VINCIE: Unlocking In-context Image Editing from Video

Leigang Qu, Feng Cheng, Ziyan Yang, Qi Zhao, Shanchuan Lin, Yichun Shi, Yicong Li, Wenjie Wang, Tat-Seng Chua, Lu Jiang

In-context image editing aims to modify images based on a contextual sequence comprising text and previously generated images. Existing methods typically depend on task-specific pipelines and expert models (e.g., segmentation and inpainting) to curate training data. In this work, we explore whether an in-context image editing model can be learned directly from videos. We introduce a scalable approach to annotate videos as interleaved multimodal sequences. To effectively learn from this data, we design a block-causal diffusion transformer trained on three proxy tasks: next-image prediction, current segmentation prediction, and next-segmentation prediction. Additionally, we propose a novel multi-turn image editing benchmark to advance research in this area. Extensive experiments demonstrate that our model exhibits strong in-context image editing capabilities and achieves state-of-the-art results on two multi-turn image editing benchmarks. Despite being trained exclusively on videos, our model also shows promising abilities in multi-concept composition, story generation, and chain-of-editing applications.

✍️ Citation

@article{qu2025vincie,
  title={VINCIE: Unlocking In-context Image Editing from Video},
  author={Qu, Leigang and Cheng, Feng and Yang, Ziyan and Zhao, Qi and Lin, Shanchuan and Shi, Yichun and Li, Yicong and Wang, Wenjie and Chua, Tat-Seng and Jiang, Lu},
  journal={arXiv preprint arXiv:2506.10941},
  year={2025}
}

📜 License

VINCIE is licensed under the Apache 2.0.