[About Me]

Libin Liu

Chief Scientist
DeepMotion Inc.

Email: libin [at] deepmotion [dot] com

I am currently the Chief Scientist of DeepMotion Inc.. Before joining DeepMotion, I was a R&D postdoctoral associate in Disney Research, Pittsburgh. I was a postdoctoral research fellow in the Imager Laboratory at the University of British Columbia in 2015, after I recieved my Ph.D. degree in computer science in 2014 and my B.S. degree in mathematics and physics in 2009 from Tsinghua University.

I am interested in character animation, physics-based simulation, motion control, and related areas such as optimal control, reinforcement learning, and deep learning. I put a lot of work into realizing various agile human motions on simulated characters. I also have some experience in simulating deformable objects and soft characters.


Learning Basketball Dribbling Skills Using Trajectory Optimization and Deep Reinforcement Learning

Libin Liu, Jessica K. Hodgins

We present a method based on trajectory optimization and deep reinforcement learning for learning robust controllers for various basketball dribbling skills, such as dribbling between the legs, running, and crossovers.

ACM Transactions on Graphics, Vol 37 Issue 4, Article 142 (SIGGRAPH 2018). (to appear)

Learning to Schedule Control Fragments for Physics-Based Characters Using Deep Q-Learning

Libin Liu, Jessica K. Hodgins

We present a deep Q-learning based method for learning a scheduling scheme that reorders short control fragments as necessary at runtime to achieve robust control of challenging skills such as skateboarding.

ACM Transactions on Graphics, Vol 36 Issue 3, Article 29. (presented at SIGGRAPH 2017)

Guided Learning of Control Graphs for Physics-Based Characters

Libin Liu, Michiel van de Panne, KangKang Yin,

We present a method for learning robust control graphs that support real-time physics-based simulation of multiple characters, each capable of a diverse range of movement skills.

ACM Transactions on Graphics, Vol 35, Issue 2, Article 29. (presented at SIGGRAPH 2016)

Learning Reduced-Order Feedback Policies for Motion Skills

Kai Ding, Libin Liu, Michiel van de Panne, KangKang Yin,

Proc. ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2015 (SCA Best Paper Award)

Deformation Capture and Modeling of Soft Objects

Bin Wang, Longhua Wu, KangKang Yin, Libin Liu, Uri Ascher, Hui Huang.

ACM Transactions on Graphics, Vol 34, Issue 4, Article 94 (SIGGRAPH 2015)

Improving Sampling-based Motion Control

Libin Liu, KangKang Yin, Baining Guo.

We address several limitations of the sampling-based motion control method. A variety of highly agile motions, ranging from stylized walking and dancing to gymnastic and Martial Arts routines, can be easily reconstructed now.

Computer Graphics Forum 34(2) (Eurographics 2015).

Simulation and Control of Skeleton-driven Soft Body Characters

Libin Liu, KangKang Yin, Bin Wang, Baining Guo.

We present a physics-based framework for simulation and control of human-like skeleton-driven soft body characters. We propose a novel pose-based plasticity model to achieve large skin deformation around joints. We further reconstruct controls from reference trajectories captured from human subjects by augmenting a sampling-based algorithm.

ACM Transactions on Graphics, Vol 32, Issue 6, Article 215 (SIGGRAPH Asia 2013)

Terrain Runner: Control, Parameterization, Composition, and Planning for Highly Dynamic Motions

Libin Liu, KangKang Yin, Michiel van de Panne, Baining Guo.

We present methods for the control, parameterization, composition, and planning for highly dynamic motions. More specifically, we learn the skills required by real-time physics-based avatars to perform parkour-style fast terrain crossing using a mix of running, jumping, speed-vaulting, and drop-rolling.

ACM Transactions on Graphics, Vol 31, Issue 6, Article 154 (SIGGRAPH Asia 2012)

Sampling-based Contact-rich Motion Control

Libin Liu, KangKang Yin, Michiel van de Panne, Tianjia Shao Weiwei Xu.

Given a motion capture trajectory, we propose to extract its control by randomized sampling.

ACM Transactions on Graphics, Vol 29, Issue 4, Article 128 (SIGGRAPH 2010)

[Professional Activities]
Program Committee:
  • SIGGRAPH 2019
  • Pacific Graphics 2018
  • SIGGRAPH Asia 2014 Posters and Technical Briefs
  • ACM SIGGRAPH/Eurographics Symposium on Computer Animation 2015-2018
  • Motion In Games 2014, 2016-2018
Paper Reviewing:
  • ACM Transactions on Graphics (TOG)
  • IEEE Transactions on Visualization and Computer Graphics (TVCG)
  • Eurographics (Eupopean Association for Computer Graphics)
  • Computer Graphics Forum
  • IEEE International Conference on Robotics and Automation (ICRA)
  • ACM SIGGRAPH/Eurographics Symposium on Computer Animation
  • Motion In Games
  • CASA (Computer Animation and Social Agents)
  • Computers & Graphics
  • Graphical Models

DeepMotion Inc.

Chief Scientist May 2017 to present

Disney Research, Pittsburgh

R&D Postdoctoral Associate Aug. 2015 to Apr. 2017

The University of British Columbia

Postdoctoral Research Fellow in Imager Laboratory Aug. 2014 to Jul. 2015

Microsoft Research Asia

Research intern in Internet Graphics Group Aug. 2011 to Jul. 2014
Sep. 2008 to Jul. 2010

National University of Singapore

Research assistant in School of Computing Sep. 2010 to Jan. 2011