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arXiv AI By Siyi Xie, Xuanke Shi, Jinsheng Quan, Haoran Tang, Zukai Chen, Lei Yang, Quan Wang

TransPhy: Visual In-Context Learning for Physically Grounded Image Editing

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TransPhy is a framework for visually in-context learning that focuses on physically grounded image editing. It introduces PhysVICL-74, a dataset of 74 transformation rules and 5,240 source–target pairs, and evaluates models on novel-instance transfer and unseen-rule generalization. The method predicts the demonstrated rule and a query-specific target-state description, then synthesizes the target image using token-wise mixture-of-experts guided by localized transition cues, improving rule adherence, query consistency, and generalization over existing methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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