LVTINO: LAtent Video consisTency INverse sOlver for High Definition Video Restoration
Authors: Alessio Spagnoletti
Andres Almansa
Marcelo Pereyra
Venue: ICLR 2026 Poster (2026)
Keywords: Video Inverse Solver
Langevin sampling
Consistency Models
Video Interpolation
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We introduce LVTINO, the first PnP video inverse solver that uses Video Consistency Models (VCMs) as priors. Unlike frame-by-frame approaches, LVTINO ensures temporal consistency and high perceptual quality, achieving SOTA with just a few NFEs.

Abstract: Computational imaging methods increasingly rely on powerful generative diffusion models to tackle challenging image restoration tasks. In particular, state-of-the-art zero-shot image inverse solvers leverage distilled text-to-image latent diffusion models (LDMs) to achieve unprecedented accuracy and perceptual quality with high computational efficiency. However, extending these advances to high-definition video restoration remains a significant challenge, due to the need to recover fine spatial detail while capturing subtle temporal dependencies. Consequently, methods that naively apply image-based LDM priors on a frame-by-frame basis often result in temporally inconsistent reconstructions. We address this challenge by leveraging recent advances in Video Consistency Models (VCMs), which distill video latent diffusion models into fast generators that explicitly capture temporal causality. Building on this foundation, we propose LVTINO, the first zero-shot or plug-and-play inverse solver for high definition video restoration with priors encoded by VCMs. Our conditioning mechanism bypasses the need for automatic differentiation and achieves state-of-the-art video reconstruction quality with only a few neural function evaluations, while ensuring strong measurement consistency and smooth temporal transitions across frames. Extensive experiments on a diverse set of video inverse problems show significant perceptual improvements over current state-of-the-art methods that apply image LDMs frame by frame, establishing a new benchmark in both reconstruction fidelity and computational efficiency. The
code is available on https://github.com/LATINO-PRO/LVTINO.

Published as a conference paper at ICLR 2026
L LVTINO: LATENT VIDEO CONSISTENCY INVERSE SOLVER FOR HIGH DEFINITION VIDEO RESTORATION
Alessio Spagnoletti & Andrés Almansa Laboratoire MAP5, UMR 8145 Université Paris Cité, CNRS
F-75006 Paris, France alessio. wwwgnolettieetu.....o.
andres almansa@u parliaaarsi.... fr
Marcelo Pereyra
Heriot-Watt University, MACS & Maxwell Institute for Mathematical Sciences EH14 4AS, Edinburgh, United Kingdom
M. w..eeeyrr@hh.cc ac.uk
LATTIN
Figure 1: Results on joint tenipall super-resolution by factor x8.
ABSTRACT
Computational imaging methods increasingly rely on powerful generative diffusion models to tackle challenging image restoration tasks. In particular, state-of-the-art zero-shot image inverse solvers leverage distilled text-to-image latent diffusion models (LDMs) to achieve umprecedented accuracy and perceptual quality with high computational efficiency. However, extending these advances to high-definition video restoration remains significant challenge, due to the need to recover fine spatial detail while capturing subtle temporal dependencies. Consequently, meth- ods that naively apply image-based LDM priors frame-by-frame basis often result in temporally inconsistent reconstructions. We address this challenge by leveraging recent advances Video Consistency Models (VCMs), which distill video latent diffusion models into fast generators that explicitly capture temporal causality. Building on this foundation, we propose I. V the first zero-shot or plug-and-play inverse solver for high definition video restoration with priors encoded by VCMs. Our conditioning mechanism bypasses the need for automatic differentiation and achieves state-of-the-arr video reconstruction quality with only a few neural function evaluations, while ensuring strong measurement consistency and smooth temporal transitions across frames. Extensive experiments diverse set of video inverse problems show significant perceptual improvements over cur- rent state-of-the-art methods that apply image LDMs frame by frame, establishing a new benchmark in both reconstruction fidelity and computational efficiency. The code is available GitHub
VLVIIO is short for LAtent Video consisTency INverse sOlver.

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Published as a conference paper at ICLR 2026
INTRODUCTION
We seek to recover an unknown video of interest a (21,...,,2)) ........)))....... from a noisy measurement
y Az + n2,
where A is a linear degradation operator acting on the full video sequence, n is additive Gaussian noise with covariance o Id, and xt, RR denotes the 7th video frame.
We focus on video restoration problems that are severely ill ill-conditioned or ill ill-posed, leading to significant uncertainty about the solution. We address this difficulty by leveraging prior information about to regularize the estimation problem and deliver meaningful solutions that are well-posed. More precisely, we adopt a Bayesian statistical approach and introduce prior information by specifying the marginal p(æ). so-called prior distribution, which we then combine with the likelihood function p(yla (x) x exp( -1y 2 } by using Bayes' theorem to obtain the posterior
p(aly) = p(ylz)p(z)
plyl))p(()d
We aim to leverage a state-of-the-art generative video model as p(æ). In recent years, the use of deep generative models as priors in Bayesian frameworks has garnered significant attention, particularly
in computational imaging, where denoising diffusion models (DMs) have emerged as powerful
generative priors for solving challenging inverse problems (Song & Ermon 2019 [Song et al. | 2020)]
Chung et al. 2022 Kawar et al., 2022, Zhu et al. 2023 Song et al. 2023a Moufad et al. 2025).
For computational efficiency, modern DMs are often trained in the latent space of variational autoencoder ( (VAE), yielding Latent Diffusion Models (LDMs), which are now the backbone of widely used large-scale priors such as Stable Diffusion (Rombach et al. 202T] Podell et al.)) More recently, distilled diffusion models, and notably consistency models (CMs) (Song et al. 2023b Luo ct al., 2023a), have emerged as powerful alternatives, producing high-quality samples with only few neural function evaluations (NFEs), in contrast to the hundreds thousands often required by iterative DM-based methods. Several recent works have explored leveraging these models in a zero-shot, so-called Plug & Play (PnP), manner for Bayesian computational imaging (Spagnoletti
ct al. 2025 Garber & Tirer 2025 Xu et al.
2024
Li et al.) 2025).
Several powerful video DMs (Ho et
2022
B Blatmmnn et al. 2023b a Chen et al.
2023 Hong
et al. 2022) and fast CMs (Wang et al. 2023 Lv et al. 2025 Zhai et al. 2024 Yin et al.
2024b) have
recently been proposed, offering great potential for Bayesian video restoration. However, leveraging them remains challenging, so most current methods apply image DMs trame-by-frame and enforce temporal consistency through external constraints (Kwon& & Ye 2025a b). In challenging settings, this strategy leads to temporal flickering and incoherent dynamics, as it fails to fully capture inter-frame dependencies. This issue could be principle mitigated by operating directly with video DMs, but applying standard DM-guidand techniques such as DPS to video DMs requires computing gradients by backpropagation through the DM, which incurs a high memory cost (woon et al. 2025).
We herein present -ATINO, the first zero-shot or PnP inverse solver for Bayesian restoration of high definition videos, leveraging priors encoded by video CMs that capture fine spatial-temporal detail and causal dependencies. Morcover, by building on the recent image restoration framework of (Spannlettii ctt al. (2025). LVTINO provides a gradient-fret inference engine that ensures strong measurement consistency and perceptual quality, while requiring few NFEs and automatic differentiation.
BACKGROUND
We begin by revisiting the core concepts underlying DMs and LDMs, and briefly discuss their recent extension to generative modeling for video data, which we will use as priors in LVTINO.
Diffusion Models. (DMs) generative models that draw samples from distribution of interest
(x) by iteratively reversing a "noising" process, which designed to transport To (x) to a standard
normal distribution (Sohl-Dickstein et al. 2015 Ho al. 2020 (Son al. 2020
Song & Hrmon
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2020 In the framework of Ho al. (2020), the noising and reverse processes are given by the SDEs:
3,
dx. = dt + v B.ddw..
2
31
dx, =
2 ae V. log Te (2i) dt + v Brdwor
where Be is the noise schedule, and the score function Va, 1 (wz)) which encodes the target To. is represented by a network trained by denoising score matching on samples from no (Vincent 201I). For computational efficiency, modern DMs rely heavily on (deterministic) probability flow representation of the backward process
(2) given by the following ODE (Song et al. (020):
Br 22
3r VVa. m. log Te (xx) dt.
dæ.
Latent Diffusion Models. LDMs dramatically increase the computational efficiency of DMs by operating the oow-dimeniional latent space of an autoencoder (E, D), rather than directly in pixel space (Rombach et al. 202I). This substantially reduces compute and memory costs, enabling models like Stable Diffusion (SD) to generate large images and video (Podell et al. Wang et al. 2025).
Video Diffusion Models. Extending DMs to video is an active area of research, requiring models to capture temporal coherence and causality. Below, we highlight some key contributions to this field:
Ho al. (2022) introduce spatiotemporal U-Net-based DM tailored for video generation. Their architecture applics 3D convolutions to jointly process space and time, integrates spatial attention blocks for fine-grained detail, well as temporal attention layers capture inter-frame dependencies.
Blattmann et al. 2023b a propose to repurpose pre-trained LDMs to video through the incorporation of trainable temporal layers 1," into a frozen U-Net backbone. The temporal layers reshape input batches into a temporally coherent sequence of frames by using temporal self-attention mechanism.
Wang et al. (2055) introduce state-of-the-art video foundation model built on three components: (i) Wan- VAE, lightweight 3D causal variational autoencoder, inspired by Wu al. (2024), that compresses video E [I+++)) into latent tensor IR (1+ 4/ 8/ Xxx while
ensuring temporal causality; (ii) a Diffussio Transformer (DiT) Peebles & Xie (022) that applies patchification, self-attention, and cross-attention model spatio-temporal context and text condition- ing: and (iii) a text encoder (umT5) Chung et al. (2023) for semantic conditioning. This architecture enables efficient training and scalable generation of high-resolution, temporally coherent videos.
Consistency Models. Consistency Models (CMs) are single-step DM samplers derived from the probability-fovw ODE (3). They rely on a so-called consistency function f (æ,, )) >> By that maps any state x, trajectory {æ; hejn, K] of (3) backwards Ty,, for some small 17 > 0, ensuring f(z,,) t) = far t') for all t, t' E [n. K]. Two-step CMs achieve superior quality by re-noising By = f(x f) following ①1 for some intermediate time s E (1), K). followed by f(x,, s) to bring back close to the target To. Multi-step CMs apply this strategy recursively in 4 to 8 steps, combining top performance with computational efficiency (Song et al. 2023b Kim et al. 2024).
Latent Consistency Models. CMs can also be trained in latent space by distilling pre-trained LDM into a latent CM (LCM) (Luo et al. 2023a b). A particularly effective distillation strategy Distribution Matching Distillation (DMD) (Yin et al. 2023), which trains generator Go to match the diffused data distribution by minimizing a KL divergence over timesteps, using frozen teacher DM as reference. Its improved version, DMD2 (Yin al. 2024a), adds a GaN-based loss to further enhance fidelity, and enables few-step samplers (e.g., 4 steps) by conditioning Gg on discrete timesteps 1, In practice, Go often initialized from pre-trained SDXI. model (Podell et al.). We use DMD2 (Yin et al. 2024a) within our video prior, as prior distribution on individual video frames.
Recent advancements have extended CMs to video generation. Wang et al. (2023) propose VideoLCM, the first LCM framework for videos, derived by distilling pre- trained text-to-vided DM; it can generate temporally coherent videos in as few as four steps. (Yin et al. (022b)) present theoretical and practical framework to convert slow bidirectional DMs into fast.
Video Consistency Models.
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ruto-regressive video generators. This conversion enables Crame-b--framm causal sampling, allowing generation of very long, temporally consistent videos. Our proposed LATINO method incorporates the CM variant of Wan (Wang et al. 2025), distilled via DMD (Yin et al. 2023), into our video prior to effectively capture subtle
dependencies and long-range temporal causality.
Zero-shot (plug & play) posteror sampling. Zero-shot methods leverage a prior model p(æ) (implicit in pretrained denoiser or generative model) and the known degradation p(y| (z)) to obtain an estimate of the posterior distribution p(æly) yy) x p ( p(x). Whereas early zero-shot literature
concentrates in maximum posterioni point estimators (Venkatakrishnan al. 2013 Monod et al.
2022). we concentrate here on producing samples from the posterior p(z]y) y). This has been addressed
by combining prior and likelihood information in various ways, like the split Gibbs sampler (Vono ct al. 2219), a discretization of the Langevin SDE (Laumont et al. 2022), a guided diffusion model
(Chung et al. 2022 Zhu al. 2023 Song et al., [023a]] K Kwon Ye
2025a
b Kwon et al. 2025 or
a guided consistency model (Spagnoletti et al.) | Garber & Tirer 2025
Xu el al. 2024
Li et al.
2025 which is the approach we pursue in this work.
LATINO (Spagnoletti et al. 2025) constructs a Markov chain approximating a Langevin diffusion æ, targeting p( (y) by using the following splitting scheme:
Ik +
V ((., ds + v2 dw Zo Fk
(4)
1k+1 = u + 0g V log
(5)
with step-size dz. Note that the first step corresponds to an overdamped Langevin diffusion targeting the prior p(æ:), while the second step incorporates the likelihood via an implicit Fuler step.
In order to embed an LCM (E. Jo) as prior p(z), LATINO replaces (4). which is intractable, with stochastic auto-encoder (SAE) step that applies the forward and reverse transports (1) as follows
z = v Van, E (xx) + v E
u = (So(z, 1k))
2k++1 = u + 6 V log (6g))))))11),
where we note that the SAE step preserves three fundamental properties of (4: (i) contraction of random iterates I3 towards the prior p( <);; ;; (ii) p(x) is the unique invariant distribution; and (iii) the amount of contraction is controlled via tg. which plays a role analogous to the integration step-size 5g. As demonstrated in (Spagnoletti al. (025). LATINO exhibits high computational efficiency, requiring only a few NFEs. By leveraging a ofate-of-thh-arr SDXL LCM (Yin et af.] (024a)) achieves remarkable accuracy and perceptual quality across range of challenging imaging tasks.
LATINO FOR HIGH DEFINITION VIDEO POSTERIOR SAMPLING
We are now ready to present our proposed LAtent Video consisTency INverse sOlver (LVTINO), which approximately draws samples from the posterior distribution
p(aly, JEZ p(yl=))((()) Ajdær
parametrized by the data text prompt c, and a spatiotemporal regularization parameter À E R$. As mentioned previously, LVTINO is zero-shot Langevin posterior sampler specialised for video restoration, which jointly leverages prior information from both Video Consistency Models (VCMs) and Image Consistency Models (ICMs). In addition, LVTINO is highly computationally efficient, requiring only a small number of NFEs and operating in a gradient-free manner, which significantly reduces memory usage and enables scalability to long video sequences.
A main novelty in LVTINO is the use of the following product-of experes prior for video restoration
p( A) x (x)c))) (ele)) (x))))
where 7) (0, 1) is temperature parameter and pv (ar (c), pr (o), and ps (x| are as follows:
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B0S Manssann days 02
aae fa (r .)
EV step noising SDE
proX 1.
prox nio. (
Dv(
84-18
1441/
510
CM son fld
Dr(-)
x++1
CN
SCE
ICM SAE
Figure 2: One step of the LVTINO solver, discretization of the Langevin SDE (7 which targets the posterior p( c, X), involving two stochastic autoencoding (SAE) steps and two proximal steps.
Py (alc) is implicitly defined via text-to-video LCM designed to capture subtle spatial- temporal dependencies well as long-range temporal causality. It is specified by an encoder-decoder pair (Ev, Dv) and consistency function fy operating their latent space.
pr(z|c) is implicitly defined via high-resolution text-to-image LCM, acting separately each frame, to recover fine spatial detail and enhance perceptual quality. It is specified by an encoder-decoder pair (E,, D1) and consistency function f% operating in their latent space.
(a|A) x exp (=) where Φx is convex regularizer promoting background stability and smooth temporal transitions across frames, with À R controlling the regularity enforced. Without loss of generality, in our experiments we use the total-variation norm
(x (2)
Σ (DR( X2 (D (D( T.O.i.j
where (Dn, D.) is the three-dimensional discrete gradient. Note that TV3 is not smooth.
Following a PnP philosophy, p(aly, c, A) combines an analytical likelihood function p(y ar) with prior distribution p(ae X) that is represented implicitly by pre-trained machine learning model. However, unlike conventional PnP approaches that exploit a denoising operator (e.g.. PnP Langevin (Laumont al. 2022)), LVTINO leverages the LATINO framework of Spaagnoletti al. (2025) which is specialised for embedding generative models as priors, notably distilled foundation CMs.
To draw samples from p(aly, c, A), LVTINO considers Moreau- Mosidu-Yosida regularized overdamped Langevin diffusion, given by the SDE
dæ. V logp(y/æz)ds + V log pi (xx/c)ds V loge/ (1-n) (z,|c)ds
+ boggyo AAdds + V2dw,,
where w, denotes n-dimensional Brownian motion and (. &. is the 7-Moreau-Yostee approx- imation of the non-smooth factor (xc A), given by (Pereyra 2016
(>)() sup (a(u) (- 2
x 2 11))
wER"
with 0. As mentioned previously, (x A) is log-concave and Lipchitz differentiable by construction because σx is convex on 12" (Pereyra) 2016). The likelihood p(y x) oc exp (- yy Ax ( is also log-concave and Lipchitz differentiable.
Under mild regularity assumptions on py (z]c) and (a((c). starting from an initial condition D.. the process I, converges n--eighborrooo of p(x kzly,c, A) exponentially fast as
(Luumont et al. 2022). While solving (6) exactly is not possible, considering numerical approximations of æ, provides powerful computational framework for deriving approximate samplers for p(ac c).
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LVTINO stems from approximating (6) by a Markov chain derived from the following recursion: given am initialization zy and a step-size ó > O, for all k 2 0,
1/+1/4 = ak + V log pv le)ds + v dws, Tip ark
VCM VCM prior
k 2k+ + V log p (y) (24 + V log Pay (xx+1/21 12)
implicit likelibood half step with reggularization
1/43/44 = #k41//2 %
V logpr( + 2 77) dw ão = 22/1/2
ICM prior
kk+ k 7)) Poy
implicit likelihood half-stop
where we identify a splitting in which each CM prior involved separately through exact integration (these integrals will be approximated through SAE steps), and the likelihood is involved through two implicit (backward Euler) half-steps
It is worth recalling that the Langevin diffusion is time- homogenoggssu process. The iterates resulting from its discrete-time approximation converge to neighhorhood of p(aly, e, A) as k - oo. Unlike DMs, these iterates do not travel backwards in time through an inhomogeneous process. Therefore, Langevin algorithms use directly the likelihood p(y|zz (z) x exp {- Ax 20/ J. avoiding the need to approximate the likelihood of y w.r.t. noisy version of as required in guided DMs like (Chung et al. 2022; Song et al. 2023a Kwon et al. 2025).
Following Spagnoletti et al. (225)) we compute 2/41/s and 3/4 approximately via SAE steps,
kk+1/1 = DV
f (Varuee (x( + / -1 aaaaa (V)
3243/44 = D' (5d (Vo,nn (x) + - (1)
where we recall that ( (') act frame-wise and that fy and f have model-specific schedules. explicit proximal point steps follows
The implicit Euler steps in 7 can be reformulated
02+1/22 = arg min (u) + ueR=
inf u ERR
@x(u') + 27 || u' 11 + 20n |x+t//4 - -u12.
RJ arg min (w) + Φ> (x) + 287 | - ull2
uER
Øx+1 arg min Gy (u) + .| I k/3
u11
WER*
where 0 ) log p .z) and where we have simplified the computation of Ø2+1/2 by assuming that y d17 (Pereyra 2016). The optimization problems described above are strongly convex and can be efficiently approximated by using a small number of iterations of specialized solver. In particular, to compute 1 we employ a few iterations of the conjugate gradient algorithm with warm-starting (Hestenes & Stiefel, 1952). For the computation of 12411/2, we recommend using proximal splitting optimizer (Chambolle & Pock (01IT) or a warm-started Adam optimizer (Kingma) & Ba, 2014), both of which are effective in practice. Please see Appendix A.6 for more details. Refer to Algorithm|] for more details about LVTINO, and to Figure 2 for schematic representation.
EXPERIMENTS
For our experiments, we implement LVTINO by using CausVid as VCM prior. We adopt the standard bidirectional WaN architecture, fine-tuned as CM. The model also supports an
Models.
Whiiee most Langevin samplers use the explicit steps and require 5 to he small, the implicit steps in 7 are stable for all 8 > 0, allowing I.VTINO converge quickly by taking 8 large, albeit with some small bias.

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Algorithm LATINO (LAtent Video consisTency INverse sOlver)
1: given degraded video y, operator A, initialization # A'y, video lenght 7 + steps N
2: given video CM (Ev. fa image CM (E,, (I, f f/). schedules (t/ (V) .k 8k. A k=0 By
N-1
3:
for k o....... - do
4:
# VCM prior half-step (temporal coherence)
5:
EV ~ M(, X///+///////////////
z (V)
vogy Ev(2+=1) V agg ev
6:
(
0xt%/ - -DV (= 1)
7:
> VCM
8:
First likelihood Solved with proximal splitting or Adam iterations
9:
1k+1/22 < arg w xw 9y 9y(u) + Φx xx + 26,9 | 2u12
if N then
11:
# ICM prior half-step (per-frame detail)
12:
E, ~ N(O, Idk//xx m/8xe)
0k+2/4 + stack Di Sá (V aa,n (x(+++1/2))) + v al ET, k2)
13:
b ICM
14:
# Likelihood prox (2nd) Solved with conjugate gradient iterations
15:
3t c- arg min uER(T () Jy( + 2b.(1-n) ||0 - u1l
16:
else
Final iteration: skip ICM and second likelihood
18:
k+11/2
19:
end
20:
end for
21:
return VN
autoregressive configuration, which we do utilize here, leaving the exploration of autoregressive priors for longer video restoration to future work. We use DMD2 as ICM as Spagnoletti et al. (2025). For our experiments,
use ( {757, 522, 255, 125) and ( (374, 249, 124, 63) for the VCM and ICM respectively. This results in a total of 9 NFEs, where applying the ICM across all frames counts as a single NFE. Regarding the text prompt specifying VCM and ICM, in the same spirit as K wwon & Ye (2025b), we do not perform any prompt optimization and instead use the generic prompt "A high resolution video/image" Exploring prompt optimization by leveraging the maximum likelihood strategy of Spaagnoletti et al. 2025) remains key direction for future work.
Dataset and Metrics. We evaluate methods on 435 video clips of 25 frames each from the Adobe24 4 0 dataset (Su et al., 2017), and 239 video clips of frames each from the GoPRO240 test dataset Nah et al. (2016). These datasets contain high-quality, high-frame-rate video sequences that we rescale to spatial resolution of 1280 x 768 pixels to match our targeted resolution.
We assess reconstruction quality using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) (Wang et al.] 2004). Additionally, we evaluate two perceptual metrics: Learned Perceptual Image Patch Similarity (LPIPS) (Zhang et al. 20018). along with the recently proposed Fréchet Video Motion Distance (FVMD) (Liu et al. 2024) which is tailored for assessing motion smoothness and perceptual quality in videos.
Inverse Problems. We consider three linear inverse problems for high-resolution video restoration.
Let æ (z E #I xC denote the unknown high-resolution video and y = .Aæ + n the observed degraded video with additive Gaussian noise n. For fair comparisons, we consider mild noise regime on = 0.001, which addresses the noiseless case.
Problem A Temporal SRx4 + SRx4: here A first applies temporal average pooling with factor 4 (reducing the frame rate), followed by frame-wise spatial downsampling by factor 4, simulating a low frame rate and low resolution video. Temporal upsampling to generate the missing frame is highly challenging here, as it requires prior knowledge of motion.
Temporal SR xk is also coarse (Riemann sum) approximation of motion blur due to moving objects camera during full continuous exposure between frames (Zhang 2021.
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Frame from measurement y
GT slice
LVTINO slice
VISION-XI. slice
Figure 3: Comparison between slices from 81 consecutive frames for Problem C (seq. C2). Slice images (i, r) are obtained from the video tensor (i, by fixing a column index shown green.
Problem B Temporal blur + SRx8: here A first applies uniform blur kernel of size 7 pixels along the temporal dimension, followed by frame-wise spatial downsampling by factor 8, simulating motion-blurred and low-resolution video (Kwon & Ye, 2025a b).
Problem C Temporal x + SR is a harder version of Problem A, where A first applies temporal average pooling with factor and then spatial downsampling by factor
Problem A: Temp. SR + SR
Probiem B: Temp. Mur + SR
Problem C: Temp. SRx + SRxs
NFE]
FVMD]
PSNRT
SSIMI
LPIPS]
NEE]
FVMD]
PSNR1
SSIM
LPIPS]
NEE
FVMDJ
PSNRT
SSSM
LPIPS]
LYTINO
21.78
ADMM-TV
02677
0.297
1262
21.18
18.15
Table 1: Results on the Adobe240 dataset across the three problems. Best results are in bold, second best underlined.
Problem
Temp. SRx SRx1
Problem B: Trmp. Mur SRx5
Probiem C: Temp. SRxS SRxs
Method
NFE
FVMDJ
PSNRT
SSIMF
LPIPS]
NFE.
FVMD
PSNRT
SSIM
LPIPS]
NER.
FVMD
PSNRT
SSIM
LPPPS]
LVTINO
189.4
24.01
0,275
0.315
2
46.20
22.46
1.687
0,435
2
232.6
22.91
0.677
0.445
VISION-XL
252.2
26,86
0.792
0.526
52.00
24J55
0.697
0.556
8
995.9
22.67
0.069
0,775
VIDUE
1
$4.45
20.66
0.37]
0.548
ADMM-TV
265
24.32
0.745
0.406
1459
20:83
0.618
0,527
969.3
17.70
0.631
0,527
Table 2: Results the GoPro240 dataset across the three problems. Best results are in bold, second
best are underlined.
Computational Efficiency. While NFEs provide a hardware-agnostic measure of complexity, practical de- ployment requires considering runtime and memory foot- prints. Table 4 reports the wall-clock time and peak GPU memory usage for restoring 25-frame video, measured on one A100 GPU, VISION-XL, by only loading an image model, exchanges memory usage for time, as it needs to perform sequentially each frame. LVTINO offers a com- petitive trade-off thanks to the VCM, which scales better for longer videos. Notably, the lighter variant LVTINO-V (see Appendix [A.6] for more details) achieves the fastest runtime among deep generative approaches with mod- erate memory cost, as it only loads the VCM component. While VIDUE only solves the frame interpolation problem under uniform motion blur, and, by working natively at lower resolution, it is not suitable for high-resolution applications.
Method
NFE
Time (s)
Mem. (GB)
LVTINO
9
132
35.15
VISION-XL
176
15.64
ADMM-TV
13.6
22.01
LVTINO-V
5
105
25.42
Table 3: Runtime and memory usage. Measured on single video clip 25 frames at 1280 x 768 resolution. Best results are in bold, second best are underlined. VIDUE is added light pre-trained reference.
lower bounds time and memory, it
Results.
Experiments in Table refer to Problems A, B and C, and are obtained with different numerical schemes for (7). We fix the hyperparameters per problem to better tackle the different
degradations; see Table in Appendix A.6 for more details and for an ablation study.
For the more challenging Problem C, stabilize and warm-start LVTINO, we use the joint deblur- ring/interpolation network of Shang et al. (2023 to produce a temporally interpolated version of y.
Whiic is trained the GoPRO240 train dataset Nah
2016
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which we then upsample via bilinear spatial interpolation that it can be used as initialization Zo- This warm-start allows us to reduce the number of integration steps, bringing the NFEs to 7. The same model, referred to as VIDUE, is used as a baseline comparison in Table and Table [
We further provide a visual analysis of motion quality using fixed vertical slices of video frames, following Cohen et al. (2024). who observed that spatiotemporal slices of natural videos resemble natural images. Figure] and Appendix B in Figures 11a and 11b show (i, r) slices. These reveal that even for small motions, LVTINO more closely preserves ground truth temporal continuity.
Qualitative and quantitative evaluation. Figures I 4 5 show the results of our algorithm
compared to the measurements, ground truth and VISION-XL (see also the videos by following the links in the captions). Table in Appendix B provides additional results. These results demonstrate that LVTINO yields more detailed and temporally coherent videos than VISION-XL The ICM prior enhances spatial detail, while the VCM prior and TV3* jointly improve temporal coherence, particularly in the challenging upsampling tasks B and C. For example, in Figure LYTINO achieves noticeably sharper results with minimal motion blur and strong temporal coherence, whereas VISION- XL shows a staircase effect with repeated frames and unresolved blur, also evident in Figure In Figure 5 VISION-XI exhibits temporal flickering, which our method eliminates via the VCM and TV models. Table supports these visual findings: LVTINO achieves strong FVMD and LPIPS scores, reflecting accurate spatiotemporal dynamics and fine spatial detail.
Other baselines. We also report comparisons with ADMM-TV, a classical optimization based method (we use the hyperparameters of Kwon & Ye (2025a)) We also considered comparing with VDPS (Woon et al. 2025). however the backpropagation through Wan's DiT and Decoder at resolution 1280 768 pixels required > 80 Gb of VRAM, exceeding the memory capacity of GPUs available in our academic HPC facility. Since LVTINO's conditioning mechanism does not rely on automatic differentiation, it has significantly lower memory usage. We do no include comparisons with the recently proposed zero-shot video restoration algorithm (Cao et al., 2026), which was developed concurrently with our work. Also, as our comparisons focus on zero-shot methodology, we do not report results for methods that are specifically designed and trained for a particular task,
like super-resolution (Liu et al. 2025) or space-time super-resolution (Wei et al. 2026).
CONCLUSION
We introduced I LVTINO, the first VCM-based zero-shot or PnP inverse solver for Bayesian restoration of high definition videos. By combining a VCM, a frame-wise ICM and TV3 regularization, LVTINO can recover subtle spatial temporal dynamics, as evidenced by its strong performance on challenging tasks and datasets involving both moving objects and camera shake. Moreover, LVTINO's s condi- tioning mechanism ensures strong measurement consistency and perceptual quality, while requiring as few as 8 NFEs and no automatic differentiation. We anticipate that upcoming advancements in distillation of VCMs will further improve the accuracy and computational efficiency of LVTINO.
Future research will explore sequential and auto-regressive Bayesian strategies for the restoration of long videos, well as better Langevin sampling scheme through the use of more sophisticated numerical integrators. Generative models for video tend to drift out of distribution after a few seconds (compounding errors), problem that has only be partially addressed very recently by clever mechanisms to combine long and short-term contexts (Yesiltepe et al., 2025 Xie et al., 2025). Another promising research direction is the incorporation of automatic prompt optimization by maximum likelihood estimation, as considered in /Spagnoletti et al. (2025) for image restoration tasks. Furthermore, it would be interesting to specialize LVTINO for particular tasks through the unfolding and distillation framework of Kemajou Mbakam et al. 2025).
ACKNOWLEDGGENTT AND DISCLOSURE OF FUNDING
MP acknowledges support by UKRI Engineering and Physical Sciences Research Council (EPSRC) (EP/Z534481/1) AS and AA acknowledge support from the France 2030 research program on artificial intelligence via the PEPR PDE-AI grant (ANR-23-PEIA-0004)) HPC resources provided

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Published as a conference paper at ICLR 2026
by GENCI-IDRIS Jean-Zay (Grant 2024-AD010014557) The authors grateful Murat Tekalp, Pablo Arias, Gabriele Facciolo, and Yaofang Liu for valuable discussions.
GT
LYTINO
VISIONALL N
Figure 4: Visual comparison for Problem A (seq. A1). The continuity of the motion is retrieved as the hand moves from right left. See full videos: LVTINO and VISION-XT.
GT
ONILAT
II-NOOSSA
Figure 5: Visual comparison for Problem B (seq. B2). The flickering problem is solved by LVTINO
(see darker and lighter area behind the chair). See full videos: LVTINO and VISION-XL
Gr
VisioNcal INtINo
Figure 6: Visual comparison for Problem C (seq. C2). The motion is retrieved by the reconstruction.
See full videos (81 frames for a better direct comparison): LVTINO and VISIÓN-XI
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A
APPENDIX
A.1 IMPLEMENTATION OF THE FORWARD OPERATORS
For all the inverse problems considered, we use the following formulation
A SpatialSR Tempora1sR.
For Temporal SRx4 Spatial SRx4, we apply a temporal average pooling with factor (with end padding if T is not divisible), followed by frame-wis wise spatial downsampling with factor 4 T Downsapl ing Tachella et al. (2025)). The adjoint AA first applies the spatial adjoint (back-projection to HR) and then the adjoint of temporal averaging (ncarest upsample by 4 divided by 4, with folding of the padded tail back to the last frame when is not multiple of 4). The same approach, but with x8, is adopted for the Temporal SRx8 Spatial SRx8 problem. For the Temporal blur + Spatial X task, we use a 1D temporal uniform convolution with circular boundary conditions via FFT of window size of followed by frame-wis wise spatial downsampling with factor 8; the adjoint corresponds to spatial back-projection and time-reversed temporal filtering via FFT.
A.2 IMPLEMENTATION OF LIKELIHOOD PROXIMAT. STEPS
We will now describe the implementation of the likelihood updates in the splitting scheme (Equa- 7 instantiated by task-specific linear operators A over videos x E g((+1)xH>Xx3 We remind that we have solve the following problems:
arg min MERCT+INHHWSS (y(u)) + Φx(u) 28n 1/44 - -ul?
(8)
and
arg arg min wxRT
gy(u) 28(1- 11 -u1/3
(9)
where (-)
|A
3
20
Starting from Equation 19, notice that this is exactly the shape of the prox.0 (0p]|AA--13300 (2) thus provide details about the computation of this step.
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Quadratic proximal (lg data term). variance o,)), ) the quadratic likelihood proximal operator
Given 0 (which may include 6, n as well the noise
prox; |IA [AA--vl (u) = arg min 2 Al 21
+
x
u
reduces to the normal equations
(Id+
u u
where 1d is the identity operator. The exact solution is computationally tractable in high dimensions when A admits a closed-form and fast SVD (Zhang et al. 2020 but to make our method applicable to general operators, solve this linear system approximately using ~ 10 Conjugate Gradient (CG) (Hestenes Stiefel 1952) iterations.
CG is a Krylov-subspace method that iteratively refines an approximate solution x() without explicitly inverting Id AAT. A. Starting from the initial guess x())) u, we iteratively update:
(k) = (Id + eA A)x
b = u + &AA y,
|| 12
(*a) r (k) + g(h) (k-1))
g(k)
r
(k( 11
(k) (p(k), (Id + EA A)p(k)
x (k+1) (k) + a (1) (k),
(k+1) = (k) - (k) (Id
(Id - E (1))
The algorithm terminates after fixed number of iterations once the residual norm (|)(k) || falls below a tolerance (e.g. 10-6). Because Id + EAT A is symmetric positive definite, CG converges rapidly.
This iterative scheme is memory-efficient, requiring only matrix-vector products with A and AT, and avoids the explicit computation of A A, making it suitable for large-scale inverse problems and long video sequences.
Spatio-temporal TV3 proximal (PDHG). For the regularised subproblem we solve
min
20
Au
y112
20n
u
24+1/012 + (x(,),
(10)
f(u)
((Du)
where
Φx(u) = (V) = Σ V X (Dnur.rs))) )² + X3 (D, D.uneis) 2 X7 (2((,ur,c)s)
and Dx = [An D. drD;], so that (Dxxu) = |D2xul12.
The associated subproblem in (10) is convex and can be solved using the primal-duec dual hybrid gradient (PDHG, Chambolle- Pock) algorithm Chambolle & Pock (201I). Let = (ph, Per Pr) denote the dual variable with three components per voxel. Given stepsizes a, 0 such that pa DxI1 and extrapolation 0 E [o, 1], the iterations read:
p² + DDx
6+1 proxog- (p" + +DDxu") max(1, ||p* a ()au(112)
(projection onto unit & ball),
u k+1 prox of (u DDNp*t1)
obtained by solving (T + p(A TAAA A+ bn 11 = = + (AT y on ) with z - DDDDD
h k+1 - htl #+1 u²)
Foor Problems A, B, C, the SVD of A can be expressed in terms of Fourier transforms, only if convolutions are periodic which not always the case for the kind of spatial and temporal blur we have our case.
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Here D A,, +.D. X,D, is the weighted divergence, and the proximal step for f() 2 |lAu 1222-222-211 + 26q u 0x+1/4412 is implemented by solving the normal equations. As in our implementation on is often 2 10", to simplify the computations we remove the regularization term 20r #x+1/4/22- Around 10 iterations of the CG algorithm can be used to solve the normal equations, as they are warm-started with u".
_
In practice, we apply Chambolle- Poctbollll-ookkhhcoo (~ 200 iterations) only in the pure temporal TV case (A), A = 0). When spatial weights are nonzero (A₁ > 0 or X > 0), we instead minimise (8) directly with ADAM (Kingma & Ba 2014) (learning rate 10 100 iterations), which proved more robust in this setting.
A.3 THE LATINO ALGORITHM
In order to clarify the practical implementation of the splitting scheme introduced in Equation (). we provide here the pseudo-code implement LATINO as described in Spagnoletti et al. 2025
Algorithm 2 LATINO
1: given 2o = A'y, text prompt c, number of steps N, latent consistency model fe, latent space decoder D, latent space encoder E, sequences {tx, 6ā -
2: for = ......N- do
3: E~ Id)
4: Zie t ((re) + v1 - anc
(k)
D Encode
5: u (R) + D(fo(zi
(K) c))
>> Decode
6: Ih+1 + precsso0 (k))
> Iy + log p3y ())
7: end for
8: return aN
A.4 THE VISION-XL ALGORITIIM
VISION-XL K Kwon& & Ye 2025b) (Video Inverse Inverse-problem Solver using latent diffusION models) is SOTA framework for resolutonn video inverse problems, LDMs such as SDXL restore videos from measurements affected by spatio-temporal degradations.
Components VISION-XI integrates three main contributions: (i) Pseudo-batch inversion, which initializes the sampling process from latents obtained by DDIM-inveringg the measurement frames. (ii) Pseudo-batch sampling, which splits latent video frames and samples them in parallel using Tweedie's s formula EEfon (2011), reducing memory requirements to that of single frame. (iii) Pixel-space data-consistency updates, where each denoised batch é; is refined using iteeattons of quadratic proximal step
Et arg min
lly
A(x)
typically solved via conjugate gradient (CG). This enforces alignment with the measurement before re-encoding to the latent space and re-noising for the next step.
Overall Algorithm. Starting from 2, DDIM- with 0.37, VISION-XL alternates
denoising in latent space and proximal consistenn refinement in pixel space. After decoding the denoised latent batch &; Dy (b(2). low-pass filter is applied suppress high- seh-frequency inconsistencies before re-encoding and re- re-noising, yielding 2, This process is repeated until shown in Algorithm
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Algorithm 3 VISION-XL
Require: Pretrained VAE encoder Ea, decoder Dg, denoiser E measurement forward operator
A, initial DDIM inversion step p. CG iterations I, low-pass filter widths {o,), noise schedule
1: 2a + Eo(y)
2: f- DDIM (z0)
> Step 1: Pseudo-batch inversion (informative latent initialization)
3: for = p.....; do
4: 21 - 2: - vI
Ei Eg (zx)
b Step 2: Pseudo-batch sampling (Tweedie's formula)
V@x
5:
á; (D((
6:
Sz arg minnean +X A(xx) ||
Step 3: Data-consistency refinement (multi-step
proximal via CG steps)
7:
It
ha, Step 4: Scheduled low-pass filtering (mitigate VAE error accumulation)
8:
+ Eo (T))
9:
2/-1 va
v 14-18
Step 5: Renoising (batch-consistent noise)
10: end for
21 - (21)
11: 20
Vā1
return Bg Do(2o)
A.S CONNECTION WITH PNP-FLOW ALGORITHMS
The PnP-Flow Martin et al. (025)) algorithm designed to leverage Flow Matching image priors has some direct connections to LATINO Spagnoletti et al. (2025). For this reason, we now briefly introduce their setting and state how this idea can be extended to Video Flow models.
Let Xo ~ Po denote a latent variable and X1 ~ P1 a data variable, with joint law (Xo, ~ n. Assume are given pre-trained Flow Matching model with velocity field
[0, 1] RRd + RRdd
(f, r> (t(
learned by minimizing the Conditional Flow Matching (CFM) Lipman et (2033) loss along the straight-line interpolation Liu et al. ;; Benton et al. (2024)
X, : eXo,, X1) == (1 t)Xo IX1,
E [0,
Time-dependent denoiser from Flow Matching. From the velocity field uo we define a family of time-dependent denoisers
Dz(x) == æ + (1 - t) To motivate this choice, recall that for each / [0, the population minimizer of of the CFM loss satisfies
E 1].
(11)
1. (x) E[X: Xo Xr
x])
so that in the ideal case (t 2/ (-) one has
Da():) = x + 1) m7 (x)
[ X, =
(12)
Thus D, coincides with the minimum meca--quare--e given a noisy point X, on the interpolation path. Equivalently, D, solves the regression problem
(MMSE) estimator of the clean variable X1
D. E arg min E g s(x
and can be interpreted as time-indexed denoiser that projects points lying along the straight path (Xx))e(o.1) onto the target distribution Pt.
In particular, if the FM flow is straight-line in the sense that X, () + (X, is realized by the
associated flow ODE, then D, can perfectly recover X, from X,, Under mild regularity assumptions,
one can show that the mean- mquane error E (X), X1]13] vanishes for all t [0, 1] if and only
the learned flow forms a straight-line Flow Matching pair between X, and X, This highlights
the particular suitability of straight-line FM models (e.g. OT-FM Pooladian et al. (023)) Tong et al.
(2024)) as building blocks for PnP priors.
"See Proposition in Martin al. 2025 for a precise statement and proof.
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Algorithm 4 PnP-Flow Matching
1: Input: Pre-trained Flow Matching network vg, time sequence (tx)n with &, E [0, 1] and tn / 1, step sizes (?a) data-fidelity F: Rd + R, prior Po (e.g. standard Gaussian), initial iterate 20 Rd
2: for n = 0, 1, 2, do
3:
In f- In 7/n (x
> gradient step data-fidelity D latent noise interpolation along the flow path PnP denoising with FM-induced denoiser Dr,,
4: Sample E Po
5: Σn < (1 (n) in Zn
6: #n++ 2n (1 In) vo (as Zn)
7: end for
8: Output: Reconstruction xn+1
PnP Flow Matching algorithm. Martin et al. (2025) incorporates the denoisers {Dr (eejo.1) into Forward-Backwarr Splitting (FBS) scheme for solving imaging inverse problems of the form
min F(x) + R(xr),
where F is differentiable data-fideligg term (e.g. negative log- likelihood), and R is an implicit prior induced by the generative model. Classical PnP-FBS Meinhardt al. (2017): [unn al. (2019): :
Hurault et al. (2022); Tan et al.] (2024) replace the proximal operator of R by a time-independent denoiser, applied directly after gradient step on F.
In contrast, PnP-Flow introduces two key modifications:
A time-dependent denoiser D, as in indexed by a schedule (tn)n C. [0, 1] with in /
2. An intermediate interpolation/rerrojectiion step that maps the gradient iterate back onto the straight FM path before denoising.
Given an initial guess Zg E IR sequence of times (t,) ), with t, E [0, 1] and t, + 1, and stepsizes
(7n),, each PnP-Flow iteration at time i,, proceeds as follows:
Gradient step. Move towards data consistency by a gradient descent step on F:
Zn1 Fr 7/ F.
2. Interpolation (reprojection) step. The denoiser Dr,, is trained to act on points distributed as Xt,. i.e. lying on the straight-line FM path. The output 2,, of the gradient step does not follow this distribution, so we "reproject" it onto the FM trajectory by drawing latent sample Po and forming
2 = (1 t in Zn.
(13)
Intuitively, 2, mimics point at time straight path between latent sample from Po and the current gradient iterate.
PnP denoising step. Finally, we apply the FM-induced denoiser at time En-
In+1 D.,, (zn) = 2, + (1 tx vo(tn, Zn),
(14)
which pushes ž, towards the data distribution while still respecting the measurement model encoded in F.
The resulting discrete-time algorithm, summarized in Algorithm 4 alternates between data- fida-fidelity gradient step, an interpolation onto FM trajectories, and generative PnP denoising step. The time parameter f,, controls the relative weight of the prior: for small f,. the denoiser has strong effect (large factor 1 f,, in (14) while as t, + 1 the updates gradually become more likelihood-driven. Comparing Algorithm 4 to Algorithm 2 it is clear that both adapt the same core idea: data-term > add noise + denoise and repeat. Both LATINO and PnP-Flow reproject the intermediate step æ,, to point in the Flow ODE, to which is applied, in one case, the CM, and in the other, the FM denoiser. The other difference is in the type of data-fide data-fidelity term adopted; in one case, is proximal step as a result of an implicit Euler step, while in the other, it is a gradient one, which equivalent to an explicit Fuler step and requires many more iterations converge due the limitations 7a-
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Given these similarities, it is natural to think about merging the two frameworks by leveraging few- step FMs Liu et al. (2022); ; Kornilov et al. (2024) in place of CMs. This would lead to a Flow-SAE that could be plugged into the LATINO algorithm and provide different way to integrate the prior term in Equation (5) As a direct consequence, given a video FM prior; it can be deployed in place of the VCM in our Algorithm []] and benefit from the modular framework introduced in this work, as it can be coupled with an ICM, or an image FM prior, and the TV3 term. We believe that future research may benefit from this Flow-LVTINO formulation to improve the quality of restorations and further generalize our setting.
A.6 ABLATION STUDY
To better understand the impact of the data-consistency updates in LVTINO, we perform an ablation study comparing different strategies for the likelihood proximal steps appearing in Equation (7) Furthermore, provide results on Problem A and Problem B obtained with lighter version of LATINO that only includes the VCM prior. We call this version LVTINO-V and we provide in Algorithm its implementation.
In Table we find the hyperparameters used to get Table iin Section These values were chosen after an extensive grid search on À = (An, Aw Å₂), ), 7): nevertheless, other combinations also produced sat- isfactory results, and we want to illustrate some alter- native choices in this section.
Problem
(Aa, Au, X-)
1/8
(1
0, 2.8 10-4)
10c
106
B
(0, 0, 0)
3 x 10a
1.5x 1,5x10 10
C
(10 10 10
10
10
Table 4: Hyperparameters used in
LVTINO: w/ and w/ TV. As can see from Table it seems better to keep the TV prior term Φy when we solve Problem A, while it is better to fall back on the prox-only case (i.e. X = (0, O, 0)) when we tackle Problem B. We then show in Table [ what happens in the two symmetric cases, meaning when we switch the optimal configurations of Problem A with those of Problem B. We can observe how the metrics do not change much for Problem B, we are still able beat the SOTA VISION-XL method in half of the metrics (in particular, we focus on the FVMD that tells us how temporally consistent the reconstruction is). As opposed to this, we see that we lose lot of precision for Problem A in all the metrics. This can be explained by the fact that the TV prior is crucial when dealing with temporal interpolation, as it prevents the ICM from creating flickering effects.
LVTINO-V as a lighter alternative. As anticipated, also provide some results when turn off the ICM part of the LVTINO algorithm, meaning that we set 1. This solution, described in Algorithm [ only presents choices in one data-fidelity step, which we can again tune as a TV- regularized step or as a classical prox-only step. We provide in Table 5 both cases. The values of and are the same as Table meaning that the TV case will follow the Problem A row and the prox case the Problem B row. We see how this lighter version can still beat VISION-XL in almost all metries with only 5 NFEs. In particular, since we no longer have the ICM, the TV prior loses its importance, and the prox case emerges the best option. LVTINO-V is capable of getting highly temporally coherent reconstructions, as shown by the low FVMD values, only losing to LYTINO, especially in LPIPS, as its single frame quality suffers from the limitations of the VCM. We believe that further research could fill the gap between LYTINO and LVTINO-V, developing new SOTA methods that solely use VCMs, without the need for its image counterpart, to increase spatial quality.
Temp. SRx4
| SRx4
Temp. blur
SRx8
Method Slata-Consistency Config)
NFE,
FVMD
PSNR
SSIM
LPIPS
FVMDJ
PSNR
SSIM
LPIPS
LVTINO-V (prox)
425.2
25.00
0.811
0.270
31.70
23,80
0.737
0.375
LVTINO (ICM: prox, VCM: prox)
9
607.5
22.59
0.614
0.475
42.65
24.91
0.741
0.370
LVTINO-V (TV)
5
503.3
24.44
0.776
0.338
578.0
22.01
0.684
0.441
LVTINO (ICM: prox, VCM: TV)
371.1
27.25
0.837
0,249
51.52
23.18
0.725
0.418
VISION-XL
8
1141
26.03
0.672
0.439
82.92
26.18
0.749
0.468
ADMM-TV
427.6
18.04
0.767
0.297
128.2
21.18
0.644
0.452
Table 5: Ablation study on data-consistency schemes. Left block: results for temporal SRx4
S Problem A. Right block: results for temporal blur + SRx8, Problem B.
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Algorithm 5 LLTINO-V
given degraded video y, operator A, initialization 26 A'y, video lenght 7 + steps N
2: given video CM (Ev, Dv. ft). schedules {tk, Ox A} 9v
3: for k == =0=0. -....... NN do
4:
e~
5:
Zhe
(N)
t Vara Ev(2r) + -I-
encode diffuse fy
6:
k/+1//2 t DD(f (z (t))
VCM prior contraction
7:
Ik+1 < arg C xa gy gy(u) xx(2)) + 26, 0x+1//2 u112 data-consistency
Solved with a few CG iters; TV-in-time can be used here.
8: end for
9: return
A.7 ADDITIONAL EXPERIMENTS AND ANALYSES
Comparisons other baselines. To provide more comprehensive evaluation, extend comparison to include non-zero-shot methods, such as VIDUE Shang et al. (2023), which is explicitly trained for joint motion-blur removal and frame interpolation. This makes highly relevant baseline for the combined blur and interpolation tasks of Problem C, whereas standard Video Frame Interpolation (VFT) methods often fail to address motion blur. We indeed specifically compare VIDUE against the recent BiM-VRI (So et al. (2025). As shown in Figure 7 specifically for interpolation, it fails remove the degradation caused by motion blur. In contrast, VIDUE addresses the joint problem more effectively. As VIDUE does not perform super-resolution, we apply bicubic upsampling (x8) to its output for fair comparison LVTINO. The results are shown in Table[aand Table 2
because BiM-VFI is trained
We also acknowledge that DifiRRVR Yeh et al. (2025) a relevant competitor to VISION-XL. and thus to LVTINO. However, the specific Stable Di Diffusion v2 1 checkpoint required to reproduce their method is no longer publicly available, which prevents a fair comparison.
BIM-VFI
VIDUE
Figure 7: Visual comparison Problem C. Left: BiM-VFI preserves blur artifacts. Right: VIDUE removes some motion blur
Noisier cases. We now show results computed on the Adobe240 dataset for higher noise scenario with o, 0.01. As expected, the optimization step in VISION-XL fails properly restore the video sequences in this case, as VISION-XI XL is not conceived to deal with noisy measurements, yielding NaN values. In contrast, LYTINO and ADMM-TV handle this case without difficulty. Their results are reported in Table together with VIDUE for Problem C.
Problem A: Temp. SRx4
SRx4
Problem B: Temp. blur + SRx6
Problem C: Temp. SRx8 + SRx8
Method
NPK] PYMD) PSNKT
SSIMT
LPIPSL
NFKI PVMD]
PSNRT SSIMF LPIPS)
NEE.
FYMD]
PSNRT
SSIM LPIPSL
LVTINO
256.5
24.95
0.782
0,331
62.6
21.91
0.671
0.448
1
310.6
23.42
0.688
0.428
VIDUE:
121.5
21.33
0.683
0.511
ADMM-TV
424.1
17.85
0.758
0.373
145.5
21.38
0.646
0.411
1665
18.12
0.475
Table 6: Results on the Adobe240 dataset with noise ay 0.01 across the three problems. Best results are bold, second best are underlined.
Non-linear Inverse Problems. Although for presentation clarify we present LYTINO in the context of linear inverse problems, L,TINO can be applied to non-linear problems too. The main requirement
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is the ability to evaluate the proximal operator of the bog-likelihood, which is feasible for many non-linear degradations, as already shown in Spagnoletti et al. (2025).
To demonstrate this, consider non-linear degradation: Additive Gaussian noise (ar 0.01) fol- lowed by JPEG compression (quality=10) applied to each frame independently. Figure@Jsoww frames extracted from the reconstruction results. LYTINO successfully recovers high-frequency details and suppresses compression artifacts, confirming its applicability to non-linear inverse problems.
GT
Mcasurement y
LVTINO
RASHEER
RARBEL
RÄMBEE
THIET
THIES
Figure 8: Results on non-linear inverse problem (Gaussian Noise + JPEG compression). Top row: Example from Adobe240. Bottom row: Example from GoPRO240. LVTINO effectively removes blocking artifacts and noise in both cases.
Hyperparameter Sensitivity. We analyze the stability of LYTINO with respect the step size å and the regularization weight X. Figure P plots PSNR and LPIPS metrics on representative sequence from the challenging Problem C. We observe that performance remains stable across reasonable range of values (e.g., E [2 10 10'D. This indicates that the parameters reported in Table 4 are not brittle, and e-good hyperparameters can be found without exhaustive fine-tuning.
PENR tealmep
IP heslmwp
7/8+40
700+401
22
0A35
2006400
2000400
0.455
5004400
227
2000401
Figure 9: Sensitivity analysis for Problem C. The method shows robust performance across wide range of step sizes (6) and regularization weights (A).
In similar way. it is also possible to analyse the parameter 77, which controls the balance between the VCM and the ICM our theoretical framework (see equation (6). It must be translated into practice by choosing the corresponding evaluation times Lv and fy. In particular, when increases, the ty is larger, and the ICM is evaluated at smaller Ê,. Because pretrained Consistency Models are only accurate restricted subset of timesteps, this severely limits how finely we can tune v in practice.
To approximate different effective values 1J, we therefore perform an ablation in which we vary the possible video timesteps ty and image timesteps Ł within the valid finetuned ranges of the two backbones. Operationally, we choose among the subsets:
-CM (video model): (7777, 522, 375, 255, 125)
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ICM (image model): (7749, 624, 490, 374, 249, 63))
and pairing them to simulate "larger rf" (larger VCM steps + smaller ICM steps) and "smaller vj"
(smaller VCM steps + larger ICM steps).
For clarity, Table 7 shows some configurations evaluated to provide a comparison for Problem B (Temporal SR x SRx4). Each configuration is evaluated on the same sample sequence:
Experiment
ly (video)
I (image)
PSNR 1
SSIM t
LPIPS
EXP
[375, 255, 125]
[499, 374]
22.90
0.752
0.296
EXP
[757, 522, 375]
[249, 124]
22.56
0.714
0.308
EXP 3
[522, 375, 255]
[374, 249]
22.80
0.762
0.290
EXP 4
[522, 255, 125]
[749, 624]
22.36
0.720
0.317
BASELINE
[757, 522, 375, 255, 125]
[374, 249, 124, 63)
23.96
0.770
0.272
VISION-XL
24.36
0.667
0.488
Table 7: Ablation study on scheduling strategies (ty and 12) for Problem B. EXP 1-4 represent varying balances of 77, while BASELINE represents the configuration used in the main paper.
For comparison, the values used for the experiments shown in the other tables are: tv E [757, 522, 375, 255, 125] and t, [374, 249, 124, 63]. We notice how, even with fewer steps and varying the configurations, the metrics remain stable.
Error Map Analysis. To better visualize the nature of the residuals, provide T.2 maps in Figure TO for Problem on an example sequence. Comparing LVTINO against VISION-XI and ADMM-TV,
observe that our method yields lower residuals, particularly around motion boundaries and fine structural details where competing methods exhibit larger errors due to unresolved blur or temporal inconsistencies.
Frame
Frame n +
AISIOIA
ADMM-TV LVTINO
Figure 10: L2 error maps between reconstructions and Ground Truth. LVTINO (middle row) demonstrates lower error magnitude compared VISION-XL and ADMM-TV, particularly in dynamic regions.
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B ADDITIONAL EXAMPLES
We provide in Table 8 qualitative video comparisons for Problem A, Problem B, and Problem C. Each triplet corresponds the Ground Truth (GT), the observed degraded input (z), and the restored sequence. For Problem C, we provide longer sequences (81 frames) to better appreciate the results.
GT
y
LVTINO VISION-XL
Problem A (seq. A1)
1ink
1ink
1ink
link
Problem (seq. B1)
link
link
link
link
Problem B (seq. B2)
1ink
1ink
Link
link
Problem C (seq. C1)
1ink
link
link
link
Problem C (seq. C2)
1ink
1ink
11nk
1ink
Table 8: Results of our method compared to those obtained by VISION-XL, ground truth, and measurements (input sequence). Click the links to see the videos.
Additional examples are shown in Figures 12 We also include additional sliced images in Figures 11a and 1lb
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Frame from measurement y
GT slice
LVTINO slice
VISION-XL slice
(a) Comparison between slices from 25 consecutive frames. Problem A (seq. A1)
Frame from measurement y
GT slice
LVTINO slice
VISION-XI slice
(b) Comparison between slices from 81 consecutive frames. Problem C (seq. C1)
Figure 11: Slice comparisons across two sequences. In green, the sliced column. Slice images are obtained from the dimensinnsi video tensor (i.j.T) by fixing column index This leads to 2D tensor with indices (i.r) that is represented as an image, where the index represents the row and the index represents the column.
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GT
LYTINO
DISTONA
VISIO
Figure 12: Visual comparison for Problem
GT
CENONNY LYTINO
Figure 13: Visual comparison for Problem B.
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Figure 14: Visual comparison for Problem C.
GT LVTINO VISION-XL
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Figure 15: Visual comparison for Problem C.
GT LVTINO VISION-XL
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Figure 16: Visual comparison for Problem C.
GT LVTINO VISION-XL
30


Reviews:
[{"review_id": "wsPzlQn0Az", "reviewer": "Reviewer_h2v5", "summary": "The paper proposed LATINO, a plug-and-play inverse solver for high definition video restoration with latent consistency model (LCM) priors. In particular, LATINO leveraged a video consistency model to capture global dependencies and causalities, and a image consistency model to recover local spatial detail and enhance perceptual quality on each frame. The two LCMs are integrated via a Langevin-type algorithm, together with measurement gradient guidance and regularizations. Experiments show superior performances comparing to baseline algorithms.", "questions": "1. Is it possible to experiment on more datasets and inverse problems?\n2. Can the authors provide more insight of the Langevin dynamics algorithm?\n3. Is it possible to carry out an ablation study on \\eta?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. Successfully combined VCM and ICM to generate with both temporal causality and spatial quality. \n2. Developed Langevin-type splitting algorithm to effectively balance measurement consistency with prior data consistency.\n3. Demonstrated highly competitive experimental results under low NFEs.", "weaknesses": "1. The paper only shows experiments on a single dataset and limited baselines. Also, videos are short and noise level is low. Would be more convincing if more experiments could be added. \n2. Since Langevin dynamics typically require the score function to be \"well-behaved\" to converge, fundamental questions could be raised on why LCM scores could lead to good sampling results. Further explanation should be provided to justify the choice of developing a Langevin-type algorithm for such tasks. \n3. There seems to be no ablation studies on \\eta, which is a crucial hyper-parameter in balancing the algorithm.\n4. LATINO is based on various models and algorithms. It seems to me that introducing all base models dilutes the core motivation and method.", "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": "SCul3lm9NU", "reviewer": "Reviewer_Drrh", "summary": "This paper proposes LVTINO, a zero-shot inverse solver for high-definition video restoration. It uses Video Consistency Models (VCMs) as a prior. The method combines a VCM prior (for temporal), an ICM prior (for spatial), and a classical TV regularizer in a unified framework. The method is based on a Langevin posterior sampler (like LATINO) and does not require gradient calculation through the network, making it efficient. The experiments show good results on several video restoration tasks like a combination of temporal super-resolution and deblurring.", "questions": "1. In Equation (2), the coefficient for the score term is $\\beta_t$. But in Equation (3) (the ODE form), this coefficient changes to $\\beta_t / 2$. Could you explain why there is a such change?\n\n2. In Equation (4), the term $\\tilde{x}_s$ is used in the integral, which is not defined (I can't find it). What is this variable?\n\n3. On L175, the superscript (k) is used. Is superscript different from other subscripts k? Or is it just a typo?\n\n4. On L213, the paper introduces a TV3 regularizer. Is this 3D Total Variation a widely used regularizer? It would be great if you can provide a reference for this.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. LVTINO is computationally efficient. A very big advantage is that it does not need to compute gradients in the latent space, which is a heavy problem for methods like DPS (Chung et al., 2023). This is a very good point for practical use.\n\n2. The paper combines the video prior (VCM), the image prior (ICM), and a classical prior (TV3) in a unified and clean way. I think this product-of-experts prior is an elegant and good way to solve this video restoration problem.", "weaknesses": "1. Lack of baseline comparison: This paper compares with only a very small number of baselines (VISION-XL, ADMM-TV). This is much less than other papers in this field. It is not necessary to solve all problems, but maybe the authors should focus on one task, for example Frame Interpolation (Temporal SR), and compare their method with more specialized models for that task. For example, comparing with BiM-VFI (Seo et al., in CVPR 25).\n\n2. Limitied to *Linear* inverse problem: The method is proposed for linear inverse problems ($y = Ax + n$ where $A$ is a matrix). This is a fundamental limitation. It is not clear if it can be used for non-linear problems.\n\n3. Somewhat impractical metric (NFE): The NFE (neural function evaluations) metric is not very practical (it is more like theoretical). For a better comparison, it would be good to add more practical metrics like runtime (seconds) and memory usage (GB) to Table 1.\n\n4. Hyperparameter sensitivity: Table 2 shows that the optimal hyperparameters are very different for each problem (A, B, C). This suggests the method might be sensitive to these parameters. Also, the paper has not enough ablation study on how to choose these different hyperparameters (e.g., the lambdas for TV, eta, delta).\n\n5. Missing Error Maps: The qualitative results (Figures 4, 5, 6) are good, but it would be much easier for readers to understand how well the model is performing if the authors provide error maps (e.g., Difference from GT).", "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": "eAFReMesP6", "reviewer": "Reviewer_WjZ3", "summary": "This work proposes a novel framework that enables high-definition video restoration in a plug-and-play manner using Video Consistency Models (VCMs).\nUnlike previous methods that directly apply image LDMs to video inverse problems, the proposed approach explicitly captures temporal causality through VCMs. Leveraging the temporal priors encoded in VCMs, the method achieves substantial perceptual improvements over current state-of-the-art techniques.\n\nSpecifically, the video inverse problem formulated as $y=Ax+n$ is solved as follows.\nThe sampling process begins by initializing $x_0$ with the pseudo-inverse reconstruction $A^{\\dagger}y$.\nThe subsequent diffusion sampling proceeds in three stages:\n\n1. VCM sampling: the model first evolves the initial estimate using only the VCM prior for better temporal coherence.\n\n2. Alternating VCM–ICM: the sampling then alternates between the VCM and Image Consistency Model (ICM) priors to progressively refine both temporal and spatial consistency.\n\n3. ICM refinement phase: finally, the process is completed using the ICM prior alone for better spatial details.\n\nEach after ICM sampling, a few iterations of the conjugate gradient (CG) method are performed to enforce data consistency,\nwhile each after VCM sampling, the Adam optimizer is used to align the samples with the measurement constraint $y=Ax+n$.", "questions": "1. Please provide the runtime and VRAM usage for all comparative methods to ensure a fair and comprehensive comparison.\n\n2. Please provide a clear explanation addressing Weakness 1 and Weakness 2.\n\n3. Please clarify whether the code will be publicly released, as this would enhance the reproducibility and impact of the work.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. Using Video Consistency Models (VCMs) to solve video inverse problems represents a novel and timely contribution, especially given the current interest on efficiency in high-definition video inverse problem solvers.\n\n2. The idea of alternating between VCM and ICM is conceptually interesting, offering a fresh perspective on how temporal and spatial priors can be jointly leveraged.\n\n3. The authors conduct comprehensive ablation studies, examining different design choices such as whether to enforce measurement consistency using the conjugate gradient or Adam optimizer, and whether to alternate between VCM and ICM priors or rely solely on VCM.\nThe results empirically demonstrate that alternating between VCM and ICM yields the most effective performance.\n\n4. I really enjoyed reading the submission. The discussion of open problems provides valuable insights and motivates further research, making the paper timely and impactful.", "weaknesses": "1. I understand the empirical evidence showing that alternating VCM and ICM is effective for solving video inverse problems. However, there is no clear explanation as to why alternating between VCM and ICM yields better results than using VCM alone. Providing a clear theoretical or intuitive explanation would make the paper stronger.\n\n2. The authors state that $p_\\phi(x|\\lambda)$, representing the total variation (TV) prior, helps prevent the ICM from introducing flickering artifacts. If that is the case, the TV regularizer should logically be applied after the ICM step. However, in the current formulation, $p_\\phi(x|\\lambda)$ is applied after the VCM step. Clarifying this design choice would be beneficial.\n\n3. Since runtime and VRAM usage are also direct measures of a method’s efficiency, the authors should report these metrics for all comparative methods to ensure a fair and comprehensive comparison.", "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": "RmBFjGAA5b", "reviewer": "Reviewer_acCb", "summary": "This paper produces LVTINO, a video reconstruction model, based on Latino. It \"handcrafts\" a prior, using state of the art generative models. This special mixture of experts prior has the consequence that it is capturing the video as a whole object, instead of operating on a frame to frame basis.  Furthermore, relying only on \"denoising\" operations, this algorithm does not need to backprop through any ODE or even network evaluation. The resulting algorithm is applied to video datasets, and compared against two other models.", "questions": "see weaknesses. Overall I really appreciate the novelty and lightweightness of the idea :) my main issues are making sure the algorithm is robust and understandable.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 1, "contribution": 3, "strengths": "I find the idea very innovative. Indeed, training a full usual generative model, on a video space is prohibitively expensive. So defining a mixture of experts prior (based on generative models), one for \"smoothness\", one for the temporal relations, and one for frame consistency. The evaluation is good, it is nice that the authors performed a full grid search for finding suitable hyperparameters, and also showed that others work too, solidifying the trust in the method. Further, I commend the authors for providing many examples and an explanation of the latino algorithm. Furthermore, that it is gradient free makes that it is super cheap as opposed to gradient based diffusion restoration algorithms.", "weaknesses": "The downside is that this paper is very densely written. I dont find the intro to diffusion/consistency models helpful i would rather want to paper to introduce the latino framework and then delve into the video model. I dont think the long equation 7 is necessary and the spelling out of the Euler steps, too. Rather focus on more explanation in a simpler setting to make it easier to digest. \n\n There is a plethora of hyperparameters. While the grid search is nice, and good that there area other vals I would like to see that this method is not overengineered. If put on a compute (tuning budget), how does it compare to the other models? \n\nDoes the efficiacy of the approach depend a lot on the chosen consistency/distillation strategy?\n\nWhy is the hyperparameter between the video regularizer and the image one? I would rather see a tuning tradeoff between the smoothing regularizer and the image one?", "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)