Redefining 360° Video Navigation: A Novel Benchmark for Enhanced Immersive and Automated Exploration

360° video navigation aims to model users' viewing behavior in immersive videos through continuous viewport selection. This task is challenging because human viewing is not driven by instantaneous responses to local saliency, but by continuous decisions that evolve with semantic events over time, involving attention shifts across events and long-term viewport changes. However, progress remains limited, mainly due to the lack of a well-established benchmark, resulting in limitations in data, annotation, and evaluation. Current datasets are limited in scale and diversity, and mostly consist of short or single-event videos, making cross-event viewing behavior underrepresented. Existing annotation methods rely on frame-level or short-term clustering, which emphasizes local consistency but fails to capture temporal continuity and the rationality of viewport selection during event transitions. Existing metrics focus on spatial overlap or trajectory consistency, ignoring motion-direction agreement between predicted and ground-truth navigation trajectories. As a result, spatially accurate predictions may still follow incorrect motion directions, leading to unreliable evaluation of viewing behavior. To address these issues, we construct a new benchmark for 360° video navigation in multi-event scenarios, with a standardized data and annotation pipeline and a more discriminative evaluation protocol. We further develop an event-level model to capture cross-event viewing behavior and long-term viewport evolution. Extensive experiments show that the proposed benchmark provides more reliable evaluation, and the proposed model achieves superior performance to existing methods.