CoFrame: Learning Real-World Video Retaking via Synchronized Reframing

Video retaking aims to synthesize a new video that follows an input camera trajectory while preserving the appearance and motion depicted in the input video. Enabled by advances in conditional video generation models, together with synthetic multi-camera supervision, recent works have shown strong retaking performance, especially in terms of camera accuracy. However, faithfully preserving subject appearance and dynamics in real-world videos remains challenging, particularly for rapid movements and large displacements. A key challenge is still the lack of real synchronized multi-view recordings for training. In this work, we address this bottleneck from the views of complementary supervision. Specifically, we introduce CoFrame, a framework that constructs synchronized reframings from real monocular videos to complement synthetic multi-camera supervision for conditional re-rendering. Our key insight is that synthetic multi-camera pairs supervise extrinsic viewpoint changes, while synchronized real-video reframings provide complementary supervision for preserving real appearance and dynamics, with camera intrinsics as the bridge for expressing crop-and-resize transformations as explicit camera conditions. We further explore relative camera conditioning and paired flow matching to encode source-target camera relationships and jointly supervise synchronized target predictions. Experiments demonstrate improved motion preservation and camera accuracy over ReCamMaster, ReDirector, and Geo-Align, together with improved data efficiency under limited synthetic multi-camera supervision.