Did You Steal My Shot? Pioneering Camera Motion Plagiarism Detection in Generative Videos

Researchers built the first benchmark for camera motion analysis, comprising a motion dataset with 11 motion styles and evaluation protocols, and proposed a motion representation that augments optical flow with vorticity cues from fluid dynamics to detect camera-motion plagiarism in generative videos. Their detector achieves a 3.02x improvement in plagiarism detection over the strongest baseline and remains effective on generative videos, according to the arXiv paper 2609.22267v1. The work aims to extend copyright protection beyond static content to dynamic camera motion, which the authors note generative video models can imitate from simple prompts.

arXiv:2609.22267v1 Announce Type: new Abstract: Camera motion often reflects directorial intent and requires professional equipment, making it a high value form of intellectual property. However, generative video models can imitate such high value camera motions with simple prompts, while existing similarity detection methods mainly operate on visual content and fail to capture deeper motion similarity. This is mainly because their training data entangles camera motion with visual content. Moreover, traditional optical flow is insufficient to represent complex camera motions. We therefore build the first benchmark for camera motion analysis, including a motion dataset with \textbf{11} motion styles and evaluation protocols. Furthermore, we propose a motion representation that augments optical flow with vorticity cues from fluid dynamics, thereby better capturing motions. Experiments show that our detector achieves a \textbf{3.02 } improvement in plagiarism detection over the strongest baseline and remains effective on generative videos. We believe our work extends copyright protection beyond static content to dynamic camera motion.