Reproduce “VINCIE: Unlocking In-context Image Editing from Video” on OpenResearch
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About this paper
Leigang Qu, Feng Cheng, Ziyan Yang, Qi Zhao, Shanchuan Lin, Yichun Shi, Yicong Li, Wenjie Wang and 2 more
VINCIE introduces a framework that enables in-context image editing by learning directly from natural video data, without relying on traditional paired image editing datasets. This method achieves competitive performance on the MagicBrush benchmark and a 48.7% success rate at Turn-5 on the MSE-Bench, showcasing its ability to mitigate artifact accumulation and enable emergent applications like story generation.
Key insights
- Natural videos inherently contain rich visual dynamics and temporal transitions that serve as implicit multi-turn image editing instructions, providing a scalable data source.
- Incorporating preceding contexts (images, text, masks) in multi-turn editing effectively mitigates artifact accumulation and improves visual consistency.
- Jointly learning segmentation prediction tasks (for current and next RoE masks) enhances grounding, improves controllable generation, and helps maintain consistency in unedited regions.
Summary by alphaXiv.
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