Habitat-GS: A High-Fidelity Navigation Simulator with Dynamic Gaussian Splatting
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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Ziyuan Xia
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Jingyi Xu
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Chong Cui
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Yuanhong Yu
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2026-04-15T12:05:55.717Z
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yyh929
user
yyh929
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Jiazhao Zhang
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Qingsong Yan
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Tao Ni
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Junbo Chen
69deee98d3c84fcbf292157d
Xiaowei Zhou
69deee98d3c84fcbf292157e
Hujun Bao
69deee98d3c84fcbf292157f
Ruizhen Hu
69deee98d3c84fcbf2921580
Sida Peng
Habitat-GS: A High-Fidelity Navigation Simulator with Dynamic Gaussian Splatting
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.
Habitat-GS extends Habitat-Sim by integrating 3D Gaussian Splatting for photorealistic rendering and gaussian avatars for dynamic human modeling, enabling improved agent generalization and human-aware navigation in embodied AI.
3D Gaussian Splatting Habitat-Sim photorealistic rendering gaussian avatars embodied AI point-goal navigation cross-domain generalization human-aware navigation 3DGS renderer navigation obstacle