Edit2Interp: Adapting Image Foundation Models from Spatial Editing to Video Frame Interpolation with Few-Shot Learning

Nasrin Rahimi; Mısra Yavuz; Burak Can Biner; Yunus Bilge Kurt; Ahmet Rasim Emirdağı; Süleyman Aslan; Görkay Aydemir; M. Akın Yılmaz; A. Murat Tekalp

arXiv · 2603.15003

Edit2Interp: Adapting Image Foundation Models from Spatial Editing to Video Frame Interpolation with Few-Shot Learning

Nasrin Rahimi·Mısra Yavuz·Burak Can Biner·Yunus Bilge Kurt·Ahmet Rasim Emirdağı·Süleyman Aslan·Görkay Aydemir·M. Akın Yılmaz·A. Murat Tekalp

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Abstract

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

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Nasrin Rahimi, Mısra Yavuz, Burak Can Biner, Yunus Bilge Kurt, Ahmet Rasim Emirdağı, Süleyman Aslan, Görkay Aydemir, M. Akın Yılmaz, A. Murat Tekalp. 2026-03-16. Edit2Interp: Adapting Image Foundation Models from Spatial Editing to Video Frame Interpolation with Few-Shot Learning. https://arxiv.org/abs/2603.15003

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