Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2026

Abstract

The research frontier in human pose prediction (HPP) is advancing toward continual test-time adaptation (TTA), where models must self-adapt to dynamic test distributions. To date, the homeostatic continual TTA remains the sole viable solution, which isolates the model parameters and update domain-sensitive ones. Despite mitigating full-body domain gaps, human anatomical heterogeneity (domain shifts often localize to specific regions) is ignored. This anatomical-agnostic approach forces uniform parameter adaptation across kinematically distinct segments, causing: over-adaptation of stable regions and under-adaptation of shift-prone articulations. To address it, we introduce TT-HA, a novel Test-Time Heterogeneous Adaptation that implicitly estimates domain changes for anatomical segments, and adapt the corresponding parameters. Building on human anatomy, TT-HA partitions parameters into five anatomical subsets using fisher information matrix-based parameters uncertainty analysis. During testing, TT-HA uses the instance normalization statistics and Earth Mover's Distance (EMD) to quantify segment-wise domain changes, dynamically determining which segment-specific parameters to adapt and to what extent. When substantial domain shifts are detected, TT-HA restores only affected segments to source-trained values, ensuring robust adaptation without full parameter resetting; minor shifts trigger the fine-tuning of corresponding parameters while preserving remaining ones. Experiments show TT-HA's superior full-body accuracy with greater limb error decrease than prior methods, proving its anatomically-targeted efficacy.

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026), Denver, Colorado, United States, June 3-7

First Page

28369

Last Page

28378

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Additional URL

https://cvpr.thecvf.com/virtual/2026/poster/36987

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