Modeling spinal motion is fundamental to understanding human biomechanics, yet remains underexplored in computer vision due to the spine's complex multi-joint kinematics and the lack of large-scale 3D annotations. We present a biomechanics-aware keypoint simulation framework that augments existing human pose datasets with anatomically consistent 3D spinal keypoints derived from musculoskeletal modeling. Using this framework, we create the first open dataset, named SIMSPINE, which provides sparse vertebra-level 3D spinal annotations for natural full-body motions in indoor multi-camera capture without external restraints. With 2.14 million frames, this enables data-driven learning of vertebral kinematics from subtle posture variations and bridges the gap between musculoskeletal simulation and computer vision. In addition, we release pretrained baselines covering fine-tuned 2D detectors, monocular 3D pose lifting models, and multi-view reconstruction pipelines, establishing a unified benchmark for biomechanically valid spine motion estimation. Specifically, our 2D spine baselines improve the state-of-the-art from 0.63 to 0.80 AUC in controlled environments, and from 0.91 to 0.93 AP for in-the-wild spine tracking. Together, the simulation framework and SIMSPINE dataset advance research in vision-based biomechanics, motion analysis, and digital human modeling by enabling reproducible, anatomically grounded 3D spine estimation under natural conditions.

翻译：脊柱运动建模是理解人体生物力学的关键基础，但由于脊柱具有复杂的多关节运动学特性且缺乏大规模三维标注数据，该领域在计算机视觉中仍待深入探索。本文提出一种生物力学感知的关键点仿真框架，该框架通过从肌肉骨骼模型推导出解剖学一致的三维脊柱关键点，对现有人体姿态数据集进行增强。基于此框架，我们创建了首个开放数据集SIMSPINE，该数据集为室内多相机无约束条件下采集的自然全身运动提供了稀疏椎骨级三维脊柱标注。该数据集包含214万帧图像，能够从细微姿态变化中实现数据驱动的椎骨运动学学习，并弥合了肌肉骨骼仿真与计算机视觉之间的鸿沟。此外，我们发布了涵盖微调二维检测器、单目三维姿态提升模型和多视角重建流程的预训练基线模型，为生物力学有效的脊柱运动估计建立了统一基准。具体而言，我们的二维脊柱基线模型在受控环境中将最佳性能从0.63 AUC提升至0.80 AUC，在自然场景脊柱跟踪任务中将平均精度从0.91 AP提升至0.93 AP。该仿真框架与SIMSPINE数据集共同推动了基于视觉的生物力学、运动分析和数字人体建模研究，为实现自然条件下可复现、解剖学基础扎实的三维脊柱估计提供了支撑。