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arXiv · 2604.05070

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

Shiyao Qian·Yuan Ren·Dongfeng Bai·Bingbing Liu
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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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Shiyao Qian, Yuan Ren, Dongfeng Bai, Bingbing Liu. 2026-04-06. Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation. https://arxiv.org/abs/2604.05070

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