We present a multi-person soft-tissue avatar model. This model maps a body shape descriptor to heterogeneous geometric and mechanical parameters of a soft-tissue model across the body, effectively producing a shape-dependent parametric soft avatar model. The design of the model overcomes two major challenges, the potential redundancy of geometric and mechanical parameters, and the complexity to obtain abundant subject data, which together induce major risk of overfitting the resulting model. To overcome these challenges, we introduce a local shape-dependent regularization of the model. We demonstrate accurate results, on par with independent per-subject estimation, accurate interpolation within the range of body shapes of the training subjects, and good generalization to unseen body shapes. As a result, we obtain a parametric soft-flesh avatar model easy to integrate in many existing applications.

Abstract We present a method to augment parametric skeletal models with subspace soft‐tissue deformations. We combine the benefits of data‐driven skeletal models, i.e. accurate replication of contact‐free static deformations, with the benefits of pure physics‐based models, i.e. skin and skeletal reaction to contact and inertial motion with two‐way coupling. We succeed to do so in a highly efficient manner, thanks to a careful choice of reduced model for the subspace deformation. With our method, it is easy to design expressive reduced models with efficient yet accurate force computations, without the need for training deformation examples. We demonstrate the application of our method to parametric models of human bodies, SMPL, and hands, MANO, with interactive simulations of contact with nonlinear soft‐tissue deformation and skeletal response.>