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arxiv: 2605.18233 · v1 · pith:WAST5FVTnew · submitted 2026-05-18 · 💻 cs.CV

Enhancing Train-Free Infinite-Frame Generation for Consistent Long Videos

X. Feng , J. Zhu , M. Wu , C. Chen , F. Mao , H. Guo , J. Wu , X. Chu
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K. Huang
This is my paper

Pith reviewed 2026-05-20 10:52 UTC · model grok-4.3

classification 💻 cs.CV
keywords long video generationtrain-freeautoregressive generationtemporal consistencynoise alignmentfoundation modelsinfinite frame generation
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The pith

MIGA uses two-stage noise alignment and dual frame consistency to let foundation video models generate arbitrarily long coherent videos without retraining.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tries to establish that existing short-clip video generators can be turned into producers of infinite-length videos by fixing how noise is presented during frame-by-frame inference. It introduces a two-stage alignment step that shrinks the range of noise levels the model must handle, followed by a dual consistency step that reflects on early noisy frames to correct them and draws guidance from later cleaner frames to steer the sequence. A sympathetic reader would care because this approach keeps memory use constant while improving how well frames connect over long durations, removing the need to retrain large models for extended content. If the claim holds, it would mean current foundation models become practical tools for tasks that require sustained visual storytelling or simulation.

Core claim

MIGA mitigates the training-inference gap by reducing excessive noise span through a two-stage alignment mechanism. It then applies a dual consistency enhancement in which self-reflection corrects early high-noise frames and long-range guidance from later low-noise frames steers the generation process, jointly raising temporal consistency and yielding state-of-the-art results on VBench and NarrLV.

What carries the argument

The two-stage alignment mechanism that reduces the noise span presented to the model, combined with the dual consistency enhancement of self-reflection on early frames and long-range guidance from later frames.

If this is right

  • Foundation video models can generate videos of unlimited length while using constant memory.
  • Temporal consistency holds across extended sequences without additional training.
  • The approach delivers higher scores than prior train-free methods on standard long-video benchmarks.
  • Pretrained models become directly usable for applications needing sustained frame-to-frame coherence.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Noise-range alignment strategies could transfer to other autoregressive tasks such as long audio or text generation where training contexts are short.
  • The combination of local self-correction and global guidance might generalize to hybrid control methods in other generative domains.
  • Applying the same alignment steps to newer base models would test how dependent the gains are on the underlying architecture.

Load-bearing premise

The assumption that the two-stage alignment and dual consistency mechanisms will reliably bridge the train-inference mismatch without introducing new inconsistencies or artifacts.

What would settle it

Running MIGA and baseline methods such as FIFO-diffusion on the same long-sequence prompts and checking whether MIGA's temporal consistency scores on VBench stay measurably higher across repeated trials would confirm or refute the claimed improvement.

Figures

Figures reproduced from arXiv: 2605.18233 by C. Chen, F. Mao, H. Guo, J. Wu, J. Zhu, K. Huang, M. Wu, X. Chu, X. Feng.

Figure 1
Figure 1. Figure 1: MIGA enables temporally consistent, infinite-frame (∞) video generation in a training-free manner. We present three long videos (1000+ frames) generated by MIGA, while the foundation model used by MIGA, Wan2.1-1.3B (Wan et al., 2025), supports only 81 frames by default. exists between training and inference in long video gener￾ation (Kim et al., 2024). In particular, during training, the model is exposed t… view at source ↗
Figure 2
Figure 2. Figure 2: Inference framework comparison between FIFO-Diffusion and our Two-Stage Training-Inference Alignment (TTA) mechanism. (a) FIFO-Diffusion achieves frame-level autoregressive generation by maintaining a queue of latents with progressively increasing noise levels, resulting in an excessive noise span among the local latents fed to the model. (b) Our TTA effectively reduces the noise span: Stage 1 performs zig… view at source ↗
Figure 3
Figure 3. Figure 3: Modeling insight behind our self-reflection mechanism. (a) A video case containing the consistency anomaly. (b-d) Similarity computation between clean and noisy latents, along with the corresponding correlation coefficient analysis results. usage does not grow with longer videos. After (e − 1) itera￾tive denoising steps, we obtain nLzig fully denoised frames (i.e., N = nLzig frames in the generated video).… view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of ablation study results. (a-d) Starting from the baseline, our Stage 1, Stage 2, and DCE mechanism are sequentially added. Yellow bboxes in the first frame indicate regions with prominent noise. Red bboxes denote regions in the current frame where the subject exhibits noticeable inconsistency compared to previous frames. Better viewed in color with zoom-in. thereby avoiding the need for addi… view at source ↗
Figure 5
Figure 5. Figure 5: Ablation study on the adjustment threshold δadju. (a-b) Effects of δadju on O.S., Rcorr, and Rsucc. Overall score Stage 2 steps 0 5 10 15 20 25 30 64 ….. ….. Without Stage 1 !!! [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Ablation study on the steps in stage 2. mechanisms. Individually, TTA and DCE improve the over￾all score by 2.03% and 1.73%, respectively, demonstrating their effectiveness. Combined, they provide complementary gains and further enhance performance. Study on TTA. Our TTA mechanism comprises two stages: stage 1 employs zigzag iterative denoising, and stage 2 applies denoising at a unified noise level. As sh… view at source ↗
read the original abstract

Without incurring significant computational overhead, train-free long video generation aims to enable foundation video generation models to produce longer videos. Frame-level autoregressive frameworks, e.g., FIFO-diffusion, offer the advantage of generating infinitely long videos with constant memory consumption. However, the mismatch between training and inference, coupled with the challenge of maintaining long-term consistency, limits the effective utilization of foundation models. To mitigate these concerns, we propose \textbf{MIGA}, a novel infinite-frame long video generation method. Firstly, we propose an effective two-stage alignment mechanism that mitigates the training-inference gap by reducing the excessive noise span fed to the model. We then introduce an innovative dual consistency enhancement mechanism, where the self-reflection approach corrects early high-noise frames and the long-range frame guidance approach leverages later low-noise frames with broad coverage to steer generation, jointly improving temporal consistency. Extensive experiments on VBench and NarrLV demonstrate the state-of-the-art performance of MIGA. Our project page is available at https://xiaokunfeng.github.io/miga_homepage/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents MIGA, a novel method for train-free infinite-frame long video generation. It addresses the training-inference mismatch with a two-stage alignment mechanism that reduces excessive noise span, and improves temporal consistency through a dual consistency enhancement mechanism involving self-reflection on early high-noise frames and long-range guidance from later low-noise frames. Experiments on VBench and NarrLV show state-of-the-art performance.

Significance. This work has the potential to enable more consistent and longer video generations using existing foundation models without additional training, which is significant for applications requiring extended video content. The constant memory consumption for infinite frames is a key strength if the consistency mechanisms can be implemented without compromising the autoregressive nature.

major comments (2)
  1. [Dual Consistency Enhancement Mechanism] The long-range frame guidance uses later low-noise frames with broad coverage to steer generation. However, in an autoregressive generation process, later frames are not available when generating earlier ones. The paper needs to clarify how this is achieved without lookahead, buffering that increases memory, or a second pass, as this is critical to maintaining the constant-memory infinite-frame claim.
  2. [Experimental Results] While SOTA performance is claimed on VBench and NarrLV, the manuscript text does not provide specific quantitative results, baseline comparisons, or ablation studies. Including these details is necessary to substantiate the central claims about improved consistency and performance.
minor comments (1)
  1. [Abstract] Consider adding a sentence on the specific improvements observed to give readers a quick sense of the gains.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential of MIGA to enable consistent infinite-frame video generation without training. We address the major comments point by point below and will update the manuscript to improve clarity and substantiation of results.

read point-by-point responses

  1. Referee: [Dual Consistency Enhancement Mechanism] The long-range frame guidance uses later low-noise frames with broad coverage to steer generation. However, in an autoregressive generation process, later frames are not available when generating earlier ones. The paper needs to clarify how this is achieved without lookahead, buffering that increases memory, or a second pass, as this is critical to maintaining the constant-memory infinite-frame claim.

    Authors: We thank the referee for highlighting this critical point. The long-range frame guidance operates strictly within the autoregressive pipeline by drawing on a fixed-size buffer of the most recently generated frames (which have received more denoising iterations and thus exhibit lower noise levels). These serve as reference frames with broad temporal coverage for the current frame being denoised. No future frames are accessed, no second pass is performed, and memory remains constant because the buffer size is fixed and independent of video length. We will revise the method description and add a detailed figure with pseudocode to explicitly demonstrate this implementation and reaffirm the constant-memory property. revision: yes

  2. Referee: [Experimental Results] While SOTA performance is claimed on VBench and NarrLV, the manuscript text does not provide specific quantitative results, baseline comparisons, or ablation studies. Including these details is necessary to substantiate the central claims about improved consistency and performance.

    Authors: We appreciate the referee's emphasis on clear evidence. The current manuscript contains quantitative tables in the experiments section comparing MIGA against baselines such as FIFO-Diffusion on VBench and NarrLV, along with ablations isolating the two-stage alignment and dual consistency components. To address the concern that these may not be sufficiently prominent, we will expand the main text with additional metric breakdowns, more explicit baseline numbers, and further ablation results in the revised version. revision: partial

Circularity Check

0 steps flagged

No circularity: method proposes independent mechanisms on foundation models

full rationale

The paper describes a two-stage alignment to reduce noise span and a dual consistency mechanism (self-reflection on early frames plus long-range guidance from later frames) as new algorithmic contributions. No equations, fitted parameters, or derivations are presented that reduce by construction to the inputs. No self-citation load-bearing steps, uniqueness theorems, or ansatz smuggling appear in the abstract or method outline. The approach is built atop existing video foundation models rather than re-deriving results from its own fitted values or prior self-referential claims. This is the common case of an honest engineering proposal with no detectable circularity in its derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on domain assumptions about diffusion model noise schedules and frame consistency in autoregressive generation, with no free parameters or invented entities explicitly introduced in the abstract.

axioms (1)
  • domain assumption Foundation video diffusion models can be adapted for autoregressive infinite-frame generation by adjusting noise input spans and adding consistency corrections.
    Invoked as the basis for the two-stage alignment and dual consistency mechanisms.

pith-pipeline@v0.9.0 · 5733 in / 1140 out tokens · 26776 ms · 2026-05-20T10:52:44.799618+00:00 · methodology

discussion (0)

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    As shown in Tab

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