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

• URL: http://arxiv.org/abs/2605.18233v1
• Date: Mon, 18 May 2026 11:28:45 GMT
• Title: Enhancing Train-Free Infinite-Frame Generation for Consistent Long Videos
• Abstract summary: Train-free long video generation aims to enable foundation video generation models to produce longer videos.<n> Frame-level autoregressive frameworks, e.g., FIFO-diffusion, offer the advantage of generating infinitely long videos with constant memory consumption.<n>We propose textbfMIGA, a novel infinite-frame long video generation method.
• Score: 0.05417521241272644
• License: http://creativecommons.org/licenses/by/4.0/
• 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/.

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