3D Infant Pose Estimation Using Transfer Learning

This paper presents the first deep learning-based 3D infant pose estimation model. We transfer-learn models first trained in the adult domain. The model outperforms the current 2D and 3D state-of-the-art on the synthetic infant MINI-RGBD test dataset, achieving an average joint position error (AJPE) of 8.17 pixels and 28.47 mm respectively. Furthermore, unlike the current 3D state-of-the-art, the model presented here does not require a depth channel as input. This is an important step in the development of an automated general movement assessment tool for infants, which has the potential to support the diagnosis of a range of neurological disorders, including cerebral palsy.