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RealCam-Vid: High-resolution Video Dataset with Dynamic Scenes and Metric-scale Camera Movements
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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.
Forward citations
Cited by 2 Pith papers
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Epipolar Geometry Improves Video Generation Models
Ranking generated videos by their epipolar (Sampson) error and fine-tuning Wan2.1 with Flow-DPO cuts epipolar error 31% and raises human-rated 3D consistency from 54% to 72%.
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RealCam-I2V: Real-World Image-to-Video Generation with Interactive Complex Camera Control
Metric-scale depth alignment plus scene-constrained noise shaping improves camera controllability and video quality for image-to-video generation on RealEstate10K.
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