Topology-Unified 2D Pose Estimation across Intact, Residual and Prosthetic Limbs

2026-08-13

AI Summary

Existing human pose estimation methods struggle with atypical limb structures such as amputated or prosthetic limbs, primarily due to the absence of a unified topological representation and insufficient training data. This work introduces ProPose, a novel benchmark that establishes the first unified keypoint topology protocol accommodating biological limbs, prostheses, and missing limbs. To address anatomical and mechanical constraints inherent in such diverse structures, we propose ProLoss, a structure-aware loss function that explicitly models dependencies among keypoints, thereby preventing implausible pose predictions on non-biological configurations. Combined with a Real-to-Synthetic data augmentation strategy, our approach maintains high spatial localization accuracy while improving classification accuracy for prosthetic joints under long-tailed data distributions by 2%–6%.

Abstract

Driven by the availability of large-scale datasets, Human Pose Estimation (HPE) plays a critical role in numerous downstream tasks. However, mainstream benchmarks exhibit severe representation bias, predominantly featuring able-bodied individuals. While a few pioneering datasets have attempted to address limb differences, their annotation protocols fail to generalize, struggling to represent specialized mechanical structures like running blades or unprosthetized residual limbs. To bridge this gap, we introduce ProPose, a large-scale benchmark featuring a novel annotation protocol that unifies the topological representation of biological limbs, diverse prostheses, and physical absences within a single framework. Because real-world prosthetic images are inherently scarce and exhibit extreme long-tail distributions, we design a Real-to-Synthetic data expansion pipeline to explicitly synthesize and expand the underrepresented cases. However, simply training existing models on this enriched dataset often leads to suboptimal solutions, as they estimate each keypoint independently and might hallucinate non-existent joints on mechanical structures. To resolve this, we propose ProLoss, a structure-aware objective that enforces keypoint dependencies within a single limb to prevent unrealistic limb predictions. Extensive experiments demonstrate that our approach improves the classification accuracy of long-tail prosthetic joints by 2% to 6% without compromising spatial coordinate localization performance. This work sets a foundation for inclusive pose estimation, unlocking new possibilities for understanding the interactions between human bodies and assistive devices.

Problem

• Human Pose Estimation
• Prosthetic Limbs
• Representation Bias
• Topological Representation
• Inclusive Vision

Innovation

• topology-unified pose estimation
• prosthetic limbs
• Real-to-Synthetic data expansion
• structure-aware loss
• inclusive human pose estimation