Functionality-Driven Musculature Retargeting

TL;DR

This paper introduces a new retargeting algorithm that adapts musculature from a reference model to diverse bodies, enabling realistic simulation of motor skills while preserving original functionality.

Contribution

The novel retargeting method estimates and optimizes musculotendon parameters for different bodies, allowing accurate, simulation-ready musculoskeletal models from various data sources.

Findings

Able to generate diverse anatomies capable of walking, running, jumping, and dancing.

Models maintain balance and functionality across different body sizes and shapes.

Constructs individualized models from medical imaging data.

Abstract

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.