Stable Video Infinity: Infinite-Length Video Generation with Error Recycling
Authors: Wuyang Li
Wentao Pan
Po-Chien Luan
Yang Gao
Alexandre Alahi
Venue: ICLR 2026 Oral (2026)
Keywords: Infinite-Length Video Generation
Error Accumulation
Primary area: generative models

Abstract: We propose **Stable Video Infinity (SVI)** that can generate non-looping, ultra-long videos with stable visual quality, while supporting per-clip prompt control and multi-modal conditioning. While existing long-video methods attempt to _**mitigate accumulated errors**_ via handcrafted anti-drifting (e.g., modified noise scheduler, frame anchoring), they remain limited to single-prompt extrapolation, producing homogeneous scenes with repetitive motions. We identify that the fundamental challenge extends beyond error accumulation to a critical discrepancy between the training assumption (seeing clean data) and the test-time autoregressive reality (conditioning on self-generated, error-prone outputs). To bridge this hypothesis gap, SVI incorporates **Error-Recycling Fine-Tuning**, a new type of efficient training that recycles the Diffusion Transformer (DiT)’s self-generated errors into supervisory prompts, thereby encouraging DiT to _**actively identify and correct its own errors**_. This is achieved by injecting, collecting, and banking errors through closed-loop recycling, autoregressively learning from error-injected feedback. Specifically, we (i) inject historical errors made by DiT to intervene on clean inputs, simulating error-accumulated trajectories in flow matching; (ii) efficiently approximate predictions with one-step bidirectional integration and calculate errors with residuals; (iii) dynamically bank errors into replay memory across discretized timesteps, which are resampled for new input. SVI is able to scale videos from seconds to infinite durations with no additional inference cost, while remaining compatible with diverse conditions (e.g., audio, skeleton, and text streams). We evaluate SVI on three benchmarks, including consistent, creative, and conditional settings, thoroughly verifying its versatility and state-of-the-art role.

Published as a conference paper at ICLR 2026
STABLE VIDEO INFINITY: INFINITE-LNGTT VIDEO GENERATION WITH ERROR RECYCLING
Wuyang Li
Wentao Pan
Po-Chien Luan
Yang Gao Alexandre Alahi
VITA @ EPFL
Project Page:
ABSTRACT
We propose Stable Video Infinity (SVI) that can generate non-looping, ultra- long videos with stable visual quality, while supporting per-clip prompt control and multi-modal conditioning. While existing long-video methods attempt mitigate accumulated errors via handcrafted anti-drifting (c.g.. modified noise scheduler, frame anchoring), they remain limited to single-prompt extrapolation, producing homogeneous scenes with repetitive motions. We identify that the fun- damental challenge extends beyond error accumulation between the training assumption (seeing clean data) and the test-time autoregres- sive reality (conditioning on self-generated, error-prone outputs). To bridge this type of
critical discrepancy
hypothesis gap. SVI incorporates Error-Recycling Fine- Tunin,, efficient training that recycles the Diffusion Transformer (DiT)'s 's self-generated errors into supervisory prompts, thereby encouraging DiT actively identify and correct its own errors. This is achieved by injecting, collecting, and banking errors through closed- recycling, autoregressively learning from error-injected feed- back. Specifically, we (i) inject historical errors made by DiT to intervene on clean inputs, simulating arorrlaccmulltte trajectories in flow matching; (ii) efficiently approximate predictions with one-step bidirectional integration and calculate er- rors with residuals; (iii) dynamically bank errors into replay memory across dis- cretized timesteps, which are resampled for new input. SVI is able to scale videos from seconds to infinite durations with no additional inference cost, while remain- ing compatible with diverse conditions (c.g., audio, skeleton, and text streams). We evaluate SVI on three benchmarks, including consistent, creative, and condi- tional settings, thoroughly verifying its versatility and state-of-ther role.
INTRODUCTION
Failure is simply the opportunity begin again, this time more intelligently.
HENRY FORD
With the scaling of models and data, the video Diffusion Transformer (DiT) (Wagg al. 2025a Kong et al. 2024 Liu et al. 22224) Hong et al. 2023) has made great strides in synthesizing realistic, temporally coherent videos, supporting in-the-wild content creation. While achieving great success, this community suffers from a limited video length, typically 5 seconds (Wang et al., 2025a). This is mainly caused by the open challenge of error accumulation, a.k.a., drifting: after autoregressively conditioning on the previously generated, predictive errors will compound over time, leading to progressive degradation in image fidelity, motion stability, and semantic controllability (Fig. Ta).
In this context, existing solutions can be divided into three trends, including (i) noise modification. augmenting and modifying the noise schedule to reduce the past-frame dependency (Chen et al.
2024 Ruhe et al. 2024), (ii) frame anchoring using the error-free reference image (Henschel et al. 2025 as anchors to reduce the dependency of error-included ones, and (iii) improved sampling like
masked-noise guidance (Song et al. 2025 and anti-drifting sampling ZZhang & Agrawala 2025).
However, existing methods primarily aim to alleviate rather than correct accumulated errors, which leads to two key limitations: constrained length (generally 10 seconds up to about 1 minute) and the scene homogeneity bias with repetitive motion. Practically, most methods essentially extrap- olate the original clips controlled by single prompt, rather than creating truly long-form videos

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Published as a conference paper at ICLR 2026
(a) Video Generative Model
(b) Image Restoration Model
(c) Stable Video Infinity (Ours)
Caral leen
Generative
Antoonatoo DIT
Goscition
stable
DIT
Restorr
Degredol
infinity
Error-Rerychng Fine
video
Clsnn Image
Degraded deo
Deg Image
irage
image
lalant wrror
Hypothesis Gag.
Treis Degraded Inset
Test
Clean Imput
Degraded most
Degraded Ispui
Degroded Irput
Degreded Input
Error
Content
Eiror
Goncration
Rebustnces
Goneration
Robustness
Robustness
Figure 1: Comparison among (a) video generative DIT, (b) restoration DiT, and (c) our Stable Video Infinity regarding the scheme (row 1), training-test hypothesis gap (row 2), and outcome (row 3).
that the prompt stream storylines can easily control. Consequently, current solutions do not satisfy many creative real-world demands, such as short-form filming that requires plausible, frequent scene changes or creation of hour-scale online presentations.
To tackle this, aim to treat the cause rather than the symptom, seeking to fundamentally correct accumulated error itself rather than merely alleviating its effects. By observing the artifacts caused by errors (see Fig. 6. we empirically find that they closely align with common degradation types, such as blur and color shift within the image restoration community. Given the state-of-the-an role of DiT in low-level vision, these degradations should not be difficult for much larger video DiT (e.g., 14B) with more substantial capacity. Surprisingly, the opposite holds in practice: Why are these powerful models highly susceptible to such errors, leading to a severe and rapid collapse?
We uncover that the fundamental challenge lies in the hypothesis gap between the training and text. In training generative DiT (Fig. DD, historical trajectories of flow matching are assumed to be error-free. However, this is easily broken in test since the model autoregressively uses previous gen- crations with predictive errors, which is mathematically clarified in Sec. 3 In contrast, for restora- tion DiT (Fig. [p)), both training and test assume error-injected inputs, ensuring error robustness. Hence, to bridge this gap, we start a new perspective: recycling self- self-generated errors as supervisory prompts, encouraging DIT autoregressively correct its own mistakes via error feedback.
In this work, we propose Stable Video Infinity (SVI) to generate ultra-long videos with stable quality and non-looping motions. In Fig. Dr. SVI employs novel Error-Recyclin Fine-Tuning that repurposes the DiT's self-generated errors as supervisory signals. thereby enabling the model to iteratively refine its outputs through autoregressive error feedback. Specifically, we (i) intervene on clean input by injecting historical errors to simulate degradation, (ii) approximate the predic- tions and calculate errors via one-step integration bidirectionally, and (iii) dynamically save and selectively resample errors across discretized timesteps from replay memory. By doing so, we can efficiently unleash the restoration ability in video DiT, actively correcting errors in generation. Ad- ditionally, SVI has several emergent advantages over previous works. Data light: only small-scale data required for LoRA fine-tuning: Efficient: zero additional inference cost; Versatile: supporting in-the- wild control signals, e.g., audio and skeleton (Fig. 70).
In summary, our contributions are fivefold. (1) SVI breaks the length limit of vidcos from seconds to infinity by actively correcting errors. (2) We systematically analyze the training-test hypothesis gap in long video generation and theoretically formulate two types of errors. (3) To bridge this gap, an error-recycling fine-tuning is proposed to dynamically caleulate, save, and selectively inject errors to clean inputs, predicting error-recycled velocity. (4) We extend SVI into a family of models for different applications, e.g., talking and dancing (see Fig. 77.. (5) We propose comprehensive benchmarks with short/long consistent/creative settings, aligning with diverse real user needs.
RELATED WORK
Video Generation in the Wild. With the scaling of model (Hu et al. 2025) and data (Weissenborn)
et al. 2020), commercial-grade video generative models (Liu et al. 2024 Brooks et al. [2244] Yang et al. 2024c Blattmann et al.] 2023 Ho et al. 2022 Lin et al.) 2024), such as Wan (Wang et al.)
2025a) and Hunyuan (Chen et al.) 2025). have made significant progress in producing high-quality short videos. Based on this, the community has pursued a flourishing line secondary creation for diverse objectives, introducing task-oriented controls, e.g., audio, skeleton, to generate desired
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content like talking (Kong al., 2025
Chen et al. 2025). ). dancing (Wang al. 2025b), naviga- tion (Agarwal et al., 2025 Hassan et al. 2025), gaming Yu et al., 2025] Che et al. 2025). Despite promising progress, the short length remains an open challenge, limiting its practical applications.
Long Video Generation. Generating long videos is an open problem due to error accumulation, prompting three trends of solutions.
(i) Modified scheduler: aims to improve the ODE solver with
error robustness. Some works (Qiu et al. [024 H et al. (2024) extend videos via noise reschedul-
ing. Some other works (Chen al. [024 Ruhe al. [0244] [oong et al. (025) modify and augment
the noise schedule to reduce dependence on past frames. (ii) Frame anchoring: uses the clean image
as consistent reference, including tailored anchor designs (Henschel et al. 2025 Weng
2024)
and planning based optimization (Brooks et al., [2024) Zhao et al., [024 Yang et al. (024b). (ii)
Error-robust architecture: improve long-range consistency autoregressively with bidirectional distil- lation Yinn et al. | et al. al.] 20255, distributed generation (Tan et al. 2024), and anti-drifting
sampling (Zhang Agrawala| 2025| Gu et al. 2025). improved attention Kodaira al. (2025); Lu
et al. (0024), mixture of context (Cai al. 2025). In this work, we start a new perspective by recycling the errors made by DiT itself, encouraging DiT to actively correct errors cyclically,
PRELIMINARIES AND MOTIVATION
3.1 ERROR-FREE HYPOTHESSS IN LONG VIDEO TRAINING
Notation. We use hat (c), tilde ()) and the superscript-free symbol (.) to represent model predicted, error-injected, and clean (error-free) variables, respectively, in following sections.
vi
line Aoo
(a) Error-Free Flow Matching (Training)
Training. Flow matching enables continuous-tine generation for DIT. To delve into the essential challenge of the training- test hypothesis gap, we start from error-free flow matching (Fig. 2h) for image-to-video training, which aims to learn the model solving ODE from the joint noise and reference image distribution X x to video latent Xvid- In training, assuming error-free video latent Xvid, noise Xnoi NN 7), timestep t E [0, 1), error-free reference image X Xing: and optional mul- timodal condition C, the training objective is denoted as:
Shift caused by regressive nature
E
king
Xvidt
(b) Single-Clip Predictive Error (Test)
glnn raci
Shift caused by error- included reference image
X
L = ExxoixXvid Xing Ci Xmgg C,t, - vil², (1)
(c) Cross-Clip Conditional Error (Test)
where X1 =t Xvid + (1 (1- t)- Xnoi is the intermediate state, V. = u(X,, Ximgs C, t) the predicted velocity with model -Xvid - Xnni is the ground-truth.
Figure 2: Training-test hypothe- ses gap. (a) Training assumes his- torical trajectories and an interme- diate stage free of errors, which are easily broken in test by two errors. (b) Predictive error caused by the regressive nature affects the trajec- tory end Xvid- (c) Conditional er- caused by error-included im- ages also affects start X img
0, V, = dx.
dt
Test. The generated video latent is obtained = with X1 = Xvid using Neast sampling from noise Xp. In practice, this process can be achieved by discretizing the unit interval 1 and applying a numerical ODE solver (Esser al. 2024) for the step-wise integration:
0 = to < <i<... ... < N
Xta1 Xxe, (44-1 - (x) uu( u(Xue Ximgs txi 0), where t < Noest 1, and X next-step generation.
noi
3.2 PAROR-CORRUPTER INFERENCE IN LONG VIDEO GENERATION
In the test, we uncover that two types of errors will appear due to the error-free training hypothesis.
Single-Clip Predictive Error. In Eq. 1 the training assumes X, obtained via clean latent Xvia with correct historical trajectory. However, inference (Fig. 2 this hypothesis is easily broken, since X, is obtained from predictive trajectory with inherent errors. Due to the Seaan-Squree-Ene Error (MSE) regressive nature, this will lead to an eternal existent difference Es between the predicted ve- locity Vi u( ((X,, Ximg: C, 8) and ground-truth V22 u(X,, Xmng. C, 9).. This shift is gradually accumulated at each sampling step, which reuses the integrated velocity from previous steps. Conse- quently, for the independent generation, the small step-wist wise errors integrate over the ODE trajectory
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in Eq. 2 producing shifted predicted latent Xvid. which is defined as single-clip sampling error:
E vid
Xvid- Conceptually, E is typically small with adversarial attack nature (Goodfellow and causes negligible perceptual degradation in short video generation.
et al. 2015
Cross-Clit Conditional Error. In Fig. 2. when generating subsequent clips autoregressively, the model uses error-included frame X Xing from Xvid (Fig. 2)) instead of the clean one Xing used in training (Eq. D. leading to a shift in the trajectory start from Xime to Xime, As Eq. is optimized with clean input, these crror-corrupted samples x are out-of-distribtion regarding clean training data, which will severely confuse DiT in predicting V. ua u(x,, Ximg: C, L,; Due to its large gap with desired velocity V. = "(X,, Kx,, Ximg, C, 1;0) ) in training, this error will cause a more biased prediction Xvid, where we define this accumulated error as cross-clip conditional error: E Xvid Xvid Here, Xvia is solved by integrating V2 with error-corrupted Ximg.
Error Accumulation and Amplification. In autoregressive cross-clip conditioning, these two types of error are accumulated and reinforce each other: predictive error induces drift in the generated video latent, which magnifies the error at the trajectory start and, in turn, further increases predictive error. This feedback loop can rapidly cause catastrophic degradation of generated videos.
3.3 BRIDGING THE TRAINING- TEST HYPOTHESIS GAP
In summary, the essential challenge for long video generation is the hypothesis gap between the error-free training and error- incluid inference. More harmfully, DiTs tend to accumulate and am- plify these errors in the autoregressive condition, rather than correcting them. To bridge this gap, we aim to break the error-free training hypothesis with Error-Recycling Fine-Tuning (ERFT) to stabilize the DiT in long generation, which recycles the DiT's self- geffraenedd errors into supervi- sory prompts, thereby encouraging the model to correct its own mistakes from autoregressive error feedback. Mathematically, the error-recycling objective is defined as follows,
Given error-injected and clean inputs at random timestep t. ERFT aims to predict an error- recycled velocity: Virey = u(X,, Xings C, and V,z0y u(X,, X Ximg. C, 0), respec- tively, to stabilize DiT autoregressive generation, which consistently points to clean latents Xvid, regardless the correctness of the current state X, and historical trajectory before t.
We achieve this via a closed-loop fine-tuning: inject errors E made by DiT simulate degradation (Sec. [.I). calculate and save errors E' (Sec. 4.2), and dynamically bank and resamples errors E for new input (Sec. 4.3p, finally optimizing error-recycled velocity V.zer (Sec. 4.4).
4 STABLE VIDEO INFINITY
4.1
FINE-TUNING
Given clean video clip T> (typically using padding) latent via 3D VAE that both e [XX Then, we randomly sample a noise Xnoi E Xx W drawn from No0, 7) and a timestep t E Tiaa to train the video DÝT.
Twia and reference image 4, we extract the video Xvid and image Xing
Error Injection. Unlike existing works assuming clean input, we aim to simulate error-ate arror-accumulatd degradation occurring in inference. Given clean input Xvid, Xnois Xing. we design three types of errors accordingly: Evia, Enois Eing. These errors are resampled from the memory banks B Baoi> which are explained in the next sections. Then, we inject errors into clean inputs probabilisticall::
Xvid -Vvid + Lvid
Evid.
X Xnoi + Inoi Enois
Xing
Xing + Iung
Eung.
(3)
Here, 1, w.p. P. else controls probability p. of error injection. This design aims to simulate the randomeess and complexity of error accumulation appearing in any inference timestep. To pre- serve the generation ability with corrected errors, we set a probability p = 0.5 using the error-free input. Hence, the ultimate input X, sent to DIT blocks is denoted as X, = Concat (x, X Ximg),
CCnnidering duality, use noise Froi and latent error Evii bidirectionally for theoretical completeness.
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(a) Error-Recycling Fine-Tuning (Sec. 4.1)
Optional Control [C
Optional Coub?
X
Error Injection
LSVI
vii
3D VAE
X
Pistt
X King
Selft
attention
Artenttion
Cross
Attention
attansion
Cross-
Atlention
Cross
Fing
Error
Timestep Tux
Predicted
Relference Framme with Fadsling
n
Eusi
Velecity
Disretioation
Blameetwiae
Erior Sampling
L Tau Timestio
Xvia
Onesten forword Integration (Fig 4 top)
Subtraction
Bank Byid
Latent Erroe
Ooordted
Tim-to
Error Lipootic
Pirid
Lateee
Prediction Approximation
Video Latent
bachward Integration (Fig, bottoon)
Clean Image
Latent
Bank Basi
Noise Error
Dhurelioe
Timostip
E
Clean Nokie
Nose
(c) Error Replay Memory (Sec. 4.3)
(b) Bidirectional Error Curation (Sec. 4.2)
Figure 3: Stable Video Infinity. We (a) inject errors into clean latent to break the error-free hypoth- esis, (b) approximate predictions via one-step integration to calculate bidirectional errors, and (c) dynamically bank and resample errors from memory for clean inputs, in closed-loop cycling.
where X, vii + (1 (1-t) t) noisy video latent with errors. This error-injection can funda- mentally break the previous error-free hypothesis in Eq. [ serving to bridge the train-test gap.
Control Injection and Velocity Prediction. To satisfy in-the-will applications, we propose to extend SVI with extra controls C' {Cviss Cemb J. justified in Fig. 7b. (a) Cvis is the visual condition ensuring the spatial-level control on videos, e.g., the skeleton, which are injected at the tokenized input via glement-wise addition. Cvis can achieve precise control of spatial composition, serving tasks like dance animation. (b) Cemb is the embedding condition for the multi-modal control without spatial constraints, e.g., text and audio for talking animation. Cemb is injected via specific cross- attention layers in DiT' blocks. Hence, the error-injected input X, is tokenized, optionally with Cvis- and sent to DiT blocks with optional Cemb to predict the velocity: V22 u(X Xing 0)t;0).
4.2 BIDIRECTIONAL ERROR CURATION
Given the velocity V. we approximate error-embedded predictions by single-step integration bidi- rectionally for efficient error curation, which avoids the prohibitive cost of solving full ODEs.
Prediction Approximation. To tackle the complexity of accumulated errors, we delve into different error-inject scenarios in Fig- to calculate errors. Aligning with our main objective (Sec. 3.3), define the ground-truth error-recycled velocity Virey (green single arrow) pointing to the error-free latent Xvia, independent of the historical trajectory and current state. Then, with error-injected noisy latent X, and predicted velocity V. (red single arrow), we can approximate video latent Xvia and conditioned noise X-img via one-step forward and backward integration, respectively (red dotted line): Xvid X, + St V, ds; X-ing = X2 5 V, ds. Similarly, we deploy the integration on V. to obtain the error-recycled latent and noise: X X = X, + 1 Viey ds, X X, f Viey ds.
Error Calculation. With approximated predictions and error- recycld ground-truth, examine each error-injected case in Fig. [4] We first present a unified formulation applicable to all cases:
Evia Xvid - x
Enol = x
rey Eing Uniir(Evia).
where, Unif(.) is uniform sampling at the temporal axis T in the latent space. We then prove this unified error curation aligning with Sec. b.3 detailing each real case as follows.
(a) No Injected Error. This can simulate the initial single-clip predictive error that the predicted velocity V, is shifted anytime. Here, we define the latent and noise error with residuals: Evid = Xvia - Xvid and Enot = X - xing where we also have x Xvia and X = ximg (b) Error-Injected Start Point. This can simulate the cross-clip conditional error when the error causes shifts at the beginning from Ximg to X-ing Here,
intervene clean X Ximg or X
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(a) No-Injected (No Intervaation)
(b) Error-Injected Start Point (on Noise and Reference image)
(c) Error-Injected End Point
Trajectory
(on Video Latent)
Eron-njjcted Trajectory
+ Racyeling
Preal
injccion
Error injection
velecity
Bett
X X
Predicted
X,
X
Xxtt
ting
X psil
iisk
X
X
X
Xest
Xysi Xvid
Inlegration
X-ing
V/
Frack
X1
Crror Irjection
Celculation
X2 1/
Xxa
injection
X kig
X,
Extersion
Cord
X
X
Figure 4: Error calculation. In different cases, the latent error Evid and noise error Enoi are calcu- lated by the one-step integration in the forward (top) and backward direction (bottom), respectively.
to simulate the error impacts. With error-recycled velocity, can calculate bidirectional error with Evid Xvvi Xvid and Enoi X pon where we have X Xvid and X X - (c) Error- Injor-njected End Point. This can simulate both error types from an accumulated perspective, where the previous integration points to degraded generation Xvid. Our stabilized DiT is encour- aged verify and, if necessary, correct the wrong historical trajectory and wrong intermediate state X2 towards degraded latent X To align with the enror-recycled velocity, the errors can be written Xvid and Enoi X Xiing
Evid Xvid
X where only have X Xvid-
4.3 ERROR REPLAY MEMORY
Error Update. We propose to dynamically save calculated errors Evid and Enoi into two replay memory Byid and Bhoi according to timestep, respectively. To better reduce the train-test gap, we first discretize the training timestep Tira fim that is typically Nura 1000 by aligning it with the timestep used in test stage Tusst (L, ) n=1
Mest with typically Ntest = 50 as Eq. 2 Specifically, given a training timestep E Tia. we retrieve the nearest timestep grid ln in Treat and save each er- {vid, noij. Here, the timestep is a pointer pointing to the specific storage location. Considering the slow bank updates caused by the limited per-GPU sample, we design warmup by saving errors with cross- moss-me gathering, inspired by Federated Learning (MeMahan et al. (017). To conserve the memory usage, set upper bound Z 500 for the number of saved errors [B. When the specific bank Bi,, is full, we replace the most similar one by measuring the Toz distance between the error F. and historical errors in Ba,,, preserve error diversity.
ror E. into the corresponding location B.., in bank B. {B,,. Mers where
Z, justified in Appx
Error Sampling. Considering specific roles of error terms, we design a selective sampling method based on the individual properties of each input term in flow-matching trajectories. Specifically, aligning with our error banking, we first discretize the training timestep / sampled from Tira to the test timestep tn E Trest by retrieving the nearest one. Then, for input terms Xvid. X Xing in Eq. 3 the resampled errors are designed as follows accordingly.
(5) Here, (n.,,) is uniform sampling conducted on the memory bank B.,, E B. for the timestep tn and Unifr is performed across two dimensions: the whole timestep in the noise scheduler and the temporal axis of the video. The rationale for each selective strategy is explained as follows.
Evia (niddB.),. Enoi Unif(B,....). Emn = Univa(Bria).
(a) Video Latent Error Evid is uniformly sampled from the timestep-alignede bank Brid, because the step-wise errors predominantly depend on the current timestep I,, throughout the trajectory. We also empirically find that degradation types are highly correlated with sampling steps.
(b) Noise Error Enoi, following the video latent, is sampled uniformly within the same timesteps from Buoi.ss considering the duality between the noise (start) and latent (end).
(c) Image Latent Error Eing is sampled from the video bank to align with the cross-clip autoregres- sion, ie.. the generated frame serves as the reference image in the next-clip generation. Unlike the step-wise error, the reference image is obtained by integrating over all timesteps, which accumu- lates errors over the entire trajectory. To simulate this complexity, we sample Eing across timesteps independently of the current l, because the error may occur and accumulate any timestep.

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Published as a conference paper at ICLR 2026
Generated
Subject
Hackground Consistency (single text prompt without
Aesthetic
Imaging Quality
Dynamic Degree
Motion
Models
Scenes
Consistency
Quality
Smoothness
Consistent Video Generation
scene transitions)
Wan 2.1
Single
87.03%
92.45%
56.40%
65.70%
12.68%
98.51% %
Streaming T2V
Single
84.79%
89.27%
56.81%
66.41% %
57.04%
99.00%
HistoryGuidance
Single
83.77%
90.90%
40.42%
55.48%
4.93%
99.38%
FramePack
Single
93.08%
94.72%
63.57%
66.72%
7.75%
99.57 %
SVI-Shot (Ours)
Single
98.13%
98.19%
63.84%
71.88%
17.61% %
98.93%
Ultra-Long Consistent
Video Generation (single text prompt without scene transitions)
Wan 2.1
Single
80.00%
87.27%
56.19%
65.37%
14.29%
98, 74%
Strcaming T2V
Single 66.32%
77.62%
40.49%
55.18%
85.71%
95.60%
History Guidance
Single 64.84%
80.51%
29.84%
50.41%
7.14%
99,42%
FramePack
Single 79.37%
86.64%
55.66%
57.61%
0.00%
99.63%
SVI-Shot (Ours)
Single
97.50%
97.89 %
65.75% %
71.54%
21.43%
98.81%
Creative Video Generation (text prompt stream with scene transitions)
Wan
Multiple 81.44%
89.81%
51.33%
53.09%
61.97%
98.57%
SVI-Film (Ours)
Multiple
84.25%
90.85%
55.25%
59.97%
62.68 %
98.69%
Streaming T2V
Single
81.01%
88.47%
52.20%
58.05%
61.97%
98.96%
HistoryGuidance
Single
84.12%
91.21%
38.40%
52.31%
7.75%
99.39%
FramePack
Single
85.62%
91.22%
59.41%
59.44%
9.15%
99.49
SVI-Shot (Ours)
Single
93.52% %
95.86 %
58.07%
62.81
55.63%
98. 18.422%
Ultra-Long Creative
Video Generation (text prompt stream with scene transitions)
Wan 2.1 SVI-Film (Ours)
Multiple 67.85%
83.45%
46.68%
43.36%
57.14%
98.56%
Multiple
69.84%
84.46%
51.22%
53.93%
78.57
98.50%
Streaming T2V
Single
68.65%
82.00%
44.69%
55.20%
78,57 %
96.95%
Guidane
Single
62.58%
81.97%
28.66%
47.68%
7.14%
99.36%
FramePack
Single
70.95%
83.46%
52.39%
53.72%
0.00%
99.48%
SVI-Shot (Ours)
Single
91.96%
95.04%
63.31
65.25%
64,29%
97.97%
Table 1:
Generic video generation with diverse settings. Bold, Underline highlights the highest,
second highest, respectively. For more details on metrics, see (Huang et al. 2024b).
Models
Sync-C1
Sync-D
FVD
Models
PSNR
SSIM
FVD.
Wan 2.1
0.21
12.86
934
Wan 2.1
12.12
0.33
4099
MultiTalk
1.26
9.57
520
UniAnimate-Dit
18.97
0.69
337
SVI-Talk (Ours)
6.12
8.74
390
SVI-Dance (Ours)
20.01
0.71
299
Table Audio-conditioned long talk.
Table 3: Skeleton-conditioned long dance.
4.4
OPTIMIZATION
To train Stable Video Infinity, we aim to predict error-recycled velocity V.T0 clean latent Xvid from error-injected inputs Xvid, Xnni, Xing obtained via Eq This aligns with our error-recycling objective (Sec. 3.3) in bridging the train-test hypothesis gap. denoted as follows,
Xxid - pointing
Lsvi = EX X Xing, uu( (x,, Ximg. C,t:
V.roy
(6)
where X, t.8vid + (1 &noi noi is noisy latent with injected errors. To enable user flexibility, only train LoRA. Our error-recycling tuning can actively correct the trajectory, unleashing DiT's restoration ability. Overall, in line with Henry Ford's 's quote at the beginning of this manuscript, we can rephrase it in the context of long video generation as: "The accumulated error simply an opportunity to begin again by recycling the errors. this time more stable Stable Video Infinity"" ."
EXPERIMENTS
Benchmarks Setup. We establish three benchmarks, consistent, creative, and conditional settings, for image and text-to-video generation (each has two variants), satisfying diverse industrial needs. (a) Consistent Video Generation aims produce 50-sec and 250-sec (ultra-long) videos from an unchanging text prompt within one scene. (b) Creative Video Generation targets the needs of vlog- gers (e.g., TikTok) by emphasizing storytelling with plausible scene transitions. We develop an automatic engine via MLLM (see Appx. A) to generate prompt streams for videos of 50-sec and 250-sec (ultra-long) duration. (c) Multimodal Conditional Generation measures compatibility with extra conditions. We evaluate 300-sec audio-guided talking and 50-sec skeleton -uidedd dancing. Metrics. We use 6 core metrics from Vbench++ (Huang et al. 2024b) for global video quality. For specific conditional generation,
Syne-C, Sync-D, FVD, PSNR, and SSIM metrics.

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Published as a conference paper at ICLR 2026
(5)
Aesteelc Quality (5)
[dI
2003
Sumber frarns
Wamber
Number
Number
Figure 5: Stability comparison about video length. SVI is more stable without an obvious decrease.
Sub.
Back.
Aest.
Img-
Method.
Cons.
Cons.
Qual.
Qual.
Wan 2.1
66.73%
82.83%
43.95%
42.31%
Rust
SV1 we Ei
73.82%
84.21%
49.58%
57.63%
SVI wis Floi
94.22%
94.87%
59.80%
69.90%
SVI we Evia 93,56%
95.01% %
58,99%
71.50%
SVI full
94.69%
95.39%
61.88 %
71.22%
Exst
Eyis by Wan 2.3
Eute by SVI
Table 4: Ablation study on each error term.
Figure 6: Error correction comparison.
Implementations. We deploy a family of SVI with minor modifications for diverse usages, which is only trained with 300-6k short videos (see Appx. D). (a) SVI-Shot aligns with the previous long video generation, focusing on homogeneous scene. Here, training, we conduct padding with a random image the anchor to obtain the image latent, and replace it with the reference image in inference. (b) SVI-Film supports end-to-end long filming controlled with a storyline- based prompt stream. We use five motion frames and replace the padding frame with zero for the image latent. (c) SVI-Talk targets audio-conditioned human-ttlkinng videos, which extends SVI-Shot by injecting the audio-image cross-attention from (Kong et al. 2025). (d) SVI-Dance is for skeleton-guide dancing videos, which encodes the skeleton and injects it into input tokens following (Wang et al. 2025b) Eung. Evid, and Enoi are injected with probabilities 0.9, 0.9, and 0.01, respectively.
5.1 LONG VIDEO GENERATION IN THE WILD.
We compare with tfate-of-thearr methods: StreamingT2V (Henschel al. 2025). HistoryGuid ance (Song et al. 2025). FramePack (Zhang & Agrawala 2025), and report Wan 1 (Wang et al.) 2025a). We compare conditional generation with MultiTalk (Kong et al., 2025) and UniAnimate- DiT (Wang et al. 2025b) in audio-guided talking and skeleton-guide dancing, respectively.
Consistent Video Generation. In Tab. top, SVI-Shot achieves the best results on most core met- rics. Note that an abnormally large dynamic degree in this setting indicates uncontrollable motion degradation. Compared with FramePack, we give 5.05% and 3.37% gains on consistency, 5.16% gains on image quality. Most methods suffer from large drop when extended ultra-long videos, such as 7.03% and 13.71% subject consistency decrease for Wan 2. and FramePack. In contrast, SVI exhibits a negligible 0.63% decrease, while maintaining a satisfactory degree of dynamics.
Creative Video Generation. Tab. bottom compares the long videos guided by storyline- prompt stream, which has frequent scene transitions. Note that existing long video works uniformly fail, they cannot generate filming-level scene transitions with the prompt stream (see Fig. ( SVI achieves the best consistency, quality, and satisfactory dynamic degree with significant gains. This superiority is maintained when extended ultra-long settings, showing the stability of SVI.
Multimodal Conditional Generation. In Tab. and 3 we justify our adaptability with two typical conditions Cemb with audio-guided talking, and Cvis with skeleton-guie guidddd dancing mentioned in Sec. 4. It can be observed that SVI can effortlessly adapt to specific domains and enhance the state-on of-teoof-the-ar in long videos, verifying its versatility for in-the-wild generation.
5.2 FURTHER ANALYSIS
Stability to Video Length. Fig. compares the robustness among the latest long video methods by measuring the consistency and quality. Unlike all existing works exhibiting decreasing trend, SVI can maintain robust and high consistency and quality. This nature justifies the ability to generate an arbitrary length, which, for the first time, fundamentally corrects errors and breaks the time limit.
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x
SvrtoingT TV
FramePack
(a) Creative Video Generation
Audio-guided Talkiing (Cousa)
Skeleton-guided Dancing (Cea)
Straming
MubTak
Uniivatmate DIT
FromePack
(b) Consistent Video Generation
(c) Multimodal Conditional Generation
Figure 7: Qualitative comparison with the best apecific-domain methods (see videos in Appx B
Ablation Study. In Tab. top, we analyze each error with a I-epoch tuning for proof-of-concept. revealing 2 critical messages. (a) Removing errors on the reference image Fing gives significant drop all metrics. This indicates the primary role of intervening on the trajectory start (Fig. 4 to simulate the error accumulation, justifying its direct and major role in solving the train-test hypoth- esis gap. (b) Injecting errors into the video latent Evid or noise Enoi gives an auxiliary benefit in an indirect role compared with the major factor Eaug. More ablations are in Appx. C
Error Visualization. Fig. 6 visualizes the decoded error Evia and Enoi, and compares the prediction Evid. where we have two observations.
(a) Video generators (Wan 2.1) are sensitive the errors they make. leading to degraded prediction. This issue can be tackled by the error-recyllnng fine- tuning SVI, achieving robustness of self-errors.
(b) Injecting errors can well simulate the drifting (Xvia by Wan 2. 1), justifying the critical role of error recycling in bridging the train-test gap.
5.3 QUALITATIVE RESULTS
Long Video Comparison. Fig. a compares Creative Video Generation guided by a prompt stream. Existing works fail to achieve scene transitions with severe quality degradation. In contrast, SVI achieves smooth scene transitions, maintaining high visual fidelity and text- following, folloon.,n which paves the way for end-to-end filming. In Fig. Zp. compare Consistent Video Generation with an unchanged prompt, showing that existing methods suffer from color shifts, motion drifts, and degradation in static images. Differently, SVI generates temporally coherent videos with plausible consistency and dynamics. Fig. ? compares Multimodal Conditional Generation between SVI and state-of-the-ars counterparts in talking and dancing videos. Without being tailored these domains, SVI can effortlessly tackle the drifting in long generation, justifying its effectiveness and transferability. Refer to the videos in Appx. including additional cross tross-domain adaptability.
Extremely Ultira-Long Video Generation. In Fig. 8 we explore the generation with 1 15-minute videos to verify the ultra-long, non-looping ability. Generic-Domain Generation: We use an LLM to generate 200 random prompts and perform end-to-end generation, conditioning each clip on its own prompt. This example shows SVT's potential to produce arbitrarily long videos across diverse, open-domain scenarios. Human-Centrie Generation: We use long audio track to drive talking video generation. Each clip is conditioned on a different segment of the audio, so the motion does not loop. This example shows that SVI can maintain stable identity within single scene.
Potential Long-Range Consistency. We further fine-tune tne-tune SVI-Shot on long-video datasets to en- hance the long-range dependency and motion diversity. In Fig. 9 observe emergent long- range identity consistency happening in cross-clip conditioning without using identity-relater text prompts. Here, the woman protagonist completely exits the current scene at 30 seconds, i.e., the end

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Published as a conference paper at ICLR 2026
Freme 5,000
Frame 5,500 Frame 10,000
Frame 10, 500 to Frame 20,000
Frame to Frame 20,00 000
Figure 8: Extremely ultra-long generation results (10+ minutes). The videos generated with long
prompt streams (top) or different audio clips (bottom), showing the non-looping and diverse nature.
Clips 5
dip.5
Cipp
Cip 5
Clip
Figure 9: Visualization of last frames of generated video clips. The woman exits the current scene and then re-enters the next clip with similar personal information, e.g., haircut and clothes.
of the 6th video clip, and then re-enters the next clip with similar personal information, e.g., haircut, and clothes, showing some potential for human-centric generation.
CONCLUSION AND DISCUSSION
We address the training-test hypothesis gap in long video generation, which leads to two forms of accumulated error (Sec 3 To bridge this gap, propose Stable Video Infinity to break the time limit by actively correcting the self-generated errors, which employs a novel Error-Recycling Fine-Tuning (Sec. 4 to autoregressively learn from error feedback. Unlike the error-free training assumption, SVI deliberately injects historical errors into clean inputs and learns to predict an error- recycled velocity that computes errors bidirectional one-step integration, stores them in a replay memory, and selectively resamples them for new inputs. Across three benchmarks, SVI surpasses state-of-the-art methods long, ultra-long, and conditional video generation.
Discussing Train-Test Gap. While sharing a similar goal, i.e., reducing the mismatch between training on clean contexts and inference under autoregressive, error-prone histories, the targets and strategies are different in some concurrent related works. Specifically, Self-Forcing (Huang et al. 2025) bridges the gap by performing autoregressive rollouts with KV cache during training and using distribution matching. Building on this, Rolling Forcing (Liu et al. (2255) addresses this gap by using a rolling-window joint denoising and an attention sink to suppress errors. LongLive (Yang et al. 2025) specializes in interactive, prompt-driven long videos by combining streaming long tuning with trame-sink. Some more recent works explore the RoPE and attention sink (Yesiltepe) al. 2025 Yi et al. 2025) for long generation in training- -free manner. In contrast to restructuring the video rollouts, SVI explicitly focuses on inference errors and encourages models to eliminate their own emrrr via error-recycling, achieving lightweight and flexible fine-tuning.
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ACKNOWLEDGEMENTS
This work was supported as part of the Swiss Al Initiative by grant from the Swiss National Super- computing Centre (CSCS) under project ID al44 Alps. We would like express our gratitude to Valentin Gerard, Tomasz Stanczyk, Megh Shukla, and Xiaoyuan Liu for insightful discussions.
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A BENCHMARK SETUP
A.1 AUTOMATIC PROMPT STREAM ENGINE
In this work, we study the creative generation using a storyline-driven text prompt stream. However, this is still an open problem in the community due to the lack of high-quality data and the laborious labeling process. To solve this issue and generate sufficient test data, we propose a fully automated system for effortless, end-to-end short film production that streamlines creative video generation and evaluation. The only user input required the high-level subject specification (e.g.. "dog" and "street"). The system then automatically retrieves and downloads relevant images and uses Multimodal Large Language Model (MLLM) to generate storyline-aligner text prompt stream for each video clip. These image-prompl pairs are subsequently fed into Stable Video Infinity to produce high-quality narrative-driven short videos of unlimited length.
Fully Automatic Short Filming Pipeline driven by Stable Video Infinity
User Defined
Story Length: (4]
Automatic Retrieval and Download
Auto storytellingg by
dog
stable video infinity
User
Qwen2.5 5
Defined
PEPREEE PERMPRC
Topics:
dog,
ANHEROTE
street,
Infinite Short Films
User Need
Image
Prompt Stream
1. Keyword >> Image
2. Image Storyline
3. Storyline Short Film
Overview of the proposed end-to-end automatic pipeline, which is able to generate in- finite short films from user-given keywords. This engine is used generate the prompt streams according to a specific storyline for our creative video generation benchmarks.
Figure 10:
The complete workflow is illustrated in Fig. 10 This pipeline systematically transforms high-level keywords into structured pairs of images and prompt sequences, eliminating the need for labor- intensive manual annotation and prompt engineering. The pipeline operates follows:
1. Keyword-based Image Retrieval (Keyword Image): set of user-defined keywords (e.g., "dog" and "street"). These keywords are fed into an automated download script, which retrieves a diverse set of relevant images from online resources. Optionally, the user can also skip this process by directly using the customized images instead of automatically retrieving them online.
The process commences with
2. Automated Prompt Stream Generation (Image + Storyline): Each retrieved image is then processed by the Qwen2.5 (Yang et al. 2024a for auto-labeling. Critically, instead of generating a single, static deseription, our labelin module is configured to produce temporally coherent sequence of L distinct prompts. This sequence, which we term "prompt stream",, is designed to describe plausible dynamic evolution or narrative origi- nating from the static dog, the prompt stream might describe the dog waking up, perking its ears, and then wag- ging its tail. The length of this stream, L, controllable parameter.
in the input image. For example, given image of a resting
our benchmark,
I. is set to 10 for creative video generation (10 sequential video clips in one long-shot), and set 50 for the ultra-long setting. Optionally, users can also skip this step by directly providing their prompt streams and storyline instead of generating them via MILMM.
3. Input Preparation and Video Synthesis (Storyline Short Film): Concurrently with the prompt generation, the initially downloaded image undergoes standard normalization procedures, which sent to SVI. The normalized image, paired with its corresponding generated prompt stream (prompt 1, prompt 2...., prompt L }. serves as the complete input model. SVI will iteratively generate video clip for each prompt within the prompt
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stream, which uses the last frames of the previous generation the conditions. The model then synthesizes a video by conditioning the animation of the input image on the sequential instructions provided by the prompt stream.
This automated pipeline is central to our study. It enables rapid generation of large and diverse suite of test cases for qualitative evaluation and provides a scalable framework for benchmarking the model's ability to interpret static content and animate it in response to dynamic textual guidance.
A.2 BENCHMARK DATASETS
With our automatic prompt-stream engine, we are able to efficiently construct high-quality test data spanning both creative and consistent video generation scenarios. To better mirror real-world usage, two-thirds of the dataset is automatically harvested from the web through our engine, while the remaining one-third is sourced from real users following (Zhang & Agrawala 2025). ensuring balanced mix of scale, diversity, and authenticity. We will open-source the full codebase and all benchmark datasets to catalyze progress in long-form video generation and evaluation.
For generic video generation, we assemble 152 samples under the 50-second default setting and 14 samples under the 250-second ultra-long setting, and we evaluate all benchmarked methods under identical conditions to ensure fair comparison. In the consistent video generation track, each input image is paired with single, stable texl prompi across both duration settings, emphasizing temporal coherence and identity preservation. In contrast, the creative track introduces narrative dynamics: generate prompt stream comprising 50 rextual descriptions for the default setting and 100 for the ultra-long setting, enabling rich, storyline-driven scene transitions and event progression. this setting, each generated video clip (5 seconds, 16 FPS) will follow unique prompt. Finally, for mul- timodal conditional generation, we evaluate 10 ultra-long samples across all benchmarked methods, probing the models' ability align and fuse visual inputs with evolving multimodal guidance, e.g.. audio and skeleton, over extended durations.
Together, these settings provide a comprehensive, scalable, and strict benchmark suite that stresses both fidelity and creativity. supports controlled ablations and cross cross-method comparisons, and re- flects the practical requirements of long-vides creation in the wild. This can also break through the previous long-vide video evaluation (Zhang & Agrawala 2025 Henschel et al. 2025 Song et al.) 2025). only focusing on a single homogeneous scene with repeated motions, which has been fully tackled by the proposed SVI-Shot, as proved by Tab. in the main paper.
B DISCUSSION
B.1 BROADER PRACTICAL IMPACT ON INDUSTRY
Filming and Entertainment. Contemporary short-form video production typically requires sub- stantial manual effort to design scene transitions, craft storylines, and stitch together video clips, making true single-take narrative vidcos impractical. Differently, our Stable Video Infinity makes end-to-end, single-take filmmaking feasible and accessible. Users provide only high-level intent and brief textual descriptions; the system then autonomously produces unlimited single-shot videos with controllable pacing and visually plausible transitions, without any human intervention.
Robotic World Models. Existing world models (Wang et al. 2024) for robotics (Li et al. 2025 and simulation (e.g., Cosmos (Agarwal et al., 2025g) are constrained by short video horizons and limited training diversity, making it difficuli to simulate prolonged, complex scenarios rare out-of- distribution corner cases. Our Stable Video Infinity demonstrates strong potential for long-duration controllable, and semantically consistent scenario synthesis, particularly in the navigation domain.
World Generation, Gaming, and Spatial AI. We observe strong long-term geometric and iden- tity consistency across scenes from the proposed Stable Video Infinity a property that can cat- alyze progress in large-scale world generation and spatial AI. Our pipeline provides controllable, cerrative-conditiona evolution of scenes while maintaining structural coherence, beneficial for search in 3D-aware video synthesis, continual scene modeling, and interactive agents that reason
over persistent environments (Che al. 2025 Yu et al. 2025
Zhu et al. 2024).
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B.2 BROADER METHODOLOGY IMPACT ON ACADEMIA
Our central goal is to bridge the train-test hypothesis gap in autoregressive generation. This gap pervasive across modern generative paradigms, including large language models (LLMs). and MLLM. During training, models are typically exposed to clean, error-free inputs; at test time, how- ever, autoregressive generation conditions each new token (or frame) on previously generated out- puts, which may contain errors. Hence, this hypothesis gap is also a challenge in LLM/MLLM training. Our error-recycling tuning exposes the model to its own imperfect rollouts and teaches to recover from errors. In doing so, the optimization becomes aligned with the test-time process,
This principle generalizes beyond video to a broad class of generative settings, including LLMs, autoregressive image generalive modely. In long video generation, the drift and compounding arti- facts that destabilize content can be viewed as a form of visual hallucination. This has a similar property the open problem, linguistic hallucination in the context of LLM and MLLM, i.e., LLM tends to give hallucinated words when the generated context is too long. Based on this insight, our error-recycling tuning potentially mitigates this effect by closing the train-test gap: the model learns robust policies for stabilization, re-grounding, and content repair under distribution shift.
B.3 LIMITATION AND FUTURE WORK
Scaling Up. Due to time constraints, our models were trained on small datasets without scaling up. We observe that when test-time image styles diverge from the training distribution, adjacent clips can exhibit color shifts. A likely cause is that the model incorrectly treats test-time low-level style as an eгror and "corrects" it. We plan to scale up data and diversify styles to correct this "misunderstanding" apply domain- balanced sampling, and incorporate stle-preservation losses or reference-style conditioning to reduce such artifacts. Additionally, curriculum scaling, mixed/high- resolution training, and stronger augmentations should further improve robustness to style shifts.
Real-Time and Interactive Generation. Our current model, built on Wan 2.1, generates frames in parallel rather than as a stream, which poses challenges for real-time deployment. Recent work (e.g.. Caus Vid (Yin et al., 2025), Self-Forcing (Huang et al., 2025 has begun to explore streaming generation. Because our method only trains lightweight LoRA adapters, it can be seamlessly inte- grated into a real-time pipeline. In future work, we also plan to pursue real-time, flfiite-horizon video generation and incorporate interactive control signals (e.g., live prompt updates, joystick-like trajectory guidance, and event triggers) for responsive editing and steering.
ID Consistency. In SVI-Film, we maintain cross-clip motion continuity by conditioning five mo- tion frames. However, without explicit long-term memory, when the main character exits the frame, identity drift or swapping can occur. While SVI-Shot/Tik//Danee have achieved identity control via anchor frames, they currently do not extend to creative generation with scene transitions. We intend to develop an end-to-end filming pipeline that combines persistent identity embeddings, cross-shot anchors to strengthen subject consistency across complex transi- tions. Some advanced anchoring and memorization strategies will be proposed.
feature caching, and
B.4 CLARIFICATION OF LLM USAGE
In this work, LLMs are used for writing refinement and grammatical checking, in strict accordance with ICLR guidelines. I.MSs are not involved in the conception, methodology, and other sensitive components. In qualitative comparisons, we employ LLMs to assist with ancillary Python scripting tasks (e.g., vframe extraction, clip concatenation, and related utilities) and the Readme document.
B.5 ETHICAL CONCERNS
All used and benchmarked models and the corresponding training data used in this work are sourced from openly available datasets. We do not use proprietary, restricted, or sensitive data. For the retrieved test data, we have checked the permissive licenses. This study does not involve human subjects, biometric information, or biological data, and therefore does not raise associated human- subjects risks. Regarding human talking videos, recognize potential misuse risks such as deep- fakes and fraud. To mitigate these risks, future open-source releases will include compliance con- straints and guardrails to discourage malicious use and support ethical deployment.
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Generated
Subject
Background Consistency
Aesthetic Quality
Imaging Quality
Dynamic
Motion
Models
Scenes
Consistency
Degree
Smoothness
Copy Clips Ping-Pong Clips
Single
98. 48%
98.60%
67. .999
71.93%
7.14%
98.93%
Single
98.5 1%
98.59%
67.92%
71.92%
7.14%
99.06%
Copy Reference Img
Single
100.00%
100.00%
68.55%
73.05%
0.00%
99.84%
Wan 2.1
Single
80.00%
87.27%
56.19%
65.37%
14.29%
98.74%
StreamingT2V
Single
66.32%
77.62%
40.49%
55.18%
85.71%
95.60%
HistoryCuiiance
Single 64.84%
80.51 %
29.84%
50.41%
7.14%
99.42%
FramePack
Single
79.37%
86.64%
55.66%
57.61%
0.00%
99.63%
SVI-Shot (Ours)
Single
97.50%
97.89%
65.75%
71.54%
21.43%
98.81%
Table 5: Exploring naive video extension methods. The best is highlighted in red (abnormally large).
Sub.
Back.
Aest.
Img.
Error
Back. Cons.
Aest. Qual.
Img.
Cons.
Cons.
Qual.
Qual.
Cons.
Qual.
0.2
92.16%
93.68%
58.52%
77.49%
Self Only
69.34%
83.14%
52883
56.97%
Handcraft Only
69.21%
83.65%
49.62%
45.17%
0.4
97.49%
96.73%
59.86%
77.32%
SelE4Handeraft
69.24%
83.48%
48.51%
39.50%
0.8
99.59%
99.37%
59.69%
78.11%
1.0
98.54%
97.97%
59.88%
78.12%
Table Comparison between self-generated errors
Table 7: Analysis error-recycling intensity
and handcraft errors with image augmentation.
by modifying LoRA alpha.
C QUANTITATIVE EXPERIMENTS
Exploring Naive Ways of Fooling Metrics. Tab 5 we further study three types of designs fooling metries: (a) Copy Clips, which copies the first generated video clips 50 times naively; (b) Ping-Pong Clips copies the first generated video clips 50 times in a ping-pon manner, and (c) Copy Reference Image only naively copies reference images by 50x81 times. We can see that these naive methods can successfully fool and attack some metrics, e.g., consistency and quality, showing some limitations of existing video generation evaluation. Correspondingly, methods with abnormally high values on these metrics tend to exhibit abnormally low counterparts, for example, a 0.00% dynamic degree. Hence, we can learn a valuable message that it is necessary comprehensively consider all metrics together for evaluation to prevent metric fooling. Our SVI gives a satisfactory trade-off' among all metrics, revealing its effectiveness.
Comparison between Self-generated Error and Naive Image Augmentation. In Tab. 6 we apply handcrafted degradations to the reference image, including random color shifts, blur, and sharpness, and compare with our self-generated errors. We observe that the naive image augmentations not only fail help but also substantially degrade image quality. Moreover, combining self generated and handcrafted errors causes severe conflicts, leading to further drops. These results suggest that accumulated, model-induced errors possess unique characteristics that are difficult to mimic with manual augmentations, showing the necessity of learning from the model's own errors.
Analysis on eryoe-ne Intensity. In Tab. 7 bottom, we gradually adjust the error-recycling intensity by changing LoRA weight in test, where lower value means weaker effect. Compared with a = 1, there is a consistent decrease in all metrics when reduced from 0.8 to 0.2, indicating that the more catastrophic errors appear when weakening the error correction ability. Hence, this can justify the necessary role of correcting errors actively.
Analysis Error Bank Size. Tab. evaluates the effect of varying the error bank size Z on model perfor- mance across multiple metrics. Ex- ceedingly limited error bank sizes, such as Z = or Z = 10, restrict error diversity, leading to suboptimal performance in Subjective Consis- tency, Background Consistency, Aes- thetic Quality, and Image Quality. As Z increases, all metrics show consis- tent improvements. However, beyond Z 500, performance saturates, with most metrics exhibiting no significant gains or slight declines. Our selection of Z = 500 achieves satisfactory performance, effectively balancing error diversity with model capability.
Sub.
Back. Cons.
Aest.
Img.
7
Cons.
Qual.
Qual.
67.82%
82.90%
51.55%
52.96%
10
67.41%
81.79%
52.12%
54.29%
100
68.51%
82.97%
51.03%
55.30%
500
69.34%
83.14%
52.83%
56.97%
1000
69.14 69.14%
83.83%
51.36%
55.06%
2000
69. 69.02%
82.98%
51.39%
54.55%
Table 8: Ablation study on error bank size Z.
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LoRA Rank
Subject Consistency
Hackground Consistency
Aesthetic Quality
Imaging Quality
Dynamic Degree
Motion
Smoothness
0 (baseline)
66.23%
81.96%
45.55%
44. 14.29%
62.50%
98.12%
64
95.37%
95.76%
62.17%
70.34%
31.25%
98.95%
128
96.10%
96.75%
62.84%
70.62%
25.00%
99.12%
Table 9: Exploring different LoRA ranks, where larger ranks indicate more trainable parameters.
Earor Injection
Subject Consistency
Background Consistency 89.81%
Aesthetic Quality
Imaging Quality
Dynamic Degree
Motion
Models
Smoothness
Wan
No Error
81.44%
51.33%
53.09%
61.97 %
98.57%
SVI
Offline
93.52%
95,86%
58.07%
62.81%
55.63%
98.42%
SVI
Online: Single-pass
94.65% %
95.65% 95.89 %
58.669
64.85%
47.74%
98.92%
SVI
Online: Two-pass
94.96%
59.31 %
65.24%
35.48%
99.08 %
Table 10: Exploring different online error injection variations.
The Sufficiency of LoRA Tuning. In Tab. to further study the required capacity of LoRA, we conduct studies on the LoRA rank, where the larger rank indicates more trainable parameters with larger capacity. Compared to the baseline, even a small-parameter version with rank 64 already yields a clear performance gain. Moreover, when increase the rank from our 64 to 128, the improvement is marginal, suggesting that LoRA has sufficient capacity to remove self-errors.
Exploring Online Error Injection. In Tab. 10 we further compare three error-injection variations. (1) Offline injection. The injected error is drawn from the corresponding memory bank, as in our previous design. (2) Single-pass online injection. For each clean sample, first forward propard- once to estimate the error and compute the gradient for optimization. Next, we inject this sample's own error and perform another gradient update using the error-injected sample. (3) Two-pass on- line error injection. Similar to the single-pass setting, we first run the sample through the model estimate the first-round error. We then inject this error, perform forward propagation, and compute the accumulated eтror. Finally, we inject the second-round accumulated eтror and perform the gradient update using this cumulatively error-injected sample. We can find that these online variants slightly improve most metrics compared to the original SVI, except for the dynamic- metric. We hypothesize that this because online error injection enhances restoration capacity by better matching samples to errors, thereby improving consistency and quality, However, it may also reduce the diversity of sample-error pairs, thereby decreasing motion.
Evaluation via Existing Benchmarks. In Tab. II We further evaluate SVI via VBench bench- mark (Huang et al. 2024a) (both images and prompts). Due to the high computational cost evalu- ating long videos, randomly sample 50 test cases and assess performance at 50-clip lengths, in our main paper. The results show that SVI consistently outperforms other methods on the standard benchmark, verifying its effectiveness and generalization ability.
D IMPLEMENTATION DETAILS
The experiments are conducted on large-scale GH200 cluster. Detailed hyperparameters used for SVI training are shown in Tab. T2 We implement SVI based on Wan2.1-12V 1-22V-14-4800 and only tune LoRA to enable the flexibility, i.e., the user can effortlessly inject SVI into their private models. All the models/source codes/benchmark datasets will be made publically available.
Training Data. The proposed Stable Video Infinity is significantly data efficient it only uses small-scale publicly available data to fine-tune. In all settings, we only train SVI with 10 epochs. For the creative and consistent video generation setting, the proposed SVI-Shot and SVI-Film are trained with the MixKit Dataset (Lin et al. 2024) consisting of 6K videos. For the audio audio-guided talking.
random subset of Hallo 3 (Cui et al. 2025) containing 5,000 video clips for
training. For the skeleton conditional dancing, we use Tik Tok (Jafarian & Park 202T) for the error- recycling fine-tuning, where the LoRA is pretrained from (Wang al. 2025b).
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Subject
Background Consistency
Aesthetic Quality
Imaging Quality
Dynamic
Motion
Models
Consistency
Degree
Smoothness
Wan 2.1
76.11%
86.56%
56.19%
63.66%
54.00%
98.37%
HistoryCuidance
63.60%
82.10%
27.80%
48.70%
8.00%
99.37%
FramePack
78.
86.13%
54.64%
55.57%
0.00%
99.49% %
SVI (Ours)
96.24%
96.13%
64.36%
68.54%
30.00%
98.75%
Table 11: VBench benchmark results for long-video generation (50-clip).
Parameter
Value
Description
Learning rate
2.0e-05
Adam optimizer learning rate
Max epochs
10
Maximum training epochs
Gradient clipping
1.00
Gradient norm clipping threshold
Gradient accumulation
Gradient accumulation steps Distributed training
Training strategy
dtepspeed_staae..
Data workers
Number data loading workers
Gradient checkpointing
Yes
Memory optimization technique
Checkpointing offload
Yes
CPU gradient checkpointing
LoRA rank
128
Low-rank adaptation rank dimension
LoRA alpha
128
LoRA scaling parameter
LoRA init
kaiming
LoRA weight initialization method
Architecture
lora
Training architecture type
LoRA position
q.k.v.o.ffr wf..ff..fff.....
Target modules for LoRA Video frame height (pixels) Video frame width (pixels) Tiled inference for memory efficiency Processing tile height
Frame height
480
Frame width
832
Tiled processing
Yes
Tile height
34
Tile width
34
Processing tile width
Vidco frames
81
Number of video frames per sample
error-recycling tuning
Yes
Enable arro-recycling tuning Number of iter. gathering nulti-node errors
Warmup iterations
20
Noise error Pnoi
0.01
Noise injection prohability
Latent error Pvid
0.9
Latent error injection probability Image error injection probability probability without any error Discretized timestep grids for buffer Maximum errors saved each memory grid Number of motion reference frames Probability of using motion frame
Image error Ping
0..9
Clean input
0.5
Timestep grids
50
Maximum error
500
Motion grames
5
Motion probability
0.95
Table 12: Detailed hyperparameters used in the training and test.
E ADDITIONAL QUALITATIVE COMPARISON
The proposed SVI demonstrates capability in generating temporally coherent short films guided by text streams, evidenced in Fig. through Fig- 15 showcasing its potential for end-to-end storytelling and creative content creation applications. Beyond basic text-to-video generation, our method exhibits remarkable versatility supporting multimodal controls. As illustrated in Fig. [6 and Fig. 17 SVI achieves robust long-range video synthesis through both visual conditioning and embedding-based control, enabling precise manipulation of character movements and facial expres- sions. Please refer to the anonymous project Infitity-SIDE/ for the video comparison.
Video-
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StreamingT2V
FramePack
SVI-Shot (ours)
Prompt Stream
A United Airlines Boeing 777 aircraft captured in flight against clear blue sky.
The airplane descends smoothly towards the runway for safe landing.
A team of mechanics inspects the aircraft for any potential issues.
TUN
The flight crew members begin to deplane and prepare the aircraft for the next flight.
UUTAARRS
Figure 11: Qualitative results about airplane landing story.
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StreamingT2V
FramePack
SVI-Shot (ours)
Prompt Stream
A Siamese kitten rests snugly inside straw hat, its head slightly tilted it gazes curiously to the side.
The kitten decides to explore the room and jumps out of the hat onto the soft carpet below.
The kitten sees feather toy and immediately pounces on it, chasing around the living room.
Inside box, the kitten discovers a small toy mouse, which pounces with great enthusiasm.
After while, the kitten tires itself out and falls asleep on the living room floor, still clutching the toy mouse.
The next morning, the kitten wakes up with start and looks around, seemingly disoriented by its sleep.
Figure 12: Qualitative results about the cat story.
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StreamingT2V
FramePack
SVI-Shot (ours)
Prompt Stream
The motorcyclist
riding across vast, flat desert landscape under clear blue sky.
The rider will soon encounter group of sand dunes and decide navigate through them.
The rider will skillfully maneuver the bike up the steep slopes of the dunes, leaving tracks in the sand behind.
After conquering the dunes, the motorcydlist will reach a natural water hole filled with crystal-clear turquoise water.
As they travel, the motorcyclist will spot herd of wild camels grazing nearby and take photo for memory.
Further along the desert, the motorcyclist come across large, ancient rock formation that resembles an elephant.
Figure 13: Qualitative results about the motorcycle story.
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StreamingT2V
FramePack
SVI-Shot (ours)
Prompt Stream
A group of visitors stands behind wooden fence at 20o, observing several rhinoceroses their enclosure.
The rhinos are grazing on the lush green grass front of them.
A young girl wearing green skirt turns to look at the adults.
The woman with long blonde hair leans forward to get better view.
The rhinos' large ears and prominent horns clearly visible.
One of the rhinos blinks momentarily the image.
slowly, blurring
Figure 14: Qualitative results about the zoo story.
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StreamingT2V
FramePack
SVI-Shot (ours)
Prompt Stream
newborn baby peacefully sleeping on its back, wearing white onesie and lying on soft, striped fabric.
The baby will wake and stretch its arms, feeling refreshed from its nap.
Mom will gently pick up the baby to feed bottle of milk.
The baby will be given a warm bath by the parents, with bubbles floating around it.
After the bath, the baby will be dressed in clean, fresh clothes and placed in crib for siesta.
Figure 15: Qualitative results about the baby story.
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Skeleton
SVi-Dance (ours)
UniAnimale- DIT
Wan 2.1
CPNAA
Figure 16: Qualitative results about the dancing.
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SVI-Talk (ours)
Mulitalk
Wan 2.1
TCSU
Figure 17: Qualitative results about the talking face.
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Figure 18: Qualitative results about the clips of Tom and Jerry.
27


Reviews:
[{"review_id": "IdWjwMAB52", "reviewer": "Reviewer_WpZa", "summary": "This paper introduces Error-Recycling Fine-Tuning (ERFT), a method designed to reduce the train–test hypothesis gap in autoregressive video diffusion models. During training, clean historical frames are typically assumed, whereas test-time generation depends on error-accumulating predictions. ERFT re-injects past model errors back into the conditioning frames, allowing the model to learn corrective behaviors. Experiments across diverse conditions and video models demonstrate strong gains in temporal stability and long-horizon video coherence.", "questions": "- Appendix B.3 discusses identity limitations during scene transitions. Do you intend to develop explicit mechanisms to maintain subject consistency across transitions, and will this direction be included in the current work or future extensions?\n- How stable is ERFT for extremely long generations (e.g., 10–30 minutes)?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "- The paper explains the train–test mismatch and derives ERFT step-by-step in a smooth narrative. Table 4 strongly supports the reasoning behind the method.\n- Figure 2 and Figure 4 present the mathematical formulation as intuitive diagrams, making the approach easy to understand even without flow matching background.\n- The method improves consistency in diverse scenarios (text-to-video, audio-conditioned video, skeleton-conditioned video) and demonstrates broad applicability.\n- Figure 5 and the qualitative samples show a large gap between baseline and finetuned models, especially as video length increases. The improvements in scene stability and reduction of drift are visually obvious and compelling.\n- The appendix provides thoughtful analysis of current boundaries (e.g., identity consistency, scene transitions) and potential directions, giving the work strong research depth.", "weaknesses": "- As acknowledged by the authors (appendix B.3), the model can exhibit identity drift or swapping when the main character exits and re-enters the scene.\n- The paper claims “infinite” or arbitrarily long sequences, but experiments demonstrate up to ~250 seconds. It remains uncertain how the model performs over 10+ minutes, where identity consistency, looping, or mode collapse may appear.", "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": "S57ngcOuNa", "reviewer": "Reviewer_ci1H", "summary": "This paper proposes Stable Video Infinity, a novel framework for infinite-length video generation using a Diffusion Transformer (DiT). The core contribution is an Error-Recycling Fine-Tuning (ERFT) strategy that addresses the train–test hypothesis gap in long video generation. Unlike previous anti-drifting or noise-rescheduling approaches that merely alleviate accumulated prediction errors, SVI explicitly recycles self-generated errors as supervisory prompts. The method injects historical errors into clean inputs, approximates error trajectories via one-step bidirectional integration, stores them in replay memory, and selectively resamples them for future timesteps. This closed-loop tuning enables DiTs to actively correct their own mistakes, scaling short-video models to arbitrarily long sequences. Extensive experiments across consistent, creative, and conditional benchmarks demonstrate superior temporal stability, scene diversity, and multimodal controllability over prior methods such as FramePack, StreamingT2V, and HistoryGuidance.", "questions": "For creative generation, how does SVI balance between temporal coherence and semantic diversity—are there cases where error correction overly smooths scene transitions?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The research problem of error accumulation in long video generation is crucial, and the proposed pipeline effectively addresses it.  \n2. The experiments cover various scenarios (long videos, creative storytelling, multimodal control), providing both quantitative and qualitative results with well-defined baselines.  \n3. The manuscript is well-organized, with clear figures (especially Fig. 1–4) illustrating the motivation and the training-test gap.", "weaknesses": "1. The models are only fine-tuned on small datasets (6k short clips), raising concerns about robustness and scalability to large-scale or diverse domains.\n2. The full pipeline (error banking, resampling, bidirectional integration) may appear complex relative to the simplicity of the LoRA tuning objective.\n3. Compared with Self-Forcing[1], SVI operates on the data distribution rather than the objective level. It injects historical errors to simulate AR noise, whereas Self-Forcing constrains self-generated rollouts explicitly. This makes SVI more lightweight but potentially less principled in capturing temporal feedback dynamics.\n4. As discussed in 3, while SVI effectively stabilizes autoregressive generation, its bidirectional error curation only approximates local temporal errors. It remains unclear whether the model genuinely learns long-range dependencies or simply achieves local consistency via error smoothing.\n\n[1] Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion", "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": "ehV6oLpcdF", "reviewer": "Reviewer_8N9r", "summary": "The paper addresses the fundamental challenge in generating very long videos: the discrepancy between error-free training with clean data and error-accumulating inference, which autoregressively operates on self-outputs with increasing error accumulation. They propose Error-Recycling Fine-tuning, which integrates the historical errors (degraded input) into training, to minimize the train-test gap. SVI deliberately injects historical errors into clean inputs and learns to predict an error-recycled velocity. THeir method achieves strong performance relative to baselines on their video generation benchmark.", "questions": "1. There are other papers with a similar idea of addressing this fundamental train-test prediction error gap in long video generation by training models with their own outputs/errors. These papers are relatively recent, but it might still be beneficial to discuss: how is the proposed method (conceptually) similar to and different from these other works? Here are some examples:\n- Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion\n- Rolling Forcing: Autoregressive Long Video Diffusion in Real Time\n- others?\n2. In Figure 5, why do some metrics show a decrease then increase?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The paper motivates well the core challenge for long video generation, i.e., that errors accumulate from self-outputs, and their proposed solution, i.e., put self-predicted errors into training.\n2. Their proposed method achieves strong performance relative to baselines on their video generation benchmark.\n3. The ablation study for each error term was helpful to empirically demonstrate that the image error term was the most important piece of their training strategy.", "weaknesses": "1. I think the paper would benefit from hedging its claims a little more. For example, they say \"we propose Stable Video Infinity (SVI) that can generate infinite-length videos with temporally coherent and visually plausible context following a long storyline.\" This seems like a stretch considering that the evaluations are on the order of a few minutes, and it is challenging to evaluate the storyline.\n2. The paper would benefit from comparing how long it takes for training and inference for their method relative to baseline methods. Does the proposed method take longer to train? Does this method take longer for inference?\n3. The paper develops their own benchmark (which is well-motivated), but it would also be helpful to compare their proposed method against baselines on an existing benchmark (or a subset of one, if computationally expensive).", "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": "KfrwRd7oFM", "reviewer": "Reviewer_rurm", "summary": "This paper presents Stable Video Infinity, which can generate infinite long video without quality degradation. It regards the long-video generation problem as a train-test hypothesis gap issue rather than a mere accumulation of generation error. During training, models are fed clean ground-truth latents and reference frames; at inference, to generate long video, they have to autoregressively condition on their own previously-generated, error-containing outputs. This mismatch brings two coupled error modes: single-clip predictive error and cross-clip conditional error. Instead of engineering stronger noise-schedulers or anchor frames, the proposed method recycle the model’s own error back into the training to avoid quality drop during generation. I generally like this work and only have minor questions.", "questions": "Please see the questions in weaknesses.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The idea of considering the training and inference gap is interesting. Unlike previous methods that generally considering heuristic drift mitigation, SVI aims to loop the generated error into training to reduce the gap during testing.\n\n2. A Comprehensive analysis on the accumulated error in video generation is provided.\n\n3. Comprehensive experiments with various setting are conducted. The proposed method achieve higher results compared to other competitors.", "weaknesses": "1. While injecting the error is interesting, in implementation it is not the online error injecting, which means the injected error is exactly the generated one, but instead an error sampled from the bank. This may cause some theoretical issues.\n\n2. The memory bank is capped at 500. However, the bank is filled by federated gathering across GPUs; if each GPU sees only a very few of videos, the effective diversity may be far smaller than 500, leading to the homogenization of errors.", "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 (Oral)