首个无约束下人体脊柱2D姿态估计数据集，助力运动与医疗分析
Towards Unconstrained 2D Pose Estimation of the Human Spine
- 构建9个脊椎关键点的合成与真实数据集，支持复杂场景标注
- 通过知识蒸馏和解剖正则化，提升脊柱姿态估计精度
- 适用于运动分析、医疗评估及虚拟动画等真实世界应用
我们提出SpineTrack，首个面向无约束环境下人体脊柱2D姿态估计的综合性数据集，填补体育分析、医疗健康和真实动画领域的空白。现有姿态数据集常将脊柱简化为单一刚性段，忽略其精细关节运动。SpineTrack在两个互补子集中标注了9个详细脊椎关键点：合成集包含25,000个标注，基于Unreal Engine生成并用OpenSim进行生物力学对齐；真实集包含超过33,000个标注，通过主动学习迭代优化自动标注与人工反馈。该方法确保大规模下解剖一致性标签，即便在复杂真实图像中亦可实现。我们进一步提出SpinePose，采用知识蒸馏与解剖正则化策略，联合预测身体与脊柱关键点。实验在通用及体育特定场景中验证了SpineTrack的有效性，为未来野外三维脊柱重建与高级生物力学分析奠定坚实基础。
原文摘要 · Abstract (English)
We present SpineTrack, the first comprehensive dataset for 2D spine pose estimation in unconstrained settings, addressing a crucial need in sports analytics, healthcare, and realistic animation. Existing pose datasets often simplify the spine to a single rigid segment, overlooking the nuanced articulation required for accurate motion analysis. In contrast, SpineTrack annotates nine detailed spinal keypoints across two complementary subsets: a synthetic set comprising 25k annotations created using Unreal Engine with biomechanical alignment through OpenSim, and a real-world set comprising over 33k annotations curated via an active learning pipeline that iteratively refines automated annotations with human feedback. This integrated approach ensures anatomically consistent labels at scale, even for challenging, in-the-wild images. We further introduce SpinePose, extending state-of-the-art body pose estimators using knowledge distillation and an anatomical regularization strategy to jointly predict body and spine keypoints. Our experiments in both general and sports-specific contexts validate the effectiveness of SpineTrack for precise spine pose estimation, establishing a robust foundation for future research in advanced biomechanical analysis and 3D spine reconstruction in the wild.
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