Video4Edit: Viewing Image Editing as a Degenerate Temporal Process

• URL: http://arxiv.org/abs/2511.18131v1
• Date: Sat, 22 Nov 2025 17:30:55 GMT
• Title: Video4Edit: Viewing Image Editing as a Degenerate Temporal Process
• Authors: Xiaofan Li, Yanpeng Sun, Chenming Wu, Fan Duan, YuAn Wang, Weihao Bo, Yumeng Zhang, Dingkang Liang,
• Abstract summary: multimodal foundation models have propelled instruction-driven image generation and editing into a genuinely cross-modal, cooperative regime.<n>We revisit this challenge through the lens of temporal modeling.<n>This perspective allows us to transfer single-frame evolution priors from video pre-training, enabling a highly data-efficient fine-tuning regime.
• Score: 24.8621496006791
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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