We present a novel method to enrich existing vertex-based human body models by adding soft-tissue dynamics. Our model learns to predict per-vertex 3D offsets, referred to as dynamic blendshapes, that reproduce nonlinear mesh deformation effects as a function of pose information. This enables the synthesis of realistic 3D mesh animations, including soft-tissue effects, using just skeletal motion. At the core of our method there is a neural network regressor trained on high-quality 4D scans from which we extract pose, shape and soft-tissue information. Our regressor uses a novel nonlinear subspace, which we build using an autoencoder, to efficiently compact soft-tissue dynamics information. Once trained, our method can be plugged to existing vertex-based skinning methods with little computational overhead (<10ms), enabling real-time nonlinear dynamics.We qualitatively and quantitatively evaluate our method, and show compelling animations with soft-tissue effects, created using publicly available motion capture datasets.

@Article\{CO18,
  author       = "Casas, Dan and Otaduy, Miguel A.",
  title        = "Learning Nonlinear Soft-Tissue Dynamics for Interactive Avatars",
  journal      = "Proceedings of the ACM on Computer Graphics and Interactive Techniques",
  number       = "1",
  volume       = "1",
  month        = "may",
  year         = "2018",
  url          = "http://gmrv.es/Publications/2018/CO18"
}