random number of intermediate frames to be predicted, and an optional prefix frame to ensure spatial consistency across different segments during testing. In the second stage, we freeze the denoising network and train the motion semantic extractor individually to
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Start/End Frames
LDMVFI
DynamiCrafter
TRF
ArbInterp (Ours)
Figure 5: Visual comparison. The timestamps of the intermediate frames 0.25, 0.5, and 0.75, respectively. ArbInterp demonstrates significant advantages in stability and consistency.
Table 2: Ablation study key designs of ArbInterp.
Timestamp injection
Spatiotemporal continuity
Step-time (5)
FID
FVD
Subject Concist.
Background Consist.
Temporal
Motion
Aestietic
Imaging
Quality
Quality
None
NA N/A NA Cond.
2.3
38.7
476.8
0.9193
0.9402
0.9415
0.9620
0.4833
0.6299
Vanilla
2.3
35.2
454.2
0.9210
0.9439
0.9637
0.4852
0.6298
TaRoPE
2.3
33.7
401.6
0.9272
0.9519
0.9517
0.9707
0.4859
0.6296
TaRoPE
TaRoPE
Cross altn Cond.
2.7
28.4
342.1
0.9381
0.9582
0.9584
0.9792
0.4935
0.6358
TaRoPE
Motion
2.4
354.0
0.9376
0.9549
0.9764
TaRoPE
wlo Appearance
2.3
31.7
382.7
0.9297
0.9533
0.9592
0.9803
0.4902
0.6331
TaRoPE
Ours
2.5
26.5
319.9
0.9441
0.9628
0.9624
0.9817
0.5023
0.6411
learn the ability extract motion semantics. The motion semantic extractor takes up to 8 preceding frames as input. In the third stage, we train the entire model. The three stages are trained for 10k, 5k, and 5k steps, respectively. More implementation details are provided in the supplementary materials.
4.2 MULTIINTERPBENCH
To evaluate the flexibility and coherence in video interpolation at different interpolation ratios, we propose new benchmark: MultilnierpBench. MultilnterpBench covers interpolation ratios of 2x, 8x, 16x, 32x. For the first three ratios, ArbInterp employs direct interpolation in Figure aa), while for 32x, we adopt the strategy in Figure combined with our proposed appearance-motion decoupling conditioning. The metrics include: (1) image-level metrics including LPIPS (Zhang et al.
2018), FID (Heusel et al. (017). and CLIP similarity score (CLIPimg) img) (Radford et al. 202 la);
video-level metrics including FVD (Unterthiner et al. 2018 2019 and VBench (Huang et al. 2024
4.3 COMPARISON WITH THE STATE-OF-THE-AAE METHODS
We compare ArbInterp with state-of-the generative frame interpolation methods: LDMVFI (Danier al. 2024), DynamiCrafter (Xing et al.) 2024b), TRF (Feng et al., 2024). and GI (Wang Due to their inability to flexibly generate frames at arbitrary timestamps, we can only obtain their results through approximate strategies. Specifically, when exceeding their default frame counts, we use an iterative prediction strategy similar to Figure when below, we use uniform sampling to select frames. Since most baselines are based on SVD-like architectures, we also trained a variant of ArbInterp based SVD (lattmann et al. 2023) (denoted as ArbInterp-SVDD for fair comparison. Unlike the DiT architecture, we interpolate the absolute positional encoding in SVD to enable generation at any timestamp.
2024).
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Segmenti1
Segmenti22
(b)
Figure 6: Visual comparison of appearance- decoupling conditioning strategies. (a) is produced by direct begment-by-segment prediction. (b) is the result with our proposed strategy.
1-33
Figure Visualization of independently predicted intermediate frames different timestamps.
Quantitative comparison. As shown in Table I benefiting from the flexibility and fine-grained timestamp awareness brought by TaRoPE, our model significantly outperforms previous methods across different interpolation ratios. Meanwhile, the performance superiority at 32x further demon- strates the advantage of ArbInterp in long-term interpolation.
Qualitative comparison. As shown in Figure 5 compared to previous methods, method can generate smooth and continuous intermediate frames solely by specifying timestamps, fully showcasing the flexibility and performance advantages of ArbInterp.
4.4 METHOО EXPLORATION
Ablation study. As presented in Table 2 we conduct ablation studies the core components of Arbinterp via performance analysis under 32x interpolation ratio. For timestamp incorporation, we compare baselines: direct fine-tuning without timestamp awareness (None) and MLP-based injection (Vanilla). Mcanwhile, we compare different strategies in Figure 4 and perform separate ablations on motion and appearance components, as shown rows ID 4-8 of the table. Based on the ablation results, we can derive two key insights: (1) The TaRoPE approach outperforms the Vanilla baseline across all metrics, with a particularly pronounced improvement in motion smoothness. (2) Incorporating motion information yields substantial enhancements in temporal flicker and motion smoothness, while integrating appearance information significantly boosts subject and background consistency. In Figure 6 we further present visual comparison, demonstrating the improvement in spatiotemporal coherence achieved by our proposed strategy. What's 's more, during inference, compared to latent concatenation, strategy not only delivers better performance but also significantly improves computational efficiency by approximately 40%.
Evaluation the capability of arbitrary-time interpolation. To demonstrate that ArbInterp equipped with the proposed TaRoPE is capable of directly generating intermediate frames cor- responding to arbitrary continuous timestamps, we conduct an evaluation where intermediate frames independently predicted various continuous timestamps, with visual results presented in Fig.

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Anchoet0
Anchork1
Anchore2
DynamiCrafter
TRF
GI
Arbinterp (Ours)
Figure Visual comparison streaming frame interpolation scenarios.
From the experimental results, it can be clearly observed that ArbInterp effectively captures the smooth, continuous motion evolution between the initial and final reference frames. By leveraging the temporal modeling capability introduced by TaRoPE, the model accurately infers the interme- diate visual content and faithfully synthesizes realistic intermediate frames strictly aligned with the user-specified continuous timestamps. These results validate that our method achieves reliable Abbirrary-rram interpolation without being restricted to discrete or equidistant intermediate steps.
4.5 EXTENSION TO STREAMING FRAME INTERPOLATION
The proposed design can naturally be extended to streaming frame interpolation scenarios. Specif- ically, for a sequence of consecutive input frame pairs { x æ x;-1), we regard the frames from the preceding generated interpolation of (x) 1-1 x as the previous segment to aid decoupled
the interpolation generation of (wf ) By integrating our proposed appearance- conditioning strategy, ArbInterp efficiently enforce spatio-temporal coherence across sequential frame interpolation. Notably, our method is the first effort in the generative video field address the coherence challenge in streaming frame interpolation.
For streaming frame interpolation, we generate intermediate frames between every two consecutive anchors using direct interpolation. Meanwhile, the appearance-motion decoupling conditioning strategy also employed to ensure spatiotemporal consistency across different anchors. For long- term streaming interpolation scenarios, as shown in Figure 8 ArbInterp exhibits superior motion coherence and appearance consistency by effectively leveraging previously generated frames to assist subsequent generation. More video comparisons provided in the website.
CONCLUSION
In this work, present Arbinterp. novel generative frame interpolation paradigm that can generate an arbitrary number of intermediate frames by specifying continuous timestamps. To achieve this, introduce Timestamp-aware RoPE (TaRoPÉ), enabling the model perceive the actual temporal position of each frame rather than relying on fixed input indices. Through this simple yet effective mechanism, ArbInterp theoretically supports infinite-length interpolation. Furthermore, propose an uppearance-motion decoupling conditioning strategy to enhance spatio-temporal consistency in long sequences. We design MultiinterpBench to rigorously evaluate ArbInterp. Experimental results demonstrate the superior flexibility of Arbinterp across various interpolation ratios and strong coherence long-term video interpolation, offering new architectural possibilities for future generative frame interpolation research.
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ACKNOWLEDGMENTS
This work is supported by the Basic Research Program of Jiangsu (No. BK20250009) and the Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Appendix
Model details
16
A.1 Details of transferring video generation models to frame interpolation
16
2 Details of motion semantic extractor
16
B More implementation details
17
Evaluation details
17
B.2
Training details,
17
Additional results
18
C.1
Visual comparison of TaRoPE
18
The elfectiveness of motion decoupling
18
Interpolation ratio beyond training
19
Ablation experiments across training stages
19
D
I Limitation and future work
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Broader impact
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License of datasets and pre-trained models
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Use of Large Language Models (LLMs)
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A
MODEL DETAILS
Temporally Enhanced CLIP
Previous Frames
Image Takens
Special Belf
Norm
MLP
Start & End Frames
X
(7 x
AO
Fletten
Tunable
Frozen
Tomposil Embedting
X
Qormer
Me M Querles
(' MJ
Sputial & Temporal
Swit Artn
Selt Ams
HLP
Norm
MLD
Motion Tokens
Norm
Figure 9: Structure of the motion semantic extractor. We first extract aratio-temporal features using the temporally enhanced CLIP, then compress these features into motion tokens via Q-Formers.
A.1 DETAILS OF TRANSFERRING VIDEO GENERATION MODELS TO FRAME INTERPOLATION
In Section 3.2 we have already briefly introduced our strategy for transferring video generation models to frame interpolation. Here we provide more details.
Starting from an input video tokenizer (Kingma & Welling (022) provided by Wan (Wang et al.) (025). where each frame 2; is individually encoded without temporal compression get frame-wise video latents {21, .e,
( 32 B2..... Øt} with frames, encode it with the video
During training stages, for any timestep [0, 1). we add a Gaussian noise to non-condition frames, namely, frames excluding starting and end frames (i.e. 21 and 2,), as well as prefix frame (i.e. z2) if exists. Formally, the noising add process can be define as
= x E + (1 (1- n) 24 2 2
(6)
where e!- ~ ~V, 1) is the Gaussian noise we add to each frame. Afterwards, we can obtain noisy video latents zd (21, 22/2// ...,., "[_ 21,, which is inputted into ArbInterp to predict velocity
vector (="" n, y). We calculate loss for non-condition frames only as
C
my O || uo(>" 4)11
where Vn. y, is as explained in Equation and m; E {0, 1} indicates whether a frame is non-condition frame.
A.2 DETAILS MOTION SEMANTIC EXTRACTOR
The overall architecture of our Motion Semantic Extractor (MSE) is presented in Figure 9 a video clip or start and end frames as input, MSE firstly encode each frame into image tokens independently. Afterward, the image tokens are fed into temporally enhanced CLIP model, which is initialized from a pre- trained CLIP [Radford et al.) 20216). This enhanced model comprises N1 + N2 layers, each consisting of self-attention module, MLP module, and layer normalization. The initial N, layers focus on spatial feature extraction, where self-attention modules operate exclusively on tokens within each frame to capture spatial relationships. To be noted, the initial N; layers
Taking
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frozen to retain CLIP's ability to capture spatial feature. Following these N1 layers, temporal embeddings are added to the features of all frames to provide explicit temporal positional information. The receptive field of self-attention in the subsequent N2 layers is expanded encompass tokens from all frames, enabling the modeling of global spatio-temporal information across the entire video segment. Finally, the temporally enhanced CLIP model outputs spatio-temporal video embedding. Both the temporal embedding and the final N2 layers are tunable to acquire new spatio-temporal modeling capabilities.
To further compress motion information, we utilize a trainable Q-Former (Li et al. 2023b), which comprises of initial queries, and Ng layers, each containing self-attention module, a cross attenttoon module, and an MLP module. The queries interact with the video embedding by cross-attention, which distills the essential motion information from the video embedding into fixed number of output motion tokens, effectively compressing key spatio-temporal dynamics.
B MORE IMPLEMENTATION DETAILS
In Section 4.1 of the main paper, we provide an concise version of the implementation details. This supplementary section intends to provide more necessary technical details.
B.1 EVALUATION DETAILS
To comprchensively evaluate the performance of ArbInterp under diverse frame interpolation settings, we design a new benchmarks: MultilnterpBench. MultilnterpBench evaluates the model's flexibility and performance in generating frames different timestamps and varying numbers, covering interpo- lation ratios of 2x, 8x, 16x, and 32x. It consists of 552 frame pairs selected from widely used datasets such as DAVIS (Pont-Tuset et al. 2017), SNU-FILM (Choi et al. 2020). and XTEST (Sim et al. 2021).
To balance GPU memory usage and computational efficiency, we adopt the direct frame interpolation method shown in Figure 3 a) for videos with fewer than 21 total frames. For example, the timestamp list for 8x interpolation is [0, 1/ 2/8, 3/8, 4/8, 5/8, 6/8, 7/8, 1]. For scenarios requiring more than 21 frames (c.g., 32x interpolation), we employ hierarchical design in from Figure () due to its superior stability. Specifically, we first predict the frame Ø0.s at f = 0.5, then generate two 15-frame segments from To To.5 and from 20.5 I1, respectively. We utilize the appearance- motion decoupling conditioning strategy to enhance the spatiotemporal coherence between these two segments.
Following Wan (Wang et al., 2025), we employ UniPC (Zhao et al. 2023) to optimize our flow matching-baat based denoising scheduler during inferences. all inferences, we denoise from Gaussian noise for 50 steps with timestep shift set to 5.0.
B.2 TRAINING DETAILS.
We load the pretrained weight of Wan2.1 1.3B (Wang al. 2025) to initialize ArbInterp. The model accepts two input components: (1) an interpolation sequence comprising 3 to 21 frames, including fixed starting frame, fixed ending frame, an optional prefix frame, and the remaining slots filled with noisy intermediate frames; (2) preceding motion conditioning frames consisting of up contextual frames extracted from adjacent video segments. During training, prefix frame is sampled at a rate of 50%. The time gap between the starting and end frame is limited to seconds most. All input frames are adaptively resized to one of five predefined resolutions (480 x 832, 480 x 640, 480 x 480, 832 x 480, and 640 x 480) based on most matched aspect ratio. Specifically. we first calculate the original aspect ratio of the input image and then select the target resolution that maintains the closest matching aspect ratio from our predefined set. Our training dataset comprises 50,000 videos meticulously curated from Open Vid (Nan et al. 2024). ArbInterp is trained stages on 8 GPUs (96GB) with total batch size of 16. For memory efficieen training, we employed the Zero Redundancy Optimizer stage 3 (ZeRO-3) strategy (Rajbhandari et al., 2020). The base optimizer is AdamW/ (Loshchilov & Hutter 2019), configured with a learning rate of 1 x 10-4 betas of (0.9. 0.999), an epsilon of 1 x 10 and a weight decay of 0.01. The learning rate performs a linear warmup (Goyal et al. 2017) over the first 1000 training steps.
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Figure 10: Visualization of controlled motion transfer.
First stage. In the first stage, we only finetune the parameters of all DiT blocks and the output head for 10,000 optimization steps. The timestep shift of the noise scheduler is set to 1.0, ensuring uniform training across diverse input noise levels.
Second stage. In the second stage, we freeze the parameters of DiT and train the motion semantic extractor, with trainable parameters including temporal transformer layers CLIP, Q-Former layers, implement
and MLPs for motion feature injection into DiT blocks. To enhance robustness. randomized input dropout: 20% probability of discarding preceding motion conditioning frames, 20% probability of omitting boundary frames, 10% probability of dropping both inputs simultaneously, and 50% probability of retaining full inputs. This stage executes approximately 5,000 training steps. In this stage, the noise scheduler is configured with timestep shift of 3.0 to increase the expected noise intensity, which enhances the motion feature injection's 's control over high-level semantic content. In the stage, with 50% likelihood, previous frames chosen before the starting frame while intermediate frames are consecutive to and follow it; otherwise, previous frames and intermediate frames are consecutive sequences sampled after the starting frame, with intermediate frames directly succeeding previous frames. Such data sampling strategy aims at ensuring the model to be equally proficient at utilizing motion context either prior to or subsequent to the starting frame, both are required during inferences.
Third stage. In the third stage, all parameters during the first and second stages are trained. This stage proceeds for 5,000 training steps. To be noted, we set the timestep shift of the noise scheduler back 1.0 for balanced training over all noise intensities.
C ADDITIONAL RESULTS
C. VISUAL COMPARISON OF TAROPE
We present visual comparisons, as shown in Figure 11 to further validate the effectiveness of TaRoPE. Methods without timestamp injection or those solely relying on MLP-injected AdaLN fail to precisely control the temporal position of generated intermediate frames. In contrast, our proposed TaRoPE accurately generates frames corresponding to specific timestamps.
C.2 THE EFFECTIVENESS OF MOTION DECOUPLING
To rigorously verify the proper decoupling of appearance and motion in our framework, we trained new dedicated model. This model reconstructs the original video by being conditioned on the first frame and leveraging our proposed Motion Semantic Extractor (MSE). During testing, we feed the video into the MSE to extract motion semanties, then edit the first frame (appearance) while retaining the extracted motion information, and observe whether the model can generate videos with consistent motion. As illustrated in the Figure I0] our decoupling mechanism successfully extracts motion information to a considerable extent, as evidenced by the generated videos that preserve the original motion despite modified appearance.
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Table Quantitative comparison with the state-of-the-art methods on 256x interpolation.
VBench
Interp. Rate
FID
LPIPS
Method
(1)
(j>
(1)
(1)
Satjer
Rafigmindd
Torpeesi
Marion
Anshetic
breging
Owell
Qualit
Quality
Average
LDMVE
50.7
522.3
0.8568
0.0026
0.9110
0.4578
0:5997
0.2672
TRF
38.2
712.5
0.372
0.861
0.8313
0.3675
0,5810
0,9052
0.4311
0.5303
0,7411
GI
256
41.2
687.9
0.385
0.679
0.8515
0.5708
0.3827
0,9028
0.4336
0.5357
0.7462
DemamiCrafiir
54.5
553.1
0.297
0.663
0.8536
0.8867
0.9052
0,9263
0.4518
0.5709
0,7658
Aiblnior-SVVD
28.4
185.0
0.121
0.092
0.815
0.9027
0.3097
0.9981
0.9733
0.5679
0.6164
Achlmerp
31.5
242.3
0,115
0.728
0.8969
0.9147
0.914
0.3356
0.4772
0.6080
0.7908
Input Frames
None
Vanilla
TaRoPE (Ours)
Frace
Figure 11: Comparison of different timestamp injection methods.
C.3 INTERPOLATION RATIO BEYOND TRAINING
To further validate the ability to handle continuous timestamps, we provide 256x comparisons in T Since our original test set lacked sufficient frames, we re-collected 10 videos for this test. The result is obtained by averaging the results of each 32-frame sub-clip, as many metrics cannot process sequences of such length. Despite the granularity exceeding the training range, the sustained performance advantage further confirms the effectiveness of our method in handling continuous timestamps.
C.4 ABLATION EXPERIMENTS ACROSS TRAINING STAGES
We also conducted ablation experiments on different training stages. As shown in Table directly learning all functions (only stage3) fails to achieve the best performance. In contrast, the staged training approach, which first learns the basic frame interpolation function, then establishes spatio- temporal continuity between different segments, and finally performs joint fine-tuning, exhibits higher performance ceiling.
D LIMITATION AND FUTURE WORK
As research-oriented work, the primary contributions of this work lie in successfully demonstrating that temporally fine-grained video frame interpolation can be achieved through introducing timestamp- aware RoPE into generative models, and in designing appearance-motion decoupling conditioning strategy to facilitate long-term video frame interpolation. To maintain the simplicity of the overall design, omitted text inputs and only used the first and last frames inputs, which inevitably limits the model's 's controllability and semantic reasoning capabilities. Additionally, due to limited resources, employed relatively small generative model and small-scale public dataset. Despite
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Published as a conference paper at ICLR 2026
Table 4: Ablation study on different training stages.
Training Stages
Evaluation Metrics
FVDa2x (U)
VBenchazx (1)
Stage
Stage
Stage
401.6
0.819
359.2
0.8253
357.2
0.8247
319.9
0.8324
Table 5: Licenses and URLs for every benchmark, code, and pretrained models used in this paper.
Assets
License
URL
OpenVid SNU-FILM XIEST
CC-BY-4.0
Intps://uuginggaan cowdarasetssnkkp 3tt Open Vid- Vid-IM
Benchmarks
MIT license
For reseurch and education only
Wan 2.1
Apache-2.0 License
bitps:/ggthhb
Vidco Wan2
Codes and Pretrained Models
LDMVH
MIT license
hitps::/gtti githu http:/tttubb.ooo/aiui. con/daneert ALDMH
DynamiCrafter TRF
Apache-2.0 License for research only
https://ithub cony Doubiny DynamiCrafis
httpps//pithubb.oo com/ HanleHHaeenee wwng/eeegling lige reversal
Apache License
https://gillu..com///e wang keykkkyyraaa beylrame interpolation
outperforming state-of-the-ee thrt methods, the quality ceiling for complex scenarios remains constrained. In the future, we plan to integrate text guidance, scale both the dataset and model, and thereby advance the practical utility of method for frame interpolation.
E BROADER IMPACT
This work proposes novel generative frame interpolation paradigm, ArbInterp, which extends the ca- pability boundary of current frame interpolation models. Similar to other generative models, although use carefully screened data, the generated content still has certain degree of uncontrollability, In the future, such uncontrollability can be constrained through further data cleaning and reinforcement learning, In this way, we can further balance the innovation of content with the potential impacts that fictionalization may incur.
F LICENSE OF DATASETS AND PRE-TRAINED MODELS
All the dataset we used in the paper are commonly used datasets for academic purpose. All the licenses of the used benchmark, codes, and pretrained models are listed in Table
G USE OF LARGE LANGUAGE MODELS (LLMs)
This paper only uses LLMs for grammar correction.
20


Reviews:
[{"review_id": "pjwk440TB1", "reviewer": "Reviewer_SVtL", "summary": "The paper proposes ArbInterp, a generative VFI framework that supports synthesizing intermediate frames at arbitrary continuous timestamps and arbitrary sequence lengths. The core idea is to replace discrete temporal indices with normalized timestamps in temporal dimensions via a timestamp-aware Rotary Position Embedding (RoPE). The authors also introduce an appearance–motion decoupled conditioning strategy, where a Motion Semantic Extractor (MSE) helps maintain motion coherence through cross-attention. Experiments show consistent gains over generative VFI baselines across VBench dimensions.", "questions": "Please check Weaknesses", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The method fine-tunes Wan 2.1 (1.3B) with relatively modest compute (50k videos, 8 GPUs), suggesting decent data efficiency for the reported gains.\n- Replacing temporal indices with normalized timestamps in RoPE is simple, training-efficient, and directly addresses the fixed-length limitation in generative VFI.\n- The figures and supplementary materials are clear and helpful for understanding and reproducing the approach.", "weaknesses": "- Model design constraints. Wan 2.1 uses a causal VAE, so the receptive field of the last latent can depend on previous content. This implies the encoder needs access to the whole clip, which seems at odds with arbitrarily interpolating between two standalone images. The paper should clarify this limitation and the exact inference procedure for two-image interpolation without full context.\n- Limited qualitative evaluation. To better assess generalization, please include:\na: Interpolation results given two arbitrary images (without video context).\nb: Multiple interpolation samples (different random seeds) for the same start/end frames to assess diversity and stability.\n- The fps in the presented interpolations results is very low, weakening the author's claim of arbitrary-length interpolation.\n- MultiInterpBench appears to be largely composed of DAVIS data. If so, its novelty as a benchmark is limited. Please clarify the composition and splits beyond reuse of DAVIS.\n- Given that VBench automatic metrics may not fully match human perception, it would help to include more challenging and diverse cases beyond standard DAVIS examples (e.g., large motion, occlusions, motion blur, non-rigid motion, out-of-domain content), along with a small-scale user study if feasible.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "se0nS9OyP1", "reviewer": "Reviewer_wVzm", "summary": "This paper introduces a novel video frame interpolation pipeline that finetunes a pretrained video generation model. Conditioned on a start and end frame, the model synthesizes intermediate frames through a new rotary position encoding (RoPE) design and an appearance-and-motion decoupling mechanism. Experiments validate the efficacy of the proposed approach.", "questions": "Is multi-stage training necessary? Have you experimented with training all the modules end-to-end in a single stage?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The paper is well written and easy to follow.\nThe paper presents strong experimental results that surpasses several baselines.", "weaknesses": "1. The claim that previous latent concatenation is impractical (L313-L316) lacks experimental support. To proof this, the authors should report the impact on GPU memory during training and inference, as well as the performance under an equivalent computational budget compared to the current method.\n\n2. Since the proposed Motion Semantic Extractor (MSE) uses CLIP as its backbone, it may inherit CLIP's limitation in capturing fine-grained details. Consequently, the MSE risks failing to accurately model subtle motion information from the previous video chunk.\n\n3. There're severe artifacts in the generated videos on the webpage. For example, the wheel in the moving car example moves unnaturally.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wWPOTgXNaS", "reviewer": "Reviewer_PUsK", "summary": "This paper aims to enable frame interpolation of arbitrary timesteps, which was not possible with pre-trained video diffusion model-based methods. The paper first propose TaRoPE, which is a variation of RoPE which normalizes the timestamp between the first and last frames, and enable predictions at continuous timestamps. To cope with high computation demands, the authors propose to make predictions segment-wise, either linearly (segment-by-segment) or hierarchically. In this process, to ensure spatio-temporal consistencies, appearance-motion decoupling conditioning is propoosed. The last frame tokens of a previous segment are used to ensure appearance consistencies between segments, and using a spatio-temporally tuned CLIP and a QFormer, the motion of the previous segment is encoded and this information is injected via cross attention.", "questions": "- I wonder the extrapolation ability and extension to other tasks of the proposed method. Despite the model being tuned for interpolation task only, it would be very interesting if the proposed method shows its effectiveness beyond interpolation task.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper is easy to follow, with clear problem definition.\n2. The proposed framework seems to be well-designed, with strong results.", "weaknesses": "1. Novelty of TaRoPE: the proposed approach of normalizing the timestamps sounds reasonable and seems to be effective. However, I feel hesitant to agree on the novelty of this scheme. To begin with, normalization of timestamps is a commonly used approach in the field of video frame interpolation (VFI) [1-6]. It does differ in that most methods used them for scaling the optical flows, while this paper used it in positional encoding instead of flows, but the idea itself of normalizing intermediate timestamps is not something very new in the field of (traditional) VFI. Second, normalization / use of decimal position coordinates in PE is something already discussed in [9]. For these reasons, I find the novelty of TaRoPE to be quite limited.\n\n2. Evaluations: 1) This paper is missing several important state-of-the-art methods for comparison [11, 12]. 2) The evaluation is mainly conducted on a benchmark built by themselves, and does not evaluate on a common benchmark existing in the field, such as DAVIS and Pexels [10, 11]. Proposing a new useful benchmark is welcomed, but in order to justify the efficacy of one’s methodology, it is also important to show evaluations on a commonly used benchmark to ensure fairness. Regarding evaluations, I do not think the comparison against LDMVFI to be fair.\n\n## On the definition of the task\nI would like to suggest the task of the paper to be defined as either \"generative inbetweening\" or \"keyframe interpolation\", rather than video frame interpolation (VFI). This is not merely an issue of this paper only, but is an issue of all recent papers accommodating pre-trained video diffusion models for video frame interpolation. Despite using the same term “video frame interpolation”, the task these recent methods address are quite different from traditional VFI [1-8]. This recent VFI methods with pre-trained diffusion models [10-12] assume a more extreme, challenging scenario than the task of traditional VFI. Traditional VFI has been studied in the past decade to solve a scenario where the input video has a fps of {15, 30, 60, 120} fps and aim to increase this to {30, 60, 120, 240, …, 1000} fps [1-8]. However, recent studies based on pre-trained diffusion models, including this work, assume a scenario where the input frame rate is much lower, as low as 1 fps (25 frame gap) [10-12]. One may wonder how much of a big difference this could make, the input frame rate being 1fps vs 30fps. But I argue that this fps difference in fact does make a huge difference, changing the nature of the task. The traditional video frame interpolation task considers interpolation of 15+fps (usually 30fps) input videos. The time gap between frames is extremely small, approximately 0.03 seconds. This is an extremely short time and therefore the diversity of possible intermediate motions is very limited. On the other hand, when it comes to the scenario of using 1 fps input videos, the number of possible motions increase drastically within a 1 second gap. Accordingly, traditional VFI task is considered a “restoration” task, recovering intermediate data points from a set of densely sampled data points. On the other hand, the new extreme-case frame interpolation with pre-trained diffusion models is considered more of a “generation” task, since the given input data points are far more sparse. The two scenarios pursue clearly different directions, so I think it is not fair to compare pre-trained diffusion-based methods with traditional VFI methods in extreme-low-fps scenario. For instance, LDMVFI, one of the baselines in the paper, is trained under different settings with much smaller-size dataset. Theoretically speaking, I think there is a possibility that the experimental results could have been different under the same amount of computational cost and matched train data settings. Therefore, I think there should be a clear distinction between the two tasks and I suggest the term “keyframe interpolation” to be more appropriate for these extreme-case scenarios. “Generative interpolation” does not seem to be a good alternative, since there has been several generative model-based studies also in traditional VFI literature [13, 14]. If the paper were to adhere to the task of VFI, I suggest that the paper to also compare with traditional VFI methods in their 30 fps input scenarios too, for thorough and fair evaluations. I think this suggestion could partially resolve some of the issues I mentioned above, e.g., novelty of timestamp normalization.\n\n[1] Jiang, Huaizu, et al. \"Super slomo: High quality estimation of multiple intermediate frames for video interpolation.\" CVPR 2018\n\n[2] Xu, Xiangyu, et al. \"Quadratic video interpolation.\" NeurIPS 2019\n\n[3] Niklaus, Simon, and Feng Liu. \"Softmax splatting for video frame interpolation.\" CVPR 2020\n\n[4] Sim, H., Oh, J., and Kim, M. \"Xvfi: extreme video frame interpolation.\" ICCV 2021\n\n[5] Kong, Lingtong, et al. \"Ifrnet: Intermediate feature refine network for efficient frame interpolation.\" CVPR 2022.\n\n[6] Huang, Zhewei, et al. \"Real-time intermediate flow estimation for video frame interpolation.\" ECCV 2022\n\n[7] Xue, Tianfan, et al. \"Video enhancement with task-oriented flow.\" IJCV 2019\n\n[8] Choi, Myungsub, et al. \"Channel attention is all you need for video frame interpolation.\" AAAI 2020.\n\n[9] Chen, Shouyuan, et al. \"Extending context window of large language models via positional interpolation.\" arXiv preprint arXiv:2306.15595 (2023)\n\n[10] Wang, Xiaojuan, et al. \"Generative inbetweening: Adapting image-to-video models for keyframe interpolation, ICLR 2025\n\n[11] Yang, Serin, Taesung Kwon, and Jong Chul Ye. \"Vibidsampler: Enhancing video interpolation using bidirectional diffusion sampler.\" ICLR 2025.\n\n[12] Zhu, Tianyi, et al. \"Generative inbetweening through frame-wise conditions-driven video generation.\" CVPR 2025.\n\n[13] Danier, Duolikun, Fan Zhang, and David Bull. \"Ldmvfi: Video frame interpolation with latent diffusion models.\" AAAI 2024.\n\n[14] Lew, Jaihyun, et al. \"Disentangled motion modeling for video frame interpolation.\" AAAI 2025", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zg6oJ2WLOW", "reviewer": "Reviewer_E5JG", "summary": "The paper proposes a method for continuous video interpolation that generates intermediate frames at any frame rate. Given a start and end frame, the method interpolates frames at arbitrary timestamps. To achieve this, the authors assign timestamps 0 and 1 to the start and end frames, respectively, normalize intermediate timestamps, and introduce a timestamp-aware RoPE. They further propose appearance–motion decoupled conditioning, enabling smooth and continuous segment-level interpolation. On VBench, the method achieves significantly superior video quality compared to prior approaches.", "questions": "- Beyond VFI, it would be valuable to verify whether appearance and motion have indeed been properly decoupled. If the decoupling is effective, could one borrow motion from other objects and use it as input for controlled motion transfer?\n- Can the model also predict consecutive frames at non-equidistant timestamps? For example, at timestamps such as [0, 0.1, 0.4, 0.5, 0.9, 1]", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- **Novel problem setting:** This is the first work, to the best of my knowledge, to utilize generative models for continuous interpolation between two frames.\n- **Clear and intuitive writing:** The paper is easy to follow and provides intuitive explanations. In particular, the concise extension from standard RoPE to timestamp-aware RoPE (TaRoPE) is elegant and highly effective. The method demonstrates strong performance, significantly outperforming existing approaches.", "weaknesses": "- While it is novel to apply generative modeling to VFI, methods that use continuous timestamps already exist in the non-generative video frame interpolation literature [1]. The paper does not compare against these methods. Including such comparisons would more clearly position this work relative to deterministic approaches.\n- The authors combine timestamp-aware RoPE with a training dataset containing a wide timestamp range (1/2–1/240), which likely contributes significantly to performance. However, it is unclear whether the baselines were trained under the same settings. If baselines were trained only at fixed rates, it becomes difficult to attribute performance gains solely to TaRoPE rather than to broader timestamp coverage. It would be beneficial to train at least one baseline using the same diverse-rate data to clarify this.\n\n[1] Super slomo: High quality estimation of multiple intermediate frames for video interpolation, Huaizu, et al., 2018", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)