iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image Generation
Pre-trained video models learn powerful priors for generating high-quality, temporally coherent content. While these models excel at temporal coherence, their dynamics are often constrained by the continuous nature of their training data. We hypothesize that by injecting the rich and unconstrained content diversity from image data into this coherent temporal framework, we can generate image sets that feature both natural transitions and a far more expansive dynamic range. To this end, we introduce iMontage, a unified framework designed to repurpose a powerful video model into an all-in-one image generator. The framework consumes and produces variable-length image sets, unifying a wide array of image generation and editing tasks. To achieve this, we propose an elegant and minimally invasive adaptation strategy, complemented by a tailored data curation process and training paradigm. This approach allows the model to acquire broad image manipulation capabilities without corrupting its invaluable original motion priors. iMontage excels across several mainstream many-in-many-out tasks, not only maintaining strong cross-image contextual consistency but also generating scenes with extraordinary dynamics that surpass conventional scopes. Find our homepage at: https://kr1sjfu.github.io/iMontage-web/.
69266dec243b2216fb75ca56
Zhoujie Fu
69266dec243b2216fb75ca57
Xianfang Zeng
69266dec243b2216fb75ca58
Jinghong Lan
69266dec243b2216fb75ca59
Xinyao Liao
69266dec243b2216fb75ca5a
Cheng Chen
69266dec243b2216fb75ca5b
Junyi Chen
69266dec243b2216fb75ca5c
Jiacheng Wei
69266dec243b2216fb75ca5d
Wei Cheng
claimed_verified
2025-11-26T09:27:36.745Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64b914c8ace99c0723ad83a9/B4gxNByeVY_xaOcjwiN1j.jpeg
Wei Cheng
wchengad
user
wchengad
69266dec243b2216fb75ca5e
Shiyu Liu
69266dec243b2216fb75ca5f
Yunuo Chen
69266dec243b2216fb75ca60
Gang Yu
69266dec243b2216fb75ca61
Guosheng Lin
iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image Generation
Pre-trained video models learn powerful priors for generating high-quality, temporally coherent content. While these models excel at temporal coherence, their dynamics are often constrained by the continuous nature of their training data. We hypothesize that by injecting the rich and unconstrained content diversity from image data into this coherent temporal framework, we can generate image sets that feature both natural transitions and a far more expansive dynamic range. To this end, we introduce iMontage, a unified framework designed to repurpose a powerful video model into an all-in-one image generator. The framework consumes and produces variable-length image sets, unifying a wide array of image generation and editing tasks. To achieve this, we propose an elegant and minimally invasive adaptation strategy, complemented by a tailored data curation process and training paradigm. This approach allows the model to acquire broad image manipulation capabilities without corrupting its invaluable original motion priors. iMontage excels across several mainstream many-in-many-out tasks, not only maintaining strong cross-image contextual consistency but also generating scenes with extraordinary dynamics that surpass conventional scopes. Find our homepage at: https://kr1sjfu.github.io/iMontage-web/.
iMontage repurposes pre-trained video models to generate high-quality, diverse image sets with natural transitions and enhanced dynamics through a unified framework and tailored adaptation strategy.
pre-trained video models temporal coherence continuous nature image data content diversity iMontage unified framework image generator variable-length image sets image generation image editing adaptation strategy data curation training paradigm image manipulation motion priors cross-image contextual consistency