Existing digital human models approximate the human skeletal system using rigid bodies connected by rotational joints. While the simplification is considered acceptable for legs and arms, it significantly lacks fidelity to model rich torso movements in common activities such as dancing, Yoga, and various sports. Research from biomechanics provides more detailed modeling for parts of the torso, but their models often operate in isolation and are not fast and robust enough to support computationally heavy applications and large-scale data generation for full-body digital humans. This paper proposes a new torso model that aims to achieve high fidelity both in perception and in functionality, while being computationally feasible for simulation and optimal control tasks. We build a detailed human torso model consisting of various anatomical components, including facets, ligaments, and intervertebral discs, by coupling efficient finite-element and rigid-body simulations. Given an existing motion capture sequence without dense markers placed on the torso, our new model is able to recover the underlying torso bone movements. Our method is remarkably robust that it can be used to automatically "retrofit" the entire Mixamo motion database of highly diverse human motions without user intervention. We also show that our model is computationally efficient for solving trajectory optimization of highly dynamic full-body movements, without relying on any reference motion. Physiological validity of the model is validated against established literature.

翻译：现有数字人体模型通过旋转关节连接的刚体近似人体骨骼系统。虽然这种简化对于四肢建模尚可接受，但在舞蹈、瑜伽及各类体育运动等常见活动中，其模拟丰富躯干运动的能力显著不足。生物力学研究为躯干部分提供了更精细的建模方法，但这些模型往往独立运行，且速度和鲁棒性不足以支持计算密集型应用及全身数字人体的大规模数据生成。本文提出一种新的躯干模型，旨在实现感知与功能双重高保真度的同时，保持仿真与最优控制任务的计算可行性。我们通过耦合高效有限元与刚体仿真，构建了包含椎面、韧带、椎间盘等多种解剖组分的精细人体躯干模型。对于未在躯干部署密集标记点的现有运动捕捉序列，该模型能够恢复潜在躯干骨骼运动。本方法具有显著鲁棒性，可自动对包含高度多样化人体运动的Mixamo运动数据库进行"改装"而无需人工干预。我们还证明该模型在无需依赖任何参考运动的情况下，对高动态全身运动的轨迹优化求解具有计算高效性。模型的生理学有效性已通过现有文献验证。