  A precise and user-friendly manipulation of image content while preserving
image fidelity has always been crucial to the field of image editing. Thanks to
the power of generative models, recent point-based image editing methods allow
users to interactively change the image content with high generalizability by
clicking several control points. But the above mentioned editing process is
usually based on the assumption that features stay constant in the motion
supervision step from initial to target points. In this work, we conduct a
comprehensive investigation in the feature space of diffusion models, and find
that features change acutely under in-plane rotation. Based on this, we propose
a novel approach named RotationDrag, which significantly improves point-based
image editing performance when users intend to in-plane rotate the image
content. Our method tracks handle points more precisely by utilizing the
feature map of the rotated images, thus ensuring precise optimization and high
image fidelity. Furthermore, we build a in-plane rotation focused benchmark
called RotateBench, the first benchmark to evaluate the performance of
point-based image editing method under in-plane rotation scenario on both real
images and generated images. A thorough user study demonstrates the superior
capability in accomplishing in-plane rotation that users intend to achieve,
comparing the DragDiffusion baseline and other existing diffusion-based
methods. See the project page https://github.com/Tony-Lowe/RotationDrag for
code and experiment results.
