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

• URL: http://arxiv.org/abs/2604.12626v1
• Date: Tue, 14 Apr 2026 11:52:59 GMT
• Title: Habitat-GS: A High-Fidelity Navigation Simulator with Dynamic Gaussian Splatting
• Abstract summary: Habitat-GS is a navigation-centric embodied AI simulator that integrates 3D photorealistic scene rendering and drivable gaussian avatars.<n>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.
• Score: 83.60803397468139
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training embodied AI agents depends critically on the visual fidelity of simulation environments and the ability to model dynamic humans. Current simulators rely on mesh-based rasterization with limited visual realism, and their support for dynamic human avatars, where available, is 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, with mixed-domain training being the most effective strategy. Evaluations on avatar-aware navigation further confirm that gaussian avatars enable effective human-aware navigation. Finally, performance benchmarks validate the system's scalability across varying scene complexity and avatar counts.

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