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

TL;DR

This paper introduces a generative framework that creates animatable 3D Gaussian vehicle models from minimal input, incorporating part-level articulation and kinematic reasoning for realistic simulation.

Contribution

It presents a novel method combining part-edge refinement and kinematic prediction to produce faithful, animatable 3D vehicle models from sparse data.

Findings

Successfully generates part-aware, animatable vehicle models from images.

Addresses distortions at part boundaries through edge refinement.

Predicts joint positions and hinge axes for realistic articulation.

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.