硬件革新：异步捕捉，让相机 "错峰拍摄" 本文第一作者陈羽田，香港中文大学 MMLab 博士二年级在读，研究方向为三维重建与生成，导师为薛天帆教授。个人主页：https://yutian10.github.io 当古装剧中的长袍在武林高手凌空翻腾的瞬间扬起 0.01 秒的惊艳弧度，当 VR 玩家想伸手抓住对手 "空中定格" 的剑锋，当 TikTok 爆款视频里一滴牛奶皇冠般的溅 落要被 360° 无死角重放 —— 如何用普通的摄像机，把瞬间即逝的高速世界 "冻结" 成可供反复拆解、传送与交互的数字化 4D 时空，成为 3D 视觉领域的一个难 题。 然而，受限于硬件成本与数据传输带宽，目前绝大多数 4D 采集阵列的最高帧率仅约 30 FPS；相比之下，传统高速摄影通常需要 120 FPS 乃至更高。简单升级相机 硬件不仅价格高昂，还会带来指数级增长的数据通量，难以在大规模部署中落地。另一条改变的思路是在重建阶段 "补帧"。近期，例如 4D 高斯溅射（4D Gaussian Splatting）等动态场景重建方法能在简单运动中通过稀疏时序输入合成连续帧，变相提升帧率，但面对布料摆动、高速旋转等非线性复杂运动，中间 ...

Core Viewpoint - The article introduces ReCamMaster, a video generation model that allows users to reframe existing videos along new camera trajectories, addressing common issues faced by video creators such as equipment limitations and shaky footage [2][17]. Group 1: ReCamMaster Overview - ReCamMaster enables users to upload any video and specify a new camera path for re-framing, thus enhancing the quality of video production [2]. - The model has significant applications in fields such as 4D reconstruction, video stabilization, autonomous driving, and embodied intelligence [3][17]. Group 2: Innovation and Methodology - The primary innovation of ReCamMaster lies in its new video conditioning paradigm, which combines condition video and target video in a time dimension after patchifying, resulting in substantial performance improvements over previous methods [11][17]. - The model achieves near-product-level performance in re-framing single videos, demonstrating the potential of video generation models in this area [13][17]. Group 3: MultiCamVideo Dataset - The MultiCamVideo dataset, created using Unreal Engine 5, consists of 13,600 dynamic scenes captured by 10 cameras along different trajectories, totaling 136,000 videos and 112,000 unique camera paths [13]. - The dataset features 66 different characters, 93 types of actions, and 37 high-quality 3D environments, providing a rich resource for research in camera-controlled video generation and 4D reconstruction [13][17]. Group 4: Experimental Results - ReCamMaster has shown significant performance improvements compared to baseline methods in experimental comparisons [15][17].