SIMSPINE: A Biomechanics-Aware Simulation Framework for 3D Spine Motion Annotation and Benchmarking

TL;DR

A biomechanics-aware keypoint simulation framework that augments existing human pose datasets with anatomically consistent 3D spinal keypoints derived from musculoskeletal modeling, and creates 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.

Abstract

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.

Figures (7)

• Figure 1: SimSpine annotations. Neutral pose of the simulated spine model (left), divided into three anatomical regions—cervical (pink), thoracic (blue), and lumbar (green)—with 15 annotated landmarks: 9 along the vertebral column, 2 on the skull, 2 at the clavicle joints, and 2 on the shoulder blades (last 4 not shown). Cervical and thoracic motion is limited to transition junctions (indicated by anatomical axis markers), while intermediate vertebrae remain rigidly coupled. The lumbar segment (L1–L5) is fully articulated, with intervertebral rotations simulated for all degrees of freedom (bottom right). Overall spinal curvature is characterized by thoracic kyphosis ($\theta_k$) and lumbar lordosis ($\theta_l$) angles. Motion was generated using beaucage2019validation's musculoskeletal model beaucage2019validation as a function of full-body movement in Human3.6M, with 5 training and 2 validation subjects performing 15 actions.
• Figure 2: Biomechanics-aware keypoint simulation pipeline. From synchronized multi-view RGB, a 2D detector khan2025spine predicts spinal landmarks that are robustly triangulated using calibrated cameras to obtain pseudo-3D spinal keypoints. These pseudo labels are temporally aligned and merged with known Human3.6M ionescu2014h36m 3D markers (GT 3D Pose). OpenSim inverse kinematics (IK) delp2007opensim fits a subject-scaled full-body model pagnon2022pose2simrajagopal2016fullbeaucage2019validation to the merged trajectories. We attach virtual markers to vertebral bodies and, using the IK joint angles and subject-specific anthropometrics, generate anatomically consistent spine keypoints via forward kinematics (FK). The pipeline also outputs biomechanical parameters (e.g., per-vertebra rotations).
• Figure 3: Thoracolumbar spine in SimSpine.Left: Distributions of thoracolumbar curvature across actions, defined by the Lumbar Lordotic Angle (LLA, $\theta_l$) in the lower back and Thoracic Kyphotic Angle (TKA, $\theta_k$) in the upper back. LLA and TKA average within 1 SD at 33–39° and 29–37°, respectively, indicating greater curvature in the lower spine but higher variability in the upper. Values fall within reported biomechanical ranges lin1992lumbarfon1980thoracic, confirming that SimSpine produces anatomically plausible curvatures and captures expected action-specific postural trends bae2012comparisoncho2015effecttsagkaris2022sittingsipko2024impactroren2024arm. Right: Per-vertebra range of motion (ROM) on y-axis for the three lumbar rotational DOFs. Our simulated data (solid) follows similar trends as reported by White and Panjabi (1978) white1978basic (dashed).
• Figure 4: Cervical spine in SimSpine. The distributions (per action) remain centered near neutral with task-dependent spread, reflecting that our model uses a single 3-DOF aggregate neck joint while keeping the thoracic/cervical bodies rigid beyond the cervicothoracic junction. This is within the neck ROM reported in doss2023comprehensive with approximately half coverage, which indicates the presence of only small head movements in the dataset.
• Figure 5: Ablation Study: Mixup Composition. We examine how the fraction of SimSpine used in training influences indoor (AUC) and outdoor (AP) performance. Increasing the SimSpine fraction improves indoor performance up to 10%, while outdoor gains saturate by 2--5%. Per-batch (PB) mixup maintains the best balance between indoor and outdoor metrics, whereas per-epoch (PE) alternation favors one domain at the expense of the other. Sampling only 2% of SimSpine achieves near-saturated results on both datasets, indicating diminishing returns from larger fractions.