Jaroslav Křivánek

Reconstructing Personalized Anatomical Models for Physics-based Body Animation

Petr Kadleček*
Charles University,
Alexandru-Eugen Ichim*
Tiantian Liu
University of
Jaroslav Křivánek
Charles University,
Ladislav Kavan
University of Utah

* Petr Kadleček and Alexandru-Eugen Ichim share the first authorship of this work.

We present a full-body reconstruction and animation system that can simulate physics-based volumetric effects such as self-collision and inertial effects. Our method uses a set of 3D surface scans to adapt an anatomically-inspired volumetric model to the user.


We present a method to create personalized anatomical models ready for physics-based animation, using only a set of 3D surface scans. We start by building a template anatomical model of an average male which supports deformations due to both 1) subject-specific variations: shapes and sizes of bones, muscles, and adipose tissues and 2) skeletal poses. Next, we capture a set of 3D scans of an actor in various poses. Our key contribution is formulating and solving a large-scale optimization problem where we compute both subject-specific and pose-dependent parameters such that our resulting anatomical model explains the captured 3D scans as closely as possible. Compared to data-driven body modeling techniques that focus only on the surface, our approach has the advantage of creating physics-based models, which provide realistic 3D geometry of the bones and muscles, and naturally supports effects such as inertia, gravity, and collisions according to Newtonian dynamics.


Petr Kadleček, Alexandru-Eugen Ichim, Tiantian Liu, Jaroslav Křivánek, Ladislav Kavan
Reconstructing Personalized Anatomical Models for Physics-based Body Animation
ACM Transaction on Graphics (Proceedings of SIGGRAPH Asia 2016), 35(6), 2016
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Our special thanks belong to Mark Pauly for the useful discussions and ideas, as well as Sanchit Garg for helping with the skeleton rig modeling. We also thank the anonymous reviewers for their valuable comments. This research was supported by NSF awards IIS-1617172, IIS-1622360, the grant SVV 2016-260332 and a gift from Activision. We thank TEN24 and their 3D Scan Store for the sets of detailed 3D scans used in our experiments.