Prior-guided Source-free Domain Adaptation for Human Pose Estimation
Dripta S. Raychaudhuri Calvin-Khang Ta Arindam Dutta Rohit Lal Amit K. Roy-Chowdhury
Domain adaptation methods for 2D human pose estimation typically require
continuous access to the source data during adaptation, which can be
challenging due to privacy, memory, or computational constraints. To address
this limitation, we focus on the task of source-free domain adaptation for pose
estimation, where a source model must adapt to a new target domain using only
unlabeled target data. Although recent advances have introduced source-free
methods for classification tasks, extending them to the regression task of pose
estimation is non-trivial. In this paper, we present Prior-guided Self-training
(POST), a pseudo-labeling approach that builds on the popular Mean Teacher
framework to compensate for the distribution shift. POST leverages
prediction-level and feature-level consistency between a student and teacher
model against certain image transformations. In the absence of source data,
POST utilizes a human pose prior that regularizes the adaptation process by
directing the model to generate more accurate and anatomically plausible pose
pseudo-labels. Despite being simple and intuitive, our framework can deliver
significant performance gains compared to applying the source model directly to
the target data, as demonstrated in our extensive experiments and ablation
studies. In fact, our approach achieves comparable performance to recent
state-of-the-art methods that use source data for adaptation.