We observe that recent advances in multimodal foundation models have propelled instruction-driven image generation and editing into a genuinely cross-modal, cooperative regime. Nevertheless, state-of-the-art editing pipelines remain costly: beyond training large diffusion/flow models, they require curating massive high-quality triplets of \{instruction, source image, edited image\} to cover diverse user intents. Moreover, the fidelity of visual replacements hinges on how precisely the instruction references the target semantics. We revisit this challenge through the lens of temporal modeling: if video can be regarded as a full temporal process, then image editing can be seen as a degenerate temporal process. This perspective allows us to transfer single-frame evolution priors from video pre-training, enabling a highly data-efficient fine-tuning regime. Empirically, our approach matches the performance of leading open-source baselines while using only about one percent of the supervision demanded by mainstream editing models.
@article{arxiv.2511.18131,
title = {Video4Edit: Viewing Image Editing as a Degenerate Temporal Process},
author = {Xiaofan Li and Yanpeng Sun and Chenming Wu and Fan Duan and YuAn Wang and Weihao Bo and Yumeng Zhang and Dingkang Liang},
journal= {arXiv preprint arXiv:2511.18131},
year = {2025}
}