Humans live in a real world governed by the laws of physics; that is, we apply and exploit forces, such as gravity, in our daily interactions with the world. In this project we allow virtual humans to interact with a virtual world subject to the laws of physics. How would one's body shape deform in case of a collision with an object? How would our walk pattern look like if we weighed a few kilos more?
To model soft-tissue dynamics, we learn a layered volumetric body model from data [ ]. To enable this we extend the triangulated mesh of the SMPL body model with a volumetric tetrahedral model called VSMPL. VSMPL contains an inner "rigid" layer and an outer soft-tissue layer. Given 4D sequences of people in motion, we learn the physical properties (Young's modulus) of the outer tetrahedra. We do this such that, when simulated in motion using a finite element method, the surface motion of the VSMPL model resembles the observations. The learned model is a realistic full-body avatar that generalizes to novel motions and external forces.
We also address the problem of retargeting the captured motion of one person onto a different person with a different body shape and physical properties (e.g.~taller, heavier, thinner) such that the new morphology is taken into account [ ]. We obtain visually plausible simulations using a simplified representation of human body shape that we animate using physically-based retargeting. We develop a novel spacetime optimization approach that learns and robustly adapts physical controllers to new bodies and constraints. The method automatically adapts the motion to a subject with the novel target body shape, respecting the physical properties, and producing an appropriate movement. This makes it easy to create a varied set of motions from a single mocap sequence by simply varying the characters.