Annotating camera poses on dynamic Internet videos at scale is critical for advancing fields like realistic video generation and simulation.However, collecting such a dataset is difficult, as most Internet videos are unsuitable for pose estimation.Furthermore, annotating dynamic Internet videos present significant challenges even for state-of-the-art methods.In this paper, we introduce DynPose-100K, a large-scale dataset of dynamic Internet videos annotated with camera poses.Our collection pipeline addresses filtering using a carefully combined set of task-specific and generalist models.For pose estimation, we combine the latest techniques of point tracking, dynamic masking, and structure-from-motion to achieve improvements over the state-of-the-art approaches.Our analysis and experiments demonstrate that DynPose-100K is both large-scale and diverse across several key attributes, opening up avenues for advancements in various downstream applications.