Pose and Shape Estimation of Humans in Vehicles

As autonomous driving technologies advance, occupants are expected to be free from driving, diversifying interaction scenarios with vehicles. Despite the growing importance of in-vehicle occupant monitoring systems, most existing systems focus on the face or head tracking of occupants, and only a few studies have attempted to detect their poses. In this paper, we present the first in-vehicle environment-specialized framework for the joint estimation of 3D human pose and shape from a single image. To this end, we introduce a new dataset called Human In VEhicles (HIVE), which contains a large collection of synthesized humans with different shapes and poses in vehicle images. HIVE provides RGB and NIR in-vehicle image pairs with ground-truth 2D and 3D pose and shape annotations, respectively. In addition, to exploit the different characteristics of humans in vehicles and unconstrained environments, we present a new pose prior penalizing poses that deviate from in-vehicle poses. The pose prior is derived using a variational autoencoder trained with in-vehicle human pose data. By using the proposed HIVE dataset and pose prior along with an elaborately designed two-stage training procedure, our method exhibits significantly improved pose and shape estimation performance compared with state-of-the-art methods for real-world test images captured in vehicles under different conditions.