Motion Prior Distillation in Time Reversal Sampling for Generative Inbetweening
Authors: Wooseok Jeon
Seunghyun Shin
Dongmin Shin
Hae-Gon Jeon
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Models
Generative Inbetweening
Video Frame Interpolation
Primary area: generative models

Abstract: Recent progress in image-to-video (I2V) diffusion models has significantly advanced the field of generative inbetweening, which aims to generate semantically plausible frames between two keyframes. In particular, inference-time sampling strategies, which leverage the generative priors of large-scale pre-trained I2V models without additional training, have become increasingly popular. However, existing inference-time sampling, either fusing forward and backward paths in parallel or alternating them sequentially, often suffers from temporal discontinuities and undesirable visual artifacts due to the misalignment between the two generated paths. This is because each path follows the motion prior induced by its own conditioning frame. In this work, we propose Motion Prior Distillation (MPD), a simple yet effective inference-time distillation technique that suppresses bidirectional mismatch by distilling the motion residual of the forward path into the backward path. Our method can deliberately avoid denoising the end-conditioned path which causes the ambiguity of the path, and yield more temporally coherent inbetweening results with the forward motion prior. We not only perform quantitative evaluations on standard benchmarks, but also conduct extensive user studies to demonstrate the effectiveness of our approach in practical scenarios.

Published as a conference paper at ICLR 2026
MOTION PRIOR DISTILLATION IN TIME REVERSAL SAMPLING FOR GENER INBETWEENING
Wooseok Jeon', Seunghyun Shin", Dongmin Shin', Hae-Gon Jeon
Department of Artificial Intelligence, Yonsei University 2 AT Graduate School, GIST
Four tee de GeY Blb
Jun Be
No motion contlitt
del (h4
Top down wowwwwew
Motion Prior
Mution Prior Confilit
Distillation
Residual
(c) Baslovand (c) Forvare
In 1985
180 Ours
Figure 1: Overview of the proposed motion prior distillation. (a) Ideal case of generative inbe- tweening task. (b) Motion prior conflict in existing time reversal sampling method. (c) Our proposed motion prior distillation method. (d) A video generated by Stable Video Diffusion model condi- tioned on the start frame, and (e) conditioned on the end frame and temporally flipped. (f) A result from existing time reversal sampling method, showing ghosting artifact and reverse play due to mo- tion prior conflict. (g) A result from our proposed method, showing temporally coherent motion.
ABSTRACT
Recent progress in image-to-video (12V) diffusion models has significantly ad- vanced the field generative inbetweeming, which aims to generate semantically plausible frames between two keyframes. In particular, inference-time sampling strategies, which leverage the generative priors of large-scale pre-trained 12V models without additional training, have become increasingly popular. However, existing inference-timgn sampling, either fusing forward and backward paths in par- allel alternating them sequentially, often suffers from temporal discontinuities and undesirable visual artifacts due to the misalignment between the two gener- ated paths. This is because each path follows the motion prior induced by its own conditioning frame. In this work, we propose Motion Prior Distillation (MPD). a simple yet effective efference-time distillation technique that suppresses bidi- rectional mismatch by distilling the motion residual of the forward path into the backward path. Our method can deliberately avoid denoising the end-conditioned path which causes the ambiguity of the path, and yield more temporally coherent inbetweening results with the forward motion prior. We not only perform quantita- tive evaluations standard benchmarks, but also conduct extensive user studies to demonstrate the effectiveness of our approach in practical scenarios. Project page: httpp:://vss.tt. gi thtb. 1o/MPD/
"Corresponiing author

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Published as a conference paper at ICLR 2026
INTRODUCTION
Recent advances in diffusion models have significantly improved the performance of image and video generation tasks. In partieular, image-to-video (12V) diffusion models (Blattmann et al. 2023a Xing et al. 2024b; Bar-TTal et al., 2024 Yang et al. 2025b) demonstrate strong capabil- ities across diverse applications, as they can generate temporally coherent videos from a single conditioning frame. From a generative perspective, this progress has extended video frame interpo- lation to generative inbetweening, which aims to generate natural intermediate frames between two keyframes (See Fig. [ (a)). However, 12V diffusion models are not directly applicable to bounded generation where both start and end frames serve as a dual-constraint.
To address this, recent studies have explored time reversal sampling, which employs temporally forward / backward denoising paths conditioned on the start / end frames during the iterative reverse denoising process (See Fig. [ (b)). This be categorized into two approaches, namely parallel and sequential, according to how these two paths are integrated. In the parallel approach (Feng et al. 2024 Wang et al., 2025b; Zhu et al. 2025), , samples from the forward and backward paths are denoised simultaneously at each denoising step and then linearly interpolated to form the input for the next denoising step. In contrast, the sequential approach (Yang et al., (202a)) samples two denoising paths sequentially by inserting a single re-noising step between them.
However, simply connecting the two temporal paths does not guarantee single coherent motion during the sampling process because each sample is obtained with the motion prior of its condition- ing frame (See Fig. (d) and (e)). In particular, as shown in Fig. (e). backward path initialized at the end frame tends to generate forward- howaad-lookknn sequences, instead of faithfully reconstructing his- torical frames. This -ewarrggennettoon bias commonly arises from 12V models, which are trained to predict consecutive forward frames. As shown in Fig. (f), the generated frames noticeably follow different routes and even disagree on the car's s destination, which we refer to as a motion conflict be- tween two temporal paths. This highlights that the fundamental challenge lies not merely in how to connect the forward and backward paths, but in how to align their conflicting motion priors induced by the forward- forward-generation bias.
To this end, we aim to overcome this fundamental misalignment between two temporal paths by proposing novel inference-time distillation approach, called Motion Prior Distillation (MPD). Our key intuition is that the residual of the denoised estimates contains motion information induced by a given start frame. Inspired by this, during early denoising steps, our method distills the motion residual induced by the start frame into the backward path (See Fig. (c)) Since our approach delib- erately avoids denoising the end-conditioned path, we can drive the backward path to follow the time reversed motion residual of the forward path, thereby achieving bidirectional path alignment (See Fig. [ (g)). This single path design effectively removes conflicting motion priors while preserving endpoint consistency, allowing two temporal paths to converge into coherent motions.
Through extensive evaluations, we demonstrate that our method consistently outperforms relevant methods, including existing time reversal sampling strategies. In addition, since conventional metrics are not fully capable of evaluating temporal coherence and human preference, we further conduct user studies to validate its robustness under practical scenarios in the presence of complex motion patterns and large temporal displacements.
2 RELATED WORKS
Video frame interpolation. Video frame interpolation (VFI) aims to synthesize intermediate frames between two input frames while maintaining spatial and temporal coherence (Lyu et al.
2024
Kye et al. 2025). Supervised methods (Bao et al., 2019 Niklaus & Liu, 2018: Park et al.]
2020 Lei et al., 2023] Kong al. 2022 Li et al., 2023] Huang et 2022]] Lu et al. 2022 Reda et al. 2022) that rely on estimating optical flows have been practically adopted due to their robust performance and interpretable motion trajectories. However, errors in estimated flows often lead to failures, particularly under the scenes with occlusion or non-linear motion (Long et al. 2024). Re- cently, diffusion-basee VFI methods (Danier et al., 22024] Voleti et al. 2022) have attempted to take advantage of the generative capabilities of diffusion models to improve the perceptual quality of in- terpolated frames. While these methods improve perceptual fidelity, their performance still degrades under large temporal displacements between two frames.
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X Maun
Suart
Dencise with with Fend
Time fllp
x(1aa
x
X 1/fend
x;
(a) Parallel method
(b) Sequential method
Forward
Re-noise
Motion Prior Distillation
SVD
Residual
De
AWW E4.16
X
fiwd
Forword path
Backward path
Motion Prior Distiliaton
Fuse
Backovard
Flip
AE
Ebwd
(c) Motion Prior Distillation
Figure 2: Denoising process the proposed motion prior distillation. Existing time reversal sampling methods simply connect the two temporal paths either by (a) linearly fusing them (b) alternatively denoising each path. (c) Our MPD is employed on time reversal sampling framework distill forward motion prior into the backward path, thereby achieving motion alignment.
Generative video inbetweening. With the advancement of video diffusion models (Ho et al. 2022b]a Blattmann et al.) 2223bbar a). VFI has broadened into generative inbetweening. which is interested the set of semantically plausible interpolations. Some approaches (Jain et al., [002]] Xing et al.] 22244a] b) Wang et al., 2025a iZhang et al., (205)) train diffusion models to condition on two input frames for interpolation, yielding greater robustness to ambiguous and large motion where traditional methods have struggled. While effective, they typically require substantial train- ing resources. Other approaches leverage pre-trained large-scale 12V diffusion models and achieve remarkable performances by incorporating poses a time reversal sampling strategy that fuses forward and backward denoising paths in parallel, each conditioned enhances reverse motion fidelity by fine-tuning a diffusion model through rotation of temporal self- attention maps to generate temporally reversed frames. Similarly, FCVG (Zhu et 2025) proposes a method that injects line correspondences as frame-wise conditions to alleviate the ambiguity of inbetweening path. Meanwhile, ViBiDSampler (Yang et al. 2025a) introduces a new time reversal strategy that employs sequential sampling along forward and backward paths to achieve on- on-manifold generation of intermediate frames. However, all of them still operate with two independent motion priors from the start and end frames, convergence to single coherent trajectory is not guaranteed.
sampling techniques. TRF (Feng et al. 2024) pro-
the start and end frames. Building this strategy, GI (Wang et al. 2025b)
3 PRELIMINARIES
3.1 STABLE VIDEO DIFFUSION
We base our explanation Stable Video Diffusion (SVD) (llttmann al. 2023a). which is widely adopted in time reversal sampling based methods. Specifically. SVD is a UNet-based latent video diffusion model built on EDM framework (Karras al. 2022). At reverse denoising step t e (T...,, 1} with the noise level ot, the denoiser Do predicts both the unconditional estimate 2 and the conditional estimate tо from the current noisy latent 2y;
$0 Dp(x.;0t)) and $0 Do(
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where is the imput condition. In EDM framework, the corresponding noise prediction model cæ and the score prediction model se have the following relationship with the denoiser Dy:
88 pr; σ.) (z) Do(xi; Dt.
dt
of
To guide the sample toward the condition c, the classifier-free guidance (CFG) (Ho & Salimans 2021 mixes the unconditional estimate žn with the conditional estimate cp.e:
T0.e (1 + w) - wroo
where 2 0 is guidance strength. At each iteration, we can denoise the sample with Euler step. progressively denoising from Gaussian noise Tr to sample Zo:
xt-1 = To az
c E0.e).
(4)
In particular, 12V models take the initial starting frame condition as input and generate videos with its motion prior. To reflect both start and end frame conditions, time reversal sampling process involves denoising two temporal paths with each corresponding frame condition.
3.2
TIME REVERSAL SAMPLING
Parallel method. As illustrated in Fig. (a), parallel time reversal methods denoise the tempo- rally forward/backward path conditioned the start/end frame, and then fuse them to produce the intermediate frames (Feng et al.) 2024] Wang et al.] [2025b] Zhu et al. (2025). Let's denote Caunt and Cend as the encoded latent conditions of the start and end frame, respectively. We can express the denoising step as follows:
L1-1 = a10:-1,cum + (1 11-00 aa((((( (3)
(5)
s.t. 11-1, .oo.oo + 01-1 (It D.0.egin)
(6)
and 2/-1,000 Ho
0t-1
(z', #lorow)
Tt
where (.)' indicates temporal flip along the time dimension and [0, 1] refers the interpolation weight. However, this method can suffer from off off-manifold issues, where samples deviate from the learned data manifold. As a result, their linearly interpolated results often lead oscillations and undesirable artifacts. Furthermore, they do not resolve the conficciigg motion priors induced by the two conditions, so motion fidelity can still degrade.
Sequential method. An alternative approach adopts the sequential time reversal sampling strat- egy Yangg et al.) (025a)). Instead of fusing two temporal paths in parallel, this method sequentially denoises the forward and backward paths as Fig. (b). On-manifold generation can be achieved by inserting a single re-noising step before switching from the forward to the backward path:
01-1 (x: L.,com))
F F1-1, LD
St
Dt.coom = I1-1,cam + Voi 12-o7-1 of
E~ N(0,I)
(9)
Ct-1 (io.
0r-1 (Kt, @o,con))
(10)
a+
Unlike the parallel approach, this sequential structure maintains more consistent and manifold- aligned path. Nevertheless, alternating two denoised paths results in conflicting motion priors, as each path relies on its own conditioning frame. This highlights the need to align two temporal paths without motion prior conflicts.
METHOD
Given pair of two frames {smm lena), our goal is to align two temporal paths with both temporal coherence and visual fidelity. Fig. 2 (c) provides an overview of our method. To begin with, we recast the time reversal sampling process misalignment problem. In this work, we present a simple yet effective approach called Motion Prior Distillation (MPD) which propagates motion residual from a forward path into backward path.
optimization problem to solve bidirectional path
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4.1 MOTIVATION
The existing time reversal sampling methods can be interpreted as a sampling procedure in which each denoising path approximately minimizes the following loss function L::
Cstarts Cend. σ)
| (2;; 0r Catan)
E Gend 2112
(a')) (#/
(11)
Tt
dt
|
of
Here, the objective of Eq. (1I) is to enforce the consistency between path and temporally reversed path in both directions by optimizing the noisy samples ar as follows:
arg min L(r:; 0, Caant: Gend: a),
(12)
where a denotes the latent that minimizes the discrepancy between two temporal paths. How- ever, incompatible motion priors induced by two frame conditions introduce the ambiguity between the two denoising paths, especially in early denoising steps. Without resolving this problem, the loss & is optimized to make the misaligned path worse, causing implausible motions in generated videos, as illustrated in Fig- When there a significant gap between two motion priors, we could observe unrealistic motions like reverse play. Note that various types of visual artifacts come from incompatible motion priors, which will be discussed in Sec. 5.2
4.2 BIDIRECTIONAL PATH ALIGNMENT WITH MOTION PRIOR DISTILLATION
Since subsequent denoising steps primarily focus on restoring high-frequency details, previous works (Feng et al. [024] Yang et al.) (025a) often fail to correct this misaligned trajectory. To resolve this issue, we introduce a single path sampling scheme that distills the motion prior induced by the start conditioning frame Caart into the backward path.
Here, our key intuition is that the forward motion residual A the denoised estimates &0 contain useful motion information, which can be written as:
A@o.cum
(6) 0.e
(1-1) 0.csat
(13)
where E (2) denotes the frame index, given frames. Then, using the relation between Dg and eg in Eq. 2 the residual of noise from the forward path Aeswd is given
Aæ.
Amo
ACwwd
(14)
T₁
where Ax; = x x (i-1) represents the residual of the noisy sample o;, Now, given the encoded latent Rend of the end frame Lend, we initialize the first index of the backward denoised estimate $6,cm (1) with Zend as:
(zt)
Zend
hadd
(15)
Øt
Next, we reconstruct the backward noise residual Cbwd by cumulatively subtracting the forward noise residual from the initial backward noise bawd
Bww
bbw
Σ
(k) Ae
(16)
2=
It is noteworthy that should ignore the end frame condition Cend- Therefore, reformulate Eq. as follows:
Lo x. O, Ctwd-
(17)
Here, the reconstructed Cowa from the residual of the forward noise Aecvd provides with de- noised estimate dg from cumm which implies the flipped motion prior of Csun- To curb off-
manifold behaviors, we adopt CFG++ (Chung et al. 2025) following ViBiDSampler (Yang et al, 2025a).
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Algorithm 1 MOTION PRIOR DISTILLATION
Input: 2T (N.00 of Sstart Zend. for) hyperparameters X, k, y
Output: improved inbetweening result æo
1: Cuar, Gend f- encode( sxtut; Zend)
2: fo ===1 T:
(1- do
3: for 1==:koo do
4:
20,0, Co
Do( o, Cstant) (6-1)
denoise forward path with Cann (Eq.
5:
Az) t- zi
x
b forward path residuals
(1) s- F. Dumn
(6)
13
6:
forward denoised estimate residuals (Eq.
13
7:
Arwwd 6 (Axr Adooccmn //11
forward noise residuals (Eq.
14
8:
Φ f ftip(xt))
> temporal
flip
9:
bwd ((2r)) (1) zend)/ot
(1)
initialize first index of 6bwd (Eq.
15
10:
bud
(4)
- bad -Ek=2 Acowd
fwd
reconstruct Clwi (Eq.
16
Fo, cim t- 8tt -44bbrl
b reconstruct H ca (Eq.
12:
Bo.com f (1 1) + 1(
D. fuse two estimates (Eq.
18
13:
1t-1 D0.con 2-1 (0t 20,0)
update with Euler step (Eq.
19
14:
Dt = x1-1 + V 03 oi-1c
>> re-noise
15:
end for
16:
end for
Consequently, the Euler step of SVD in Eq.
(4 denoises the sample æy;
(18)
-t-1 Dp + 01-1 (xx, 20,e);
(19)
where À E [0, 1] serves as the interpolation scale. Note that during this process, we intentionally do not denoise the temporally backward path with the end frame condition Cend- This enables the direct transfer of the forward motion prior toward the end-frame constraint without introducing additional sources of misalignment. In addition, the proposed update in Eq. (19) can be seen as satisfying the proposed objective Eq. II in relaxed form. In our single-path update, the end-conditioned estimate is effectively replaced with the reconstructed estimate ákoet... distilled from the forward motion prior. Thus, the loss that we define in Eq. (11) is simplified as:
C(a:; O, Caant; Catart
20 (#%.c.
(20)
By replacing the end frame condition Gend with canns we can reduce the gap between the two temporal paths only with the start frame condition Cstant
i arg min L(x; 8, Catart, Caun o).
(21)
This reformulated loss shows that the backward path longer introduces an independent motion prior; instead, it is aligned through the forward motion prior. This gives the denoiser De the oppor- tunity to reconcile the original denoised path with its reconstructed counterpart within a timestep. producing a more stable trajectory.
4.3 PRACTICAL CONSIDERATIONS
Specifically, we disable MPD in later denoising steps. This choice follows the observation that dif- fusion sampling proceeds in a coarse-to-fin fine manner: early denoising steps with large primarily represent global and low-frequency structure, whereas later denoising steps refine high frequency details (Rissanen et al. 2023 Kim et al. 2023 Wu et al. 2025). Complementary studies (Lee et al.) 2025b Park et al., 2025) further demonstrate that focusing guidance on these early steps yields better visual quality. Thus, modifying the motion prior is most effective when the trajectory is still being shaped globally. Motivated by these findings, we apply our method during the early denois- ing stage with additional re-noising steps A > 0 steer the trajectory onto the correct direction. Then, we switch to existing time reversal samplers to enhance endpoint consistencies, which will be discussed in Secs. 5.3 and 5,4 More details of MPD provided in Algorithm [

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Table 1: Quantitative comparison results on DAVIS and Pexels dataset. We compare against six baselines. Ours TRF and Ours + ViBiD refer to our method applied to the parallel and sequential time reversal sampling schemes, respectively. Best results are bold, and second-best are underlined.
DAVIS
Pexels
Method
LPIPS
FID
FVD
VB 1
VB++
LPIPS
FID
FVD
VB
VB++
FILM
0.2946
55.160
1058.0
0.7978
0,9740
0.1157
43,935
761.60
0.8231
0.9734
DynamiCratter
0.3158
46.739
678.92
0.7475
0.8735
0.2397
62.598
809.53
0.8211
0,9213
TRF
0.3127
56.894
674.31
0.7618
0.9352
0.2044
59.185
796.48
0,8008
0,9487
G1
0.2432
48.427
654.91
0.7747
0,9320
0.1114
47,990
476.93
0.8211
0,9566
FCVG
0.2347
38.997
621.82
0.7904
0.9353
0.1
35,269
525,08
0.8245
0,9701
ViBID
0.2492
39.885 89.883
559.49
0.7733
0.9387
0.1447
39,002
641.30
0,8130
0.9488
Ours TRF
0.2212
34,910
612.17
0.7992
0.9330
0.1149
34,470
460.99
0,8503
0.9862
Ours ViBiD
0.2220
37.241
$27.05
0.7845
0.9474
0.1028
34.775
412.66
0.8235
0.9605
5 EXPERIMENTAL RESULTS
5.1 EXPERIMENTAL SETTINGS
Evaluation dataset. Following the previous works (Yang et al. 2025a Wang al. 2025b),
compare our method with relevant SOTA methods on two representative datasets. Specifically, we utilize 100 video-keyframe pairs from DAVIS dataset (Pont-Tuset et al. 2017), and 45 from Pex- els [1 To simulate typical inbetweening conditions where long-range temporal reasoning is required between sparsely spaced keyframes, those videos exhibit diverse and large motions such as driving, dancing, and so on.
Implementation details. We plug our method into both TRF (parallel) and ViBiD (sequential) building on SVD-XT model of SVD (Blattmann et al., (223a)) on a single NVIDIA RTX 4090 GPU. For the sampling process, we use the Euler scheduler with 25 timesteps with the default settings of SVD. Additionally, we configure each TRF and ViBiD with our settings: interpolation scale À as 1.0 and 0.5, the number of re-noising steps as and 3, and the distillation step ratio yy as 0.3 and 0.2.
5.2 COMPARATIVE RESULTS
As comparison methods, we choose representative time reversal sampling-based methods: TRF (Feng et al.) 2024), GI (Wang et al., 2025b), FCVG (Zhu et al., 2025), and ViBiD (Yang al.) 2025a). We also include a flow-based VFI model, FILM (Reda et al. 2022). and a recent generative VFI model, DynamiCrafter (Xing et al. 2024b), for a broader comparison.
Quantitative results. For quantitative evaluations, we use metrics for video frame interpolation, including FID (Heusel et al., 2017). FVD (Unterthiner et al. 2019). LPIPS (Zhang et al.] 2018). FID and FVD measure the distances of generated frames/videos over ground-truie sequences. LPIPS as- sesses perceptual similarity at frame level. We also evaluate the overall quality of the videos using VBench (Huang et al. 2024a) and VBench++ (Huang al., 2024b). VBench provides a compre- hensive assessment across multiple dimensions such as subject consistency, background consistency, aesthetic quality, image quality, motion smoothness, and temporal flickering. VBench++ typically evaluates comprehensive performance of videos with single reference frame. Because inbetween- ing must treat both endpoints symmetically, we compute VBench++ for start and end frame each, then average them.
As shown in Tab. [ our method consistently outperforms the time reversal sampling- based methods, TRF and ViBiD, across all metries. In particular, our method achieves significant improvements in terms of FID and FVD scores, highlighting its ability to produce temporally coherent sequences with smooth motion. For VBench++, FILM gets slightly better scores than our methods in DAVIS dataset. However, this can be attributed to flow-based warping that preserves local structures near each endpoint. This comes with blurry artifacts and weaker long-range temporal consistency, which yields the lower FVD score. Overall, our method effectively addresses the issue of confficting motion priors and achieves both fidelity and perceptual quality over SOTA methods.
http://wwwwppxcl..com/
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4
la
42
417
225
ViBiD
Ours
1/2
1zo
125
FCVG
Ours TRF
Ours ViBiD
Figure 3: Qualitative baseline comparisons. TRF and ViBiD suffer from back-and and-forth motion and intermittent disappearance, while GI and FCVG exhibit noticeable artifacts and ghosting effects. Our method yields more temporally consistent motion than the comparison methods. Additional examples are provided the project page.
Qualitative results. As shown in Fig. tion than the comparison methods. In the first group, TRF and ViBiD fail to preserve the child's forward-walking trajectory. Near the end frames, the child appears walk backward partially vanish, indicating the misalignment issue between the two paths. In the second group, GI and FCVG exhibit oscillations and ghosting artifacts. In particular, FCVG, relying on line matching, results in ambiguous artifacts, which is observed with the skier. In common, the comparison methods en- counter difficulties when subjects' motion orientations are similar in both the start frame and end frame. In contrast, we validate that injecting forward motion residual into the backward path is enough to represent desirable object motions with fewer artifacts.
method produces more temporally consistent
User study. To further evaluate human preference beyond quantitative metrics, we conduct com- prehensive user study via Amazon Mechanical Turk (Crowston) 2012) following prior works (Shin al.) 2024 2025). Each participant is presented with pairs of the start and end frames, followed by randomly candidate videos generated by different methods. To avoid ordering bias, the display order is randomized for every sequence.
Our user study is designed with three types of questionnaires: (1) Ranking: participants are asked to rank videos in order of overall naturalness and temporal coherence, focusing how plausibly the generated sequence links the start and end frames. These rankings are converted into scores in recip- rocal order, ranging from 3.5 to -3.5. (2) Artifact detection: participants are asked to select all videos
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Table 2: Comparison results of user study. Best results are bold, and second-best underlined.
Mathod
Alignment
1
Artifact +
Unrealistic
Method
Alignment 1
Artifact
Unrealistic
FILM
-0.460
62.74%
54.76%
FCVG ViBiD
0.0988
20.36%
19.17%
DynamiCrafter
0.0190
34.64%
37.14%
0.06788
28.10%
25.24%
TRF
0.3119
28.09%
25.24%
Ours
0.3060
22.62%
0.1179
22.26%
13.57%
0.2440
8.93%
1
19
113
1
0.2
0.4
0.6
0.8
1.0
Ablation study the effect of distillation ratio 7 We vary the distillation step ratio E (0.2, 0.4, 0.6, 0.8, 1.0). where y = 0.2 corresponds to the default setting and = applies our method at every denoising step.
Figure 4:
that exhibit noticeable visual artifacts, such as distortions, ghosting, or inconsistent textures. (3) Un- realistic motion identification: participants are asked to choose all videos that contain unrealistic or physically implausible movements, which are closely related to perceptual plausibility.
We collect responses from total of 30 participants across 28 randomly sampled video groups to ensure the statistical reliability of the study. As shown in Tab. 2] our method achieves the highest preference in the ranking task, while being selected least frequently in both the artifact and un- realistic motion categories. These results demonstrate that our approach outperforms competitive baselines in terms of perceptual plausibility and human preference, providing strong evidence of its effectiveness in practical scenarios.
5.3 ABLATION STUDIES
We conduct ablation studies on DAVIS dataset to evaluate the impact of distillation step ratio y, re-noising steps k, and interpolation scale X. The results are summarized in Table
Within the parallel approach, LPIPS and FID are minimized at y 0.3 and 1 2, whereas FVD prefers weaker distillation and fewer re-noising steps. This is because TRF fuses the two conditional paths at every step, and the opposite motion prior is continually re-introduced after MPD, making the process more sensitive. Averaging the two paths partially cancels out the conflict and improves temporal coherence, while stronger MPD process can favor the frame-level fidelity at the cost of temporal smoothness.
For the sequential time reversal sampling, we observe clear and consistent optimum = 3, and À = 1.0, achieving the best scores. This indicates that strong early single- -rieerrrooooo distillation with interpolation is beneficial for the sequential method. Once the backward path is aligned to the forward motion prior the early phase, subsequent steps rarely introduce conflicting priors, so increasing steadily helps improve the temporal and perceptual quality of videos.
= 0.2,

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Table Ablation results for distillation steps ratio 7, re-noising steps and interpolation scale A. (a) (c) correspond to Ours + TRF, and (f)-(f) correspond to Ours + ViBiD. Best results are bold.
(a) Distillation step ratio (y)
(b) Re-noising steps (i)
Interpolation scale (A)
LPIPS
FID
FVD
LPIPS
FID
FVD
LPIPS
FID
FVD
0.2212
34,910
612.17
0,2246
35.149
588.27
0.2211
34,910
612.17
0.2238
35,408
576.01
0.2212
34,910
612.17
1.0
0.2264
35.612
654.72
0.2236
35.268
573.13
0.2248
35.765
662.75
(d) Distillation step ratio (r))
(e) Re-noising steps (k)
(i) Interpolation scale (A)
LPIPS
FID
FVD
LPIPS
FID
FVD
LPIPS
FID
FVD
0.3
0.2421
39.855
574.05
0,2379
39.961
568.06
0.2242
37.837
$39.99
0.2220
37.241
527.05
0.2341
39.368
545.25
1.0
0.2220
37.241
527.05
0.2374
39,949
545.25
0.2220
37.241
527.05
5.4
THE EFFECT DISTILLATION RATIO
We conduct another ablation study on the variation of the dis- tillation step ratio y up to 1.0. As shown in Tab. increasing consistently leads to worse scores across all metrics. The cropped examples in Fig. further show that applying our method with 0.3 does not further improve motion consis- tency, but rather introduces undesirable pixel-level biases and degrades visual fidelity. Both quantitative and qualitative stud-
Table 4: Efffet of the distillation ratio Best results are bold.
LPIPS 4
FID
FVD
0.2
0.2220
37.241
527.05
0.4
0.2478
45.636
634.11
0.6
0.2562
55.873
813.08
ies support our choice to apply our method in the early stage of sampling, rather than throughout the entire denoising process.
0.8
0.2679
68.007
973.64
1.0
0.2721
75.544
1086.6
5.5 COMPUTATIONAL EFFICIENCY
We compare computational cost with other 12V diffusion based methods, as summarized in Tab. DynamiCrafter requires additional training on 12V diffusion model for the generative inbetween- ing task. Likewise, GI and FCVG rely on fine-tuning SVD models, which also require substantial computational resources. During inference, our method denoises the temporally forward path and then adds few extra re-noising steps to make the two paths align. Due to these steps, the infer- ence time can be slightly longer than FCVG and ViBiD. However, this small extra cost yields better alignment and fewer artifacts in generated videos.
Table 5: Comparisons on computational efficiency.
Method
Train
Inference time (s)
VRAM Usage (GB)
Resolution
DynamiCratter
26
11.2
16 512 x 320
TRF GI FCVG ViBiD
429
13.6
25 x 1024 576
663
23.4
25 x 1024 x 576
134
24.1
25 x 1024 576
108
19.2
25 1024 576
Ours + TRF
143
19.2
25 x 1024 576
Ours + ViBiD
141
19.2
25 x 1024 576
CONCLUSION
In this work, we analyze the bidirectional path misalignment problem in existing time reversal sam- pling through the lens of optimization problem. Based on the analysis, we present Motion Prior Distillation (MPD), a training-free sampling method that resolves motion prior conflict in exist- ing time reversal samplers to enhance generative inbetweening task. MPD replaces two conflicting temporal priors with single coherent motion prior from the start frame, and distills it through the backward denoising path, yielding a coherent trajectory that satisfies both endpoint constraints. By integrating MPD into time reversal sampling methods, demonstrate that MPD synergisti- cally reduces temporal discontinuities and visual artifacts, and achieves more appealing results both quantitatively and qualitatively than SOTA methods.
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ACKNOWLEDGMENTS
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grants funded by the Korea government (MSIT) (RS-2020-1120133 and RS- 2025-254418395 by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) RSS-2024-0338443), and by the Yonsei University Future-Leading Research Initiative of 2025 (2025-22-0409).
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A
ANALYSIS ON DENOISED ESTIMATES
4
13
119
125
MdD a/M Il 0557 Aeoeerro
Wi MPP3
(t 0..51
backward foo
Figure I: Analysis on denoised estimates. At the midpoint of time reversal sampling, we take the forward and backward denoised estimate, align the latter to the temporal order, and inspect their difference.
For clearer understanding of our motivation in Sec. we conduct an additional experiment in which the forward and backward denoised estimates, Do,com and Lh,e,, are obtained at an inter- mediate time step 0.5T), both with and without applying our method. Note that the backward denoised estimate is temporally flipped to enable direct comparison. As shown in the first two rows of Fig. [ existing methods produce intermediate frames with implausible motion. The forward and backward trajectories attempt to reflect two incompatible motion priors simultaneously, revealing clear motion conffict. In contrast, as shown in the last two rows of Fig. ! our method maintains a consistent motion trajectory in both temporal paths, generating coherent intermediate frames without such conflicts.
B FINE-TUNING METHODS WITH TIME REVERSAL SAMPLING
In this section, we deeply discuss the fine-tuning methods that incorporate the time reversal sam- pling strategy. Existing fine-tuning approaches (Wang et al.] 2025b Zhu et al. (025) share two major limitations. First, they still rely on incompatible motion priors at inference time, so mis- matches between forward and backward trajectories are not fundamentally resolved. Second, both methods require additional min-tuning, which demands substantial computational cost. In contrast, method aligns the two temporal paths without additional training, and integrates into existing time reversal samplers with only a minor change to the sampling loop.
GI (Wang et al.) 2025b) improves backward motion fidelity by fine-tuning SVD through rotated temporal self-attention maps. While this design encourages consistency between two paths, the two paths are still driven by different motion priors. Additionally, the backward-motion network is trained only on a small collection of videos, which may not fully capture the diverse backward motion patterns. As shown in Fig. VI this lead motion conflicts such as two surfers being generated, even though there must be one. Instead, our method reconstructs a backward estimate directly from the forward motion residuals, so that the backward path no longer introduces its own motion prior but instead follows the time-reversed motion induced by the start frame, leveraging the faithful forward motion prior of SVD.
FCVG (Zhu et al. 2025) adopts frame-wise conditions by extracting matched line segments from the two keyframes. Then, they interpolates the frame-wise correspondences over time, and then feeds them into SVD as the guidance. This works well when scenes contain strong, well-defined structural edges, but it has practical limitations. The method is highly sensitive to the quality of the line extraction and matching pipeline. Thus, failures in detection or matching directly lead to unstable or distorted interpolations. As shown in IV such failures appear as noticeable ghosting artifacts on the carrier.
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C
EXPERIMENTS ON LARGE TEMPORAL GAPS
4
h1 l21
131 441 151
TRF
Ours TRF
Figure II: Qualitative results over 50-frame gap.
We conduct additional experiments on large temporal gaps, where the two keyframes are separated by 50 frames. In this challenging setting, both the previous time reversal sampling methods and our method exhibit noticeable degradation in generated videos. However, our method still reduces severe ghosting and back-and-forte motion compared the baselines, thereby producing intermediate frames. As shown in Fig. M1 TRF often yields duplicated or intermittent legs, whereas incorporating method with TRF generates
plausible
coherent inbetweening results.
D DISCUSSIONS ON EFFECTIVE AND CHALLENGING SCENARIOS
1
l9
117
125
(a)
(b)
Figure III: Failure cases. Our method struggles when (a) the end keyframe introduces entirely new objects or (b) the scene undergoes aarge-scale rearrangements between two keyframes.
One of the key components of our method the endpoint initialization in Eq. (15) that initializes the backward noise ebwd using the latent encoding of the end frame Zend This implicitly assumes that the end frame of the forward path is reasonably consistent with the ground truth end frame. When the forward trajectory ends far from the ground truth end frame, Eq. (15) may become a less informative initialization. In such cases, the shared motion prior can be biased toward an inaccurate end frame, smalls residual discrepancies in object placement appearance may remain.
We experimentally find that our method is most effective when both keyframes contain the same object and share semantically coherent motion presented in Appendix E In such scenarios, the forward motion prior provides the reliable global trajectories, and distilling it into the backward path successfully reduces undesirable artifacts. However, as discussed in previous works (Wang al., 2025b Zhu et al. 2025), MPD still struggles with the inevitable problems that arise when the end frame introduces entirely new objects or undergoes large-scale scene rearrangements. Representative failure cases are presented in Fig.
To better handle these challenging scenarios, we are currently exploring extensions our approach on large-scale 12V diffusion models such as Cog VideoX (Yang et al.) (2025b), which adopt DiT- based architecture (Peebles Xie 2023) and can generate over 80 frames.
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E
ADDITIONAL QUALITATIVE RESULTS
4
17
113
119
25
TRF
GI
ViBiD
FCWG
Ours TRE
Ours + VIBID
1
17
113
119
125
TRE
G
VIBID
FCVG
Ours TRF
Ours VIBID
Figure IV: Additional comparison results with baseline models.
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4
17
13
1
125
TF
G
VIBID
FCVG
Ours TRF
Ours ViBiD
1
17
113
119
125
TRE
ViBID
FCVG
TRF
Ours
Ours VIBID
Figure V: Additional comparison results with baseline models
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17
13
1
125
TRF
GI
VIBID
FCVG
Ours TRF
Ours ViBiD
1
17
113
119
125
TRF
GI
VIBID
FCVG
Ours TRF
Ours VIBID
Figure VI: Additional comparison results with baseline models
19


Reviews:
[{"review_id": "R1FskRut1E", "reviewer": "Reviewer_CGhH", "summary": "This paper addresses the issue of motion priors misalignment when adapting image-to-video diffusion models for generative in-betweening. Unlike existing adaptation methods, the proposed approach distills the forward motion prior conditioned on the first input frame into backward motion prior, eliminating the need for conditioning on the second end frame. Experiments demonstrate that this simple yet effective motion prior distillation achieves better performance compared to baseline methods.", "questions": "1. GI (Wang et al., 2025b) also addresses the motion prior misalignment issue by sharing temporal self-attention maps in a time-reversed manner. Based on the video results, both GI and the proposed method appear to perform similarly for rigid motions, such as those involving cars or boats. I am curious about the specific advantages of the proposed approach over GI in practical application scenarios.\n\n2. I also wonder how the proposed method performs when the two end frames are farther apart—for example, 50 frames. In such cases, using an image-to-video model capable of generating longer sequences (e.g., 50 frames), would the proposed method remain effective? Such a setting might further amplify the limitations discussed in the weaknesses section, as the motion priors will be more ambiguous over longer sequence.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper tackles a well-defined problem: the mismatch of generated motion priors that occurs when conditioning the image-to-video denoiser on each end frame during the adaptation of image-to-video models for generative in-betweening. The novel aspect of the proposed approach lies in converting the forward motion noise estimate into a backward motion noise estimate using per-frame motion residuals. This design allows the backward motion to be learned without relying on denoiser estimates conditioned on the second end frame, which could otherwise generate a complete different motion path that is difficult to align with the forward trajectory.", "weaknesses": "When the end point of the forward motion prior path is far from the  second input end frame,  Eq (15) would not be a good initialization. The paper should discuss this limitation and include examples to to illustrate the scenarios in which the proposed method is most effective and scenarios in which is not effective.", "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": "AFUjA31GzN", "reviewer": "Reviewer_mj1L", "summary": "This paper aims to solve the task of generative inbetweening, using the well-established image-to-video diffusion models. This paper tries to solve a problem of motion conflict when using time reversal sampling, which is the misalignment of motion paths estimated from two different directions of predictions - forward and backward. Existing methods try to fuse the estimations of both directions, and did not focus much on fixing possible misalignment issues. The authors propose Motion Prior Distillation, which use the residual noise predictions estimated from the forward path and distill them to the backward path in reverse, to match the predicted paths of both directions.", "questions": "- In lines 73-74, could the authors provide a little more explanation on how connecting the forward and backward paths differ from aligning them? Connection of forward and backward paths would inevitably require alignment, and I feel unsure of what this precisely means.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The problem definition is clear.\n- The results of the proposed method seem to be strong, outperforming existing work.", "weaknesses": "1. According to my understanding, in short, the proposed method aims to model the noise residual between frames, especially from the forward path, and distill it to the backward path for alignment. However, I feel quite unsure how this could well-align the motions. Figure 1 (c) seems to be a good description, and this also display my concern. In Fig.1(c), the position of the red car in the forward path and the backward path differ. To be more specific, in the backward path, starting from the end (green car), using the residual distilled from the forward path ends up in a different location of the red car, which is different from the reference start frame (red car). To my understanding, I believe that it is possible to cause a misalignment, and I wonder how this could rather resolve misalignment and motion conflict. Fusing the estimates of two paths may be a part which handles this part, but in that case, I am not sure of how the proposed method could theoretically contribute to the problem of resolving motion conflicts. Could the authors provide further explanation and clarify on this?\n2. Theoretically speaking, I understood the problem definition described by the authors in Sections 3 and 4.1. However, I am not following whether this is a “motion” “conflict”. For instance, Fig.1(d) is a good example to support my point. To my perspective, the forward path and the backward path both seem to locate the cars at similar positions, aligned quite well, despite blurry and unsatisfactory quality. Despite being blurry, the position of the visual artifacts, which is presumably the location where the model tried to draw the car, seems to be aligned well. Thus I find this problem to be more of a failure of inserting the object (car), rather than failure of modeling motions. I think it is not a problem with “motion”, but a failure of object injection / disappearance; and think the motions of forward and backward paths are not in “conflict”, with good alignment. It could be a matter of how we define things, but currently, I think the word choice of definition is a little misleading. Could the authors provide some discussions on this?\n\nThe weaknesses mentioned above are more of a question / discussion, rather than flaws. With these questions above clearly resolved, I am open to raise the rating.", "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": "srXmyn0Pje", "reviewer": "Reviewer_6htp", "summary": "Inference-time sampling strategies for video interpolation, which leverage the generative priors of large-scale pretrained image-to-video (I2V) models without additional training (e.g., TRF and ViBiDSampler), often suffer from temporal discontinuities. These methods typically fuse or alternate between temporally forward paths (starting from latent $z_{start}$) and backward paths (starting from latent $z_{end}$). However, misalignment between the two paths, arising from differently conditioned motion priors, frequently leads to inconsistent temporal coherence.\n\nThis work proposes an inference-time distillation technique that transfers motion priors from the forward path (starting from $z_{start}$) to the backward path during the early sampling stage ($T \\rightarrow (1 - \\gamma)T$). Specifically, the method computes frame-wise residuals from the forward path and recursively injects these residuals into the end-frame latent $z_{end}$ to generate the backward path. As a result, the backward path is effectively constrained with $z_{end}$, while the diffusion prior is applied only to the forward path—thereby reducing misalignment and improving temporal coherence.\n\nAfter applying inference-time distillation in the early sampling stage ($T \\rightarrow (1 - \\gamma)T$), the method transitions to existing sampling frameworks such as TRF and ViBiDSampler, demonstrating that the proposed technique effectively enhances their performance.", "questions": "1) Clarification on the necessity of switching methods\n\nAs mentioned in Weakness 1, it is unclear why the proposed method cannot perform the full sampling process independently. The authors should clarify why it is necessary to switch to another sampling framework midway, rather than continuing the proposed method throughout the entire process.\n\n2) Additional ablation on distillation step ratio\n\nIt would be helpful to include additional ablation studies that extend the distillation step ratio up to 1.0. This would make it clearer how the proposed method behaves when applied across the full sampling range and help justify the chosen configuration.\n\n3) Quantitative efficiency metrics\n\nPlease report quantitative efficiency metrics such as runtime (s/frame) and VRAM usage (GB) to support the claimed efficiency and enable fair comparison with prior methods.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1) Good motivation and novel framework\n\nMotivated by the goal of resolving the bidirectional path misalignment problem, the authors propose a novel framework called Motion Prior Distillation. This approach injects motion information from the forward path into the end-frame latent $z_{end}$ to guide the generation of the backward path. As a result, the method constrains to terminate at the end-frame latent $z_{end}$ while utilizing only the forward-path motion prior, effectively mitigating bidirectional path misalignment. Using only the forward-path motion prior in this context is both novel and insightful.\n\n2) Comprehensive experiments\n\nThe authors clearly demonstrate that applying Motion Prior Distillation during the early sampling stage enhances the performance of existing inference-time frameworks such as TRF and ViBiDSampler. In addition, they conduct extensive ablation studies to explore the underlying principles of the proposed approach, which are appropriately designed and well-analyzed in terms of quantitative metrics.\n\n3) Zero-shot improvement\n\nIt is worth highlighting that the proposed method improves upon prior works while maintaining a zero-shot setting. This makes the contribution particularly relevant and appealing to researchers working on video diffusion and zero-shot video interpolation.", "weaknesses": "1) Limited explanation of early-stage application\n\nAfter carefully reviewing the submission, it remains unclear why the proposed method should be applied only during the early stage of the sampling process. The authors briefly attribute this choice to the “coarse-to-fine property of diffusion sampling,” but a more detailed explanation is needed. In particular, the paper should clarify why the proposed method is especially effective for correcting global or low-frequency structures, thereby helping readers understand the underlying principle of why the approach works well in the early stage.\n\n2) Additional ablation study on distillation step ratio\n\nBuilding on the previous point, an ablation study investigating different distillation step ratios, especially values larger than 0.3, would help substantiate the authors’ claims regarding the optimal range and behavior of the proposed method.\n\n3) Limited demonstration of computational efficiency\n\nWhile prior works typically report quantitative efficiency metrics such as runtime and VRAM usage, this paper lacks such evaluations. Including these measurements would make the efficiency claims more convincing and allow for fair comparison with existing approaches.", "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)