PanToMime: Imitating Camera Change by Image Analogy

Image analogy provides a natural way to edit images by example: instead of describing a transformation, the user simply demonstrates it. However, when the exemplar also contains a change in camera viewpoint, the desired transformation becomes ambiguous. A user may want to transfer the demonstrated camera movement, match the final exemplar view, or preserve the query view while applying only the visual edit. We introduce PanToMime, a camera-aware image analogy method that understands and controls camera changes while transferring the demonstrated visual transformation. To support this setting, we introduce **PanToMime-Bench**, containing paired visual edits under controlled camera changes, together with **Render2Real**, a geometry-grounded pipeline for generating realistic multi-view training data. Experiments show that existing image-analogy methods can reproduce visual edits but struggle with camera changes, particularly when the exemplar and query start from different viewpoints. PanToMime reduces camera orientation error from $68.20^{\circ }$ for the second-best method to $30.69^{\circ }$, while retaining competitive edit fidelity with an Edit score of $9.03$. Our results also suggest that image analogy can provide an effective interface for camera understanding and pose transfer.