Edit2Interp：从空间编辑到视频帧插值的图像基础模型适应
Edit2Interp: Adapting Image Foundation Models from Spatial Editing to Video Frame Interpolation with Few-Shot Learning
AI总结：
本文通过少量样本学习，将图像编辑模型适应于视频帧插值，揭示了静态场景中物体变换的理解可激活潜在的时间推理能力。
摘要：
预训练的图像编辑模型通过数十亿张图像-文本对展现出强大的空间推理和物体感知转换能力，但缺乏显式的时间建模。本文证明，通过最小的适应即可利用这些空间先验解锁时间合成能力，无需引入视频特定架构或运动估计模块。我们展示了一个原本仅用于静态指令编辑的大规模图像编辑模型（Qwen-Image-Edit）通过仅使用64-256个训练样本进行低秩适应（LoRA）即可适应视频帧插值（VFI）。我们的核心贡献是揭示模型对静态场景中“物体如何变换”的理解中蕴含潜在的时间推理能力，可通过少量样本微调激活。尽管基线模型完全无法生成连贯的中间帧，我们的参数高效适应成功解锁了插值能力。与竞争的从头训练大规模数据集的任务特定VFI方法相比，我们的工作表明基础图像编辑模型在时间任务中具有未被开发的潜力，为资源受限场景下的视频合成提供了一条数据高效的路径。这填补了图像操控与视频理解之间的鸿沟，表明在基础模型中空间和时间推理可能比之前认为的更加紧密相连。
英文摘要：
Pre-trained image editing models exhibit strong spatial reasoning and object-aware transformation capabilities acquired from billions of image-text pairs, yet they possess no explicit temporal modeling. This paper demonstrates that these spatial priors can be repurposed to unlock temporal synthesis capabilities through minimal adaptation - without introducing any video-specific architecture or motion estimation modules. We show that a large image editing model (Qwen-Image-Edit), originally designed solely for static instruction-based edits, can be adapted for Video Frame Interpolation (VFI) using only 64-256 training samples via Low-Rank Adaptation (LoRA). Our core contribution is revealing that the model's inherent understanding of "how objects transform" in static scenes contains latent temporal reasoning that can be activated through few-shot fine-tuning. While the baseline model completely fails at producing coherent intermediate frames, our parameter-efficient adaptation successfully unlocks its interpolation capability. Rather than competing with task-specific VFI methods trained from scratch on massive datasets, our work establishes that foundation image editing models possess untapped potential for temporal tasks, offering a data-efficient pathway for video synthesis in resource-constrained scenarios. This bridges the gap between image manipulation and video understanding, suggesting that spatial and temporal reasoning may be more intertwined in foundation models than previously recognized
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
