Habitat-GS: A High-Fidelity Navigation Simulator with Dynamic Gaussian Splatting

Abstract:Training embodied AI agents depends critically on the visual fidelity of simulation environments and the ability to model dynamic humans. Current simulators predominantly rely on mesh-based rasterization, for which photorealistic assets are costly to author at scale, and their support for dynamic human avatars is largely constrained to mesh representations, hindering agent generalization to human-populated real-world scenarios. We present Habitat-GS, a navigation-centric embodied AI simulator extended from Habitat-Sim that integrates 3D Gaussian Splatting scene rendering and drivable gaussian avatars while maintaining full compatibility with the Habitat ecosystem. Our system implements a 3DGS renderer for real-time photorealistic rendering and supports scalable 3DGS asset import from diverse sources. For dynamic human modeling, we introduce a gaussian avatar module that enables each avatar to simultaneously serve as a photorealistic visual entity and an effective navigation obstacle, allowing agents to learn human-aware behaviors in realistic settings. Experiments on point-goal navigation demonstrate that agents trained on 3DGS scenes achieve stronger cross-domain generalization. Evaluations on avatar-aware navigation further confirm that gaussian avatars enable effective human-aware navigation, while performance benchmarks validate the system's scalability. Code is available at this https URL.

Accepted to ECCV 2026. Project page: this https URL

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2604.12626 [cs.RO]

(or arXiv:2604.12626v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2604.12626 Focus to learn more arXiv-issued DOI via DataCite

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