Object skeletons offer a concise representation of structural information, capturing essential aspects of posture and orientation that are crucial for autonomous driving applications. However, a unified architecture that simultaneously handles multiple instances and categories using only the input image remains elusive. In this paper, we introduce PoseDriver, a unified framework for bottom-up multi-category skeleton detection tailored to common objects in driving scenarios. We model each category as a distinct task to systematically address the challenges of multi-task learning. Specifically, we propose a novel approach for lane detection based on skeleton representations, achieving state-of-the-art performance on the OpenLane dataset. Moreover, we present a new dataset for bicycle skeleton detection and assess the transferability of our framework to novel categories. Experimental results validate the effectiveness of the proposed approach.
@article{arxiv.2603.23215,
title = {PoseDriver: A Unified Approach to Multi-Category Skeleton Detection for Autonomous Driving},
author = {Yasamin Borhani and Taylor Mordan and Yihan Wang and Reyhaneh Hosseininejad and Javad Khoramdel and Alexandre Alahi},
journal= {arXiv preprint arXiv:2603.23215},
year = {2026}
}