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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 

Emerging Zoned Namespace (ZNS) SSDs, providing the coarse-grained zone abstraction, hold the potential to significantly enhance the cost efficiency of future storage infrastructure and mitigate performance unpredictability. However, existing ZNS SSDs have a static zoned interface, making them in-adaptable to workload runtime behavior, unscalable to underlying hardware capabilities, and interfering with co-located zones. Applications either under-provision the zone resources yielding unsatisfied throughput, create over-provisioned zones and incur costs, or experience unexpected I/O latencies. We propose eZNS, an elastic-ZNS interface that exposes an adaptive zone with predictable characteristics. eZNS comprises two major components: a zone arbiter that manages zone allocation and active resources on the control plane, and a hierarchical I/O scheduler with read congestion control and write admission control on the data plane. Together, eZNS enables the transparent use of a ZNS SSD and closes the gap between application requirements and zone interface properties. Our evaluations over RocksDB demonstrate that eZNS outperforms a static zoned interface by 17.7% and 80.3% in throughput and tail latency, respectively, at most.