  The Alberta Infant Motor Scale (AIMS) is a well-known assessment scheme that
evaluates the gross motor development of infants by recording the number of
specific poses achieved. With the aid of the image-based pose recognition
model, the AIMS evaluation procedure can be shortened and automated, providing
early diagnosis or indicator of potential developmental disorder. Due to
limited public infant-related datasets, many works use the SMIL-based method to
generate synthetic infant images for training. However, this domain mismatch
between real and synthetic training samples often leads to performance
degradation during inference. In this paper, we present a CNN-based model which
takes any infant image as input and predicts the coarse and fine-level pose
labels. The model consists of an image branch and a pose branch, which
respectively generates the coarse-level logits facilitated by the unsupervised
domain adaptation and the 3D keypoints using the HRNet with SMPLify
optimization. Then the outputs of these branches will be sent into the
hierarchical pose recognition module to estimate the fine-level pose labels. We
also collect and label a new AIMS dataset, which contains 750 real and 4000
synthetic infants images with AIMS pose labels. Our experimental results show
that the proposed method can significantly align the distribution of synthetic
and real-world datasets, thus achieving accurate performance on fine-grained
infant pose recognition.
