首个高分辨率动态场景带度量尺度相机轨迹的开源数据集。
RealCam-Vid: High-resolution Video Dataset with Dynamic Scenes and Metric-scale Camera Movements
- 构建动态场景下具度量尺度的相机运动数据
- 支持复杂环境中的真实物体运动与精确轨迹生成
- 适合视频生成、三维重建与机器人视觉研究者
当前可控制相机的视频生成技术受限于静态场景数据集（如RealEstate10K），这些数据集仅提供相对尺度的相机标注，无法捕捉动态场景交互，且缺乏度量尺度几何一致性——这对复杂环境中真实物体运动和精确相机轨迹合成至关重要。为填补这一空白，我们推出了首个完全开源的高分辨率动态场景数据集，包含度量尺度的相机运动标注，详见https://github.com/ZGCTroy/RealCam-Vid。
原文摘要 · Abstract (English)
Recent advances in camera-controllable video generation have been constrained by the reliance on static-scene datasets with relative-scale camera annotations, such as RealEstate10K. While these datasets enable basic viewpoint control, they fail to capture dynamic scene interactions and lack metric-scale geometric consistency-critical for synthesizing realistic object motions and precise camera trajectories in complex environments. To bridge this gap, we introduce the first fully open-source, high-resolution dynamic-scene dataset with metric-scale camera annotations in https://github.com/ZGCTroy/RealCam-Vid.
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