InfantNet: A Large Scale Dataset for Infant Body Pose and Shape Estimation

Zhipeng Hui, Jiahao Wang, Xun Dong, Guofeng Zhang, Xingrui Wang, Jiawei Peng, Qihao Liu, Xiaoding Yuan, Yi Zhang, Junjie Oscar Yin, Wufei Ma, Qinfan Lyu, Sara N Lupolt, Keeve Edward Nachman, Alan Yuille

Keywords: Infant Pose, Keypoint Detection, SMIL, Pose Estimation

Abstract: Infant pose and shape estimation is essential for applications in childcare, developmental monitoring, and medical diagnosis. However, existing methods and datasets are largely designed for adults, and direct transfer to infants fails due to substantial differences in body proportions, articulation limits, and frequent self-occlusion. To address this gap, we introduce InfantNet, the largest real-image infant dataset to date, comprising 108,902 RGB images of infants aged 6-18 months. Each image is annotated with 2D keypoints, and a curated subset of 11,642 images additionally includes 3D pose and shape annotations with full SMIL parameters. We use an iterative annotation pipeline to ensure high fidelity across both 2D and 3D labels. InfantNet establishes a large-scale, comprehensive benchmark for infant 2D keypoint detection and 3D pose-and-shape recovery. Baseline experiments demonstrate that state-of-the-art adult pose estimators do not generalize well to infants, whereas fine-tuning on InfantNet yields a consistent improvement. The gains are even more pronounced for 3D pose and shape estimation. By releasing the InfantNet dataset and benchmark, we provide a vital resource for advancing infant pose analysis and related healthcare applications.

Primary Area: datasets and benchmarks

Submission Number: 5333