  Visual navigation, a foundational aspect of Embodied AI (E-AI), has been
significantly studied in the past few years. While many 3D simulators have been
introduced to support visual navigation tasks, scarcely works have been
directed towards combining human dynamics, creating the gap between simulation
and real-world applications. Furthermore, current 3D simulators incorporating
human dynamics have several limitations, particularly in terms of computational
efficiency, which is a promise of E-AI simulators. To overcome these
shortcomings, we introduce HabiCrowd, the first standard benchmark for
crowd-aware visual navigation that integrates a crowd dynamics model with
diverse human settings into photorealistic environments. Empirical evaluations
demonstrate that our proposed human dynamics model achieves state-of-the-art
performance in collision avoidance, while exhibiting superior computational
efficiency compared to its counterparts. We leverage HabiCrowd to conduct
several comprehensive studies on crowd-aware visual navigation tasks and
human-robot interactions. The source code and data can be found at
https://habicrowd.github.io/.
