Efficient Domain Adaptation via Generative Prior for 3D Infant Pose
  Estimation
Zhuoran Zhou Zhongyu Jiang Wenhao Chai Cheng-Yen Yang Lei Li Jenq-Neng Hwang
Although 3D human pose estimation has gained impressive development in recent
years, only a few works focus on infants, that have different bone lengths and
also have limited data. Directly applying adult pose estimation models
typically achieves low performance in the infant domain and suffers from
out-of-distribution issues. Moreover, the limitation of infant pose data
collection also heavily constrains the efficiency of learning-based models to
lift 2D poses to 3D. To deal with the issues of small datasets, domain
adaptation and data augmentation are commonly used techniques. Following this
paradigm, we take advantage of an optimization-based method that utilizes
generative priors to predict 3D infant keypoints from 2D keypoints without the
need of large training data. We further apply a guided diffusion model to
domain adapt 3D adult pose to infant pose to supplement small datasets.
Besides, we also prove that our method, ZeDO-i, could attain efficient domain
adaptation, even if only a small number of data is given. Quantitatively, we
claim that our model attains state-of-the-art MPJPE performance of 43.6 mm on
the SyRIP dataset and 21.2 mm on the MINI-RGBD dataset.