Functionality‐Driven Musculature Retargeting,Computer Graphics Forum

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Functionality‐Driven Musculature Retargeting
Computer Graphics Forum ( IF 2.7 ) Pub Date : 2021-01-13 , DOI: 10.1111/cgf.14191
Hoseok Ryu ₁ , Minseok Kim ₁ , Seungwhan Lee ₁ , Moon Seok Park ₂ , Kyoungmin Lee ₂ , Jehee Lee ₁

Affiliation

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 examination.

中文翻译：

功能驱动的肌肉重定位

我们提出了一种新颖的重新定位算法，该算法将参考解剖模型的肌肉组织转移到具有不同大小，身体比例，肌肉功能和关节活动范围的新身体上，同时尽可能地保留原始肌肉组织的功能。估计并优化了肌腱单位的几何构型和生理参数以适应新的身体。关节周围的运动范围是根据运动捕获数据集估算的，并针对各个模型进行进一步编辑。重新定向的模型已经准备好进行仿真，因此我们可以使用该模型来物理模拟肌肉驱动的运动技能。我们的系统能够生成各种各样的解剖体，可以模拟它们走路，奔跑，跳跃和跳舞，同时在重力下保持平衡。

更新日期：2021-02-24

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