SOMA: From Surface Observations to Muscle Anatomy

With the growing demand for realistic virtual humans, para-metric body models have become a cornerstone of modern medicine, sportsor entertainment applications. However, most of these models are inherentlylimited: they only capture the 3D surface of the skin, offering no insightinto the complex bio-mechanical structures that generate motion. As moreapplications expand towards biomechanics, the need for virtual humanmodels that go beyond the skin has become increasingly evident. Traditionalsoft-tissue simulations, such as FEM, are accurate but non-scalable and toocomputationally expensive for most common applications. Alternatively,existing biomechanical tools can simulate muscular forces and activations,but do not model changes in external shape, restricting how activationscorrelate with actual observable anatomy. This motivates a novel inverseresearch problem: recovering muscle deformations directly from visiblesurface observations - i.e., from the skin, and thus the pose. In this work,we present SOMA (from Surface Observations to Muscle Anatomy), aperson-specific model that infers spatio-temporal muscle behavior fromsurface signals obtained using RGB cameras, and SKIM, a subject-specificsoft-tissue deformation dataset. To the best of our knowledge, this is thefirst method that attempts to recover muscle deformations from multi-viewRGB data. We show how our method provides anatomically groundedanimations without the complexity of traditional simulations, leading to ascalable and cost-effective solution. Data and code are available.