  Infant motion analysis is a topic with critical importance in early childhood
development studies. However, while the applications of human pose estimation
have become more and more broad, models trained on large-scale adult pose
datasets are barely successful in estimating infant poses due to the
significant differences in their body ratio and the versatility of their poses.
Moreover, the privacy and security considerations hinder the availability of
adequate infant pose data required for training of a robust model from scratch.
To address this problem, this paper presents (1) building and publicly
releasing a hybrid synthetic and real infant pose (SyRIP) dataset with small
yet diverse real infant images as well as generated synthetic infant poses and
(2) a multi-stage invariant representation learning strategy that could
transfer the knowledge from the adjacent domains of adult poses and synthetic
infant images into our fine-tuned domain-adapted infant pose (FiDIP) estimation
model. In our ablation study, with identical network structure, models trained
on SyRIP dataset show noticeable improvement over the ones trained on the only
other public infant pose datasets. Integrated with pose estimation backbone
networks with varying complexity, FiDIP performs consistently better than the
fine-tuned versions of those models. One of our best infant pose estimation
performers on the state-of-the-art DarkPose model shows mean average precision
(mAP) of 93.6.
