My research interests include physics-based computer graphics/animation, specifically in control and simulation of deformable body which has a skeleton.
I am currently Ph.D. student in Motion Computing Lab, Culture Technology from KAIST, Korea.
I received the B.S.(2005) and the M.S.(2010) degree in Mathematics from Kyoungbook National University and KAIST, respectively.
I also have a career as a math teacher at middle school and science high school.
ACM Transactions on Graphics, (Proc. SIGGRAPH) [conditionally accepted], 2017 (article)
Data driven models of human pose and soft-tissue deformations can produce very realistic results.
However, they only model the visible surface of the human body, and thus cannot create skin deformation
due to interactions with the environment.
Physical simulation generalizes to external forces but its parameters are difficult to control.
In this paper we present a layered volumetric human body model learned from data.
Our model is composed of data-driven inner layer and a physics-based external layer.
The inner layer is driven with a volumetric statistical body model (VSMPL).
The soft tissue layer consists of a tetrahedral mesh that is driven using FEM.
The combination of both layers creates coherent and realistic full-body avatars
that can be animated and generalize to external forces.
Model parameters, namely the segmentation of the body into layers and the soft tissue elasticity
are learned directly from 4D registrations of humans exhibiting soft tissue deformations,
and the learned parameters can faithfully reproduce the 4D registrations.
The resulting avatars produce realistic results for held out sequences and react to
external forces. Moreover, the model allows to retarget physical properties from an avatar to another one
as they all share the same topology.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems