[About Me]

Libin Liu

Assistant Professor

School of Intelligence Science and Technology
Peking University

Email: libin.liu [at] pku.edu.cn

I am an assistant professor at the School of Intelligence Science and Technology, Peking University. Before joining Peking University, I was the Chief Scientist of DeepMotion Inc. I was a postdoctoral research fellow at Disney Research and the University of British Columbia. I received my Ph.D. in computer science in 2014 and my B.S. degree in mathematics and physics in 2009, both from Tsinghua University.

I am interested in character animation, physics-based simulation, motion control, and related areas such as optimal control, reinforcement learning, deep learning, and robotics. I put a lot of work into realizing various agile human motions on simulated characters and robots.

[Projects]

Neural3Points: Learning to Generate Physically Realistic Full-body Motion for Virtual Reality Users

Yongjing Ye, Libin Liu†, Lei Hu, Shihong Xia† (†: corresponding authors)

We present a method for real-time full-body tracking using three VR trackers provided by a typical VR system: one HMD (head-mounted display) and two hand-held controllers.

Computer Graphics Forum, Vol 41 Issue 8, Page 183-194 (SCA 2022).

Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors

Yiming Wang*, Qingzhe Gao*, Libin Liu†, Lingjie Liu†, Christian Theobalt, Baoquan Chen† (*: equal comtribution, †: corresponding author)

We propose a new method for learning a generalized animatable neural human representation from a sparse set of multi-view imagery of multiple persons.

arXiv 2022

Learning to Use Chopsticks in Diverse Gripping Styles

Zeshi Yang, KangKang Yin, Libin Liu

We propose a physics-based learning and control framework for using chopsticks. Robust hand controls for multiple hand morphologies and holding positions are first learned through Bayesian optimization and deep reinforcement learning. For tasks such as object relocation, the low-level controllers track collision-free trajectories synthesized by a high-level motion planner.

ACM Transactions on Graphics, Vol 41 Issue 4, Article 95 (SIGGRAPH 2022).

Camera Keyframing with Style and Control

Hongda Jiang, Marc Christie, Xi Wang, Libin Liu, Bin Wang, Baoquan Chen

We present a tool that enables artists to synthesize camera motions following a learned camera behavior while enforcing user-designed keyframes as constraints along the sequence.

ACM Transactions on Graphics, Vol 40 Issue 6, Article 209 (SIGGRAPH Asia 2021).

Learning Skeletal Articulations With Neural Blend Shapes

Peizhuo Li, Kfir Aberman, Rana Hanocka, Libin Liu, Olga Sorkine-Hornung, Baoquan Chen

We present a technique for articulating 3D characters with pre-defined skeletal structure and high-quality deformation, using neural blend shapes — corrective, pose-dependent, shapes that improve deformation quality in joint regions.

ACM Transactions on Graphics, Vol 40 Issue 4, Article 130 (SIGGRAPH 2021).

Unsupervised Co-part Segmentation through Assembly

Qingzhe Gao, Bin Wang, Libin Liu, Baoquan Chen

We propose an unsupervised learning approach for co-part segmentation from images.

Proceedings of the 38th International Conference on Machine Learning (ICML),
PMLR 139:3576-3586, 2021.

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).

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, Uri Ascher, Libin Liu, 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, 2020
  • SIGGRAPH Asia 2022
  • Pacific Graphics 2018, 2019, 2022
  • ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA) 2015-2019, 2021, 2022
  • ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG) 2014, 2016-2019, 2022
  • Eurographics Short Papers 2020, 2021
  • SIGGRAPH Asia 2014 Posters and Technical Briefs
  • CASA (Computer Animation and Social Agents) 2017
  • CAD/Graphics 2017, 2019
Paper Reviewing:
  • SIGGRAPH
  • SIGGRAPH Asia
  • 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
  • ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG)
  • CASA (Computer Animation and Social Agents)
  • Computers & Graphics
  • Graphical Models