仅用一对图像即可学习并迁移复杂变形，实现精准编辑。
Edit Transfer: Learning Image Editing via Vision In-Context Relations
- 通过四图拼接+轻量微调，从单对图像学空间变换。
- 42个样本即超越现有方法，在非刚性形变上表现优异。
- 适合需要精确姿态/视角调整的图像编辑场景。
我们提出新任务Edit Transfer：模型仅凭一对源-目标图像，就能学习并迁移到新查询图像上的编辑变换。文本方法擅长语义操作但难以控制几何细节（如姿态、视角）；基于参考的方法多关注风格或外观，难以处理非刚性变换。本文受大语言模型上下文学习启发，构建视觉关系上下文学习范式，基于DiT架构的文生图模型，将编辑示例与查询图像组合成四图复合结构，采用轻量级LoRA微调捕捉复杂空间变换。仅使用42个训练样本，该方法在多种非刚性变换场景下显著优于当前最优的TIE和RIE方法，证明了少样本视觉关系学习的有效性。
原文摘要 · Abstract (English)
We introduce a new setting, Edit Transfer, where a model learns a transformation from just a single source-target example and applies it to a new query image. While text-based methods excel at semantic manipulations through textual prompts, they often struggle with precise geometric details (e.g., poses and viewpoint changes). Reference-based editing, on the other hand, typically focuses on style or appearance and fails at non-rigid transformations. By explicitly learning the editing transformation from a source-target pair, Edit Transfer mitigates the limitations of both text-only and appearance-centric references. Drawing inspiration from in-context learning in large language models, we propose a visual relation in-context learning paradigm, building upon a DiT-based text-to-image model. We arrange the edited example and the query image into a unified four-panel composite, then apply lightweight LoRA fine-tuning to capture complex spatial transformations from minimal examples. Despite using only 42 training samples, Edit Transfer substantially outperforms state-of-the-art TIE and RIE methods on diverse non-rigid scenarios, demonstrating the effectiveness of few-shot visual relation learning.
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