Infant Pose Learning with Small Data.

With the increasing maturity of the human pose estimation domain, its applications have become more and more broaden. Yet, the state-of-the-art pose estimation models performance degrades significantly in the applications that include novel subjects or poses, such as infants with their unique movements. Infant motion analysis is a topic with critical importance in child health and developmental studies. However, models trained on large-scale adult pose datasets are barely successful in estimating infant poses due to significant differences in their body ratio and the versatility of poses they can take compared to adults. Moreover, the privacy and security considerations hinder the availability of enough infant images required for training a robust pose estimation model from scratch. Here, we propose a fine-tuned domain-adapted infant pose (FiDIP) estimation model, that transfers the knowledge of adult poses into estimating infant pose with the supervision of a domain adaptation technique on a mixed real and synthetic infant pose dataset. In developing FiDIP, we also built a synthetic and real infant pose (SyRIP) dataset with diverse and fully-annotated real infant images and generated synthetic infant images. We demonstrated that our FiDIP model outperforms other state-of-the-art human pose estimation model for the infant pose estimation, with the mean average precision (AP) as high as 92.2.