SceneScribe-1M: A Large-Scale Video Dataset with Comprehensive Geometric and Semantic Annotations
This provides a foundational resource for researchers in computer vision and video generation, though it is incremental as it builds on existing dataset efforts by unifying annotations.
The authors tackled the lack of a unified large-scale video dataset for both 3D geometric perception and video synthesis by introducing SceneScribe-1M, a dataset of one million videos with comprehensive annotations, and demonstrated its value through benchmarks across multiple tasks like depth estimation and text-to-video synthesis.
The convergence of 3D geometric perception and video synthesis has created an unprecedented demand for large-scale video data that is rich in both semantic and spatio-temporal information. While existing datasets have advanced either 3D understanding or video generation, a significant gap remains in providing a unified resource that supports both domains at scale. To bridge this chasm, we introduce SceneScribe-1M, a new large-scale, multi-modal video dataset. It comprises one million in-the-wild videos, each meticulously annotated with detailed textual descriptions, precise camera parameters, dense depth maps, and consistent 3D point tracks. We demonstrate the versatility and value of SceneScribe-1M by establishing benchmarks across a wide array of downstream tasks, including monocular depth estimation, scene reconstruction, and dynamic point tracking, as well as generative tasks such as text-to-video synthesis, with or without camera control. By open-sourcing SceneScribe-1M, we aim to provide a comprehensive benchmark and a catalyst for research, fostering the development of models that can both perceive the dynamic 3D world and generate controllable, realistic video content.