SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse
  Viewpoints
Authors: name: Jianhong Bai, name: Menghan Xia, name: Xintao Wang, name: Ziyang Yuan, name: Xiao Fu, name: Zuozhu Liu, name: Haoji Hu, name: Pengfei Wan, name: Di Zhang

Recent advancements in video diffusion models have shown exceptional
abilities in simulating real-world dynamics and maintaining 3D consistency.
This progress inspires us to investigate the potential of these models to
ensure dynamic consistency across various viewpoints, a highly desirable
feature for applications such as virtual filming. Unlike existing methods
focused on multi-view generation of single objects for 4D reconstruction, our
interest lies in generating open-world videos from arbitrary viewpoints,
incorporating 6 DoF camera poses. To achieve this, we propose a plug-and-play
module that enhances a pre-trained text-to-video model for multi-camera video
generation, ensuring consistent content across different viewpoints.
Specifically, we introduce a multi-view synchronization module to maintain
appearance and geometry consistency across these viewpoints. Given the scarcity
of high-quality training data, we design a hybrid training scheme that
leverages multi-camera images and monocular videos to supplement Unreal
Engine-rendered multi-camera videos. Furthermore, our method enables intriguing
extensions, such as re-rendering a video from novel viewpoints. We also release
a multi-view synchronized video dataset, named SynCamVideo-Dataset. Project
page: https://jianhongbai.github.io/SynCamMaster/.