Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation

• URL: http://arxiv.org/abs/2604.05070v1
• Date: Mon, 06 Apr 2026 18:16:12 GMT
• Title: Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation
• Abstract summary: Simulation is essential for autonomous driving, yet current frameworks often model vehicles as rigid assets and fail to capture part-level articulation.<n>We propose a generative framework that synthesizes an animatable 3D Gaussian vehicle from a single image or sparse multi-view input.<n>Our method addresses two challenges: (i) large 3D asset generators are optimized for static quality but not articulation, leading to distortions at part boundaries when animated; and (ii) segmentation alone cannot provide the kinematic parameters required for motion.
• Score: 19.45064081855758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulation is essential for autonomous driving, yet current frameworks often model vehicles as rigid assets and fail to capture part-level articulation. With perception algorithms increasingly leveraging dynamics such as wheel steering or door opening, realistic simulation requires animatable vehicle representations. Existing CAD-based pipelines are limited by library coverage and fixed templates, preventing faithful reconstruction of in-the-wild instances. We propose a generative framework that, from a single image or sparse multi-view input, synthesizes an animatable 3D Gaussian vehicle. Our method addresses two challenges: (i) large 3D asset generators are optimized for static quality but not articulation, leading to distortions at part boundaries when animated; and (ii) segmentation alone cannot provide the kinematic parameters required for motion. To overcome this, we introduce a part-edge refinement module that enforces exclusive Gaussian ownership and a kinematic reasoning head that predicts joint positions and hinge axes of movable parts. Together, these components enable faithful part-aware simulation, bridging the gap between static generation and animatable vehicle models.

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