Did You Steal My Shot? Pioneering Camera Motion Plagiarism Detection in Generative Videos
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2510.24904v2 Announce Type: replace Abstract: Although recent video generative models are getting more capable of following external camera controls, imposed by either text descriptions or came...
arXiv:2608.20770v1 Announce Type: new Abstract: Modern AI video generation models can produce videos with high visual fidelity and seemingly smooth temporal transitions. However, visual realism does...
arXiv:2601. 08828v2 Announce Type: replace-cross Abstract: Despite the rapid progress of video generation models, the role of data in influencing motion is poorly understood.
arXiv:2610.23856v1 Announce Type: new Abstract: Generated videos often lose geometric consistency under camera motion: structures warp under viewpoint change, parallax becomes inconsistent, and smoot...
arXiv:2609.38683v1 Announce Type: cross Abstract: Cinematic camera motion is a fundamental storytelling tool, defined not only by where the camera is positioned in the scene, but also by how it moves...
PoseRegen is a new framework for removing video watermarks while maintaining temporal consistency and motion fidelity. It first applies a shared viewpoint transformation to break watermark patterns, then reconstructs the sequence with a pose‑conditioned video model and Flow Matching supervision to preserve identity and background. The method also includes a Pose‑Conditioned Motion Consistency Adapter to keep body‑part trajectories stable, achieving near‑random watermark bit accuracy and outperforming baselines on motion metrics.