论文标题
功能驱动的肌肉重新定位
Functionality-Driven Musculature Retargeting
论文作者
论文摘要
我们提出了一种新型的重新定位算法，该算法将参考解剖模型的肌肉转移到具有不同尺寸，身体比例，肌肉能力和运动范围的新体的同时，同时尽可能紧密地保留原始肌肉的功能。估计并优化了Musculotendon单元的几何配置和生理参数，以适应新体。从运动捕获数据集估算关节周围的运动范围，并为各个模型进行进一步编辑。重新定位的模型已经准备好模拟，因此我们可以通过模型对肌肉分动的运动技能进行物理模拟。我们的系统能够产生各种各样的解剖体，这些解剖结构可以模拟，以便在重力下保持平衡，以行走，跑步，跳舞和跳舞。我们还将展示从双色X射线图像和体检中的个性化肌肉骨骼模型的构建。
We present a novel retargeting algorithm that transfers the musculature of a reference anatomical model to new bodies with different sizes, body proportions, muscle capability, and joint range of motion while preserving the functionality of the original musculature as closely as possible. The geometric configuration and physiological parameters of musculotendon units are estimated and optimized to adapt to new bodies. The range of motion around joints is estimated from a motion capture dataset and edited further for individual models. The retargeted model is simulation-ready, so we can physically simulate muscle-actuated motor skills with the model. Our system is capable of generating a wide variety of anatomical bodies that can be simulated to walk, run, jump and dance while maintaining balance under gravity. We will also demonstrate the construction of individualized musculoskeletal models from bi-planar X-ray images and medical examinations.
