针对健全肢体、残肢与假肢的拓扑统一二维姿态估计
Topology-Unified 2D Pose Estimation across Intact, Residual and Prosthetic Limbs
- The University of Queensland（昆士兰大学）
- Australian Institute for Machine Learning（澳大利亚机器学习研究院）
- Adelaide University（阿德莱德大学）
- City University of Macau（澳门城市大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对健全肢体、残肢与假肢的表征偏差问题，构建含统一拓扑标注的ProPose基准，结合Real-to-Synthetic数据扩展与结构感知的ProLoss，提升长尾假肢关节分类准确率2%-6%，为包容性姿态估计奠定基础。
AI中文摘要：
受大规模数据集可用性的驱动，人体姿态估计（Human Pose Estimation, HPE）在众多下游任务中发挥着关键作用。然而，主流基准数据集存在严重的表征偏差，主要以健全个体为特征。尽管少数开创性数据集试图解决肢体差异问题，但其标注协议无法泛化，难以表征跑步义肢或未假肢化残肢等特殊机械结构。为弥合这一差距，我们推出ProPose，这是一个大规模基准数据集，采用新颖的标注协议，在单一框架内统一生物肢体、各类假肢及物理缺失的拓扑表征。由于现实世界中的假肢图像天生稀缺且呈现极端长尾分布，我们设计了Real-to-Synthetic数据扩展流水线，以显式合成并扩充代表性不足的样本。然而，仅在该丰富数据集上训练现有模型往往会导致次优解决方案，因为这些模型独立估计每个关键点，可能在机械结构上生成不存在的关节。为解决此问题，我们提出ProLoss，一种结构感知的目标函数，强制单条肢体内部的关键点依赖关系，以防止不切实际的肢体预测。大量实验表明，我们的方法将长尾假肢关节的分类准确率提升了2%至6%，同时不损害空间坐标定位性能。本研究为包容性姿态估计奠定了基础，为理解人体与辅助设备之间的交互开辟了新的可能性。
英文摘要：
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.
