SIMSPINE：面向三维脊柱运动标注与基准测试的生物力学感知仿真框架
SIMSPINE: A Biomechanics-Aware Simulation Framework for 3D Spine Motion Annotation and Benchmarking
提供首个大规模脊柱三维关键点数据集与仿真标注框架，可用于训练和评测从单目/多视角图像估计脊柱运动的方法，并作为生物力学约束引入姿态估计的基准。
原文依据 We present a biomechanics-aware keypoint simulation framework that augments existing human pose datasets with anatomically consistent 3D spinal keypoints derived from musculoskeletal modeling.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论脊柱运动建模是理解人体生物力学的基础，但由于脊柱复杂的多关节运动学以及缺乏大规模三维标注，该问题在计算机视觉领域仍未得到充分探索。我们提出一个生物力学感知的关键点仿真框架，利用肌肉骨骼建模生成的解剖学一致的三维脊柱关键点来增强现有的人体姿态数据集。基于该框架，我们构建了首个开放数据集SIMSPINE，为室内多相机采集的自然全身运动提供稀疏的椎体级三维脊柱标注，且无需外部约束。该数据集包含214万帧，使得从细微姿态变化中数据驱动地学习椎体运动学成为可能，并弥合了肌肉骨骼仿真与计算机视觉之间的鸿沟。此外，我们发布了预训练基线，涵盖微调的二维检测器、单目三维姿态提升模型和多视角重建流程，为生物力学有效的脊柱运动估计建立了统一的基准。具体而言，我们的二维脊柱基线在受控环境中将最优AUC从0.63提升至0.80，在野外脊柱跟踪中将AP从0.91提升至0.93。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，使得在自然条件下进行可重复的、解剖学基础的三维脊柱估计成为可能。
英文原文摘要 1469 字符
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.