Liu Ren, Ph.D.

Contact info: williamren at gmail dot com
Dr.Liu Ren is currently Global Head and Chief Scientist in Robert Bosch Research at Silicon Valley, CA. He is responsible for shaping strategic directions and developing cutting-edge technologies in the field of intelligent Human Machine Interaction (HMI) for Robert Bosch Corporation, a multi-national corporation with over $70 Billion revenue and more than 400,000 employees worldwide.  

As the responsible global head, Liu oversees research activities conducted by several research departments and teams located in the U.S. (Silicon Valley, Pittsburgh), Germany (Renningen), and China (Shanghai). The HMI research topics include domain-specific Artificial Intelligence (AI) such as Augmented or Virtual Reality (AR/VR), Natural Language Interaction and Processing,  Big Data Visual Analytics, Audio Analytics, Intelligent Personal Assistance, User Modeling and User Monitoring as well as traditional HMI including 3D Mapping and Rendering,  Haptics Engineering, Smart Wearables, Smart Surfaces,  and UX related methods for application areas such as autonomous driving, car infotainment and driver assistance systems (ADAS),  Internet of Things (IoT), Industry 4.0, automotive aftermarket services and solutions, security systems, , smart home and building solutions, heath care, and robotics. Many of his research results has led to good product impact (e.g., Bosch Intelligent Glove, 博世智能手套)  In particular, he led the research, development, and production efforts for Robert Bosch's 3D artMap (Chinese: 三维艺术导航地图, the world's first stylized 3D navigation map powered by non-photorealistic (artistic) rendering technologies, which has been used in many products since 2010. Since 2013, he has also led Bosch Research's R&D efforts to develop new technologies in a high profile cross-unit project to present latest HMI innovations to automotive customers for Bosch's yearly CES (Consumer Electronics Show) demonstrations in private innovation showrooms at Las Vegas. He has won the Bosch North America Inventor of the Year Award for 3D maps (2016),  Best Paper Award (2018), and Honorable Mention Award (2016) for big data visual analytics in IEEE Visualization (VAST).

Liu received his Ph.D. and M.Sc. degrees in Computer Science from Computer Science Department, Carnegie Mellon University, under the supervision of Prof.Jessica Hodgins. His Ph.D. work focused on data-driven character animation that spans the areas of computer graphics, computer vision and machine learning. He received B.Sc. and M.Sc. degrees on AI from Computer Science Department of Zhejiang University in China.

Selected Publications 


Dongyu Liu, Panpan Xu, Liu Ren, "TPFlow: Progressive Partition and Multidimensional Pattern Extraction for Large-Scale Spatio-Temporal Data Analysis", video,  

IEEE Transactions on Visualization and Computer Graphics (VAST 2018, to appear)Best Paper Award, NEW! 

Summary: A first visual anlaytics solution to handle multidimensional (>2D) spatial temporal data analysis for easy extraction and intuitive visualization of latent patterns based on a novel piecewise rank-one tensor decomposition algorithm.


Gromit Yeuk-Yin Chan, Panpan Xu, Zeng Dai, Liu Ren,
 "ViBR: Visualizing Bipartite Relations at Scale with the Minimum Description Length Principle", videoIEEE Transactions on Visualization and Computer Graphics (IEEE VAST'18, to appear), NEW!

Summary: A novel visual summarization technique for interactive analysis of large bipartite graphs based on the minimum description length (MDL) principle and locality sensitive hashing (LSH). 



Zhixin Yan, Mao Ye, and Liu Ren, "Dense Visual SLAM with Probabilistic Surfel Map", videoIEEE International Symposium on Mixed and Augmented Reality 2017 (ISMAR 2017), and selected for TVCG publication (IEEE Transactions on Visualization and Computer Graphics).

Summary: A novel map representation called Probabilistic Surfel Map (PSM) that provides globally consistent map of the environment for dense visual SLAM, leading to a drastic performance improvement (e.g, tracking errors) compared to the state of the art approach (e.g, σ-DVO) 


Alsallakh Bilal, Amin Jourabloo, Mao Ye, Xiaoming Liu, and Liu Ren, "
Do Convolutional Neural Networks Learn Class Hierarchy?"
, video, IEEE Transactions on Visualization and Computer Graphics (IEEE VAST'17)

Summary: A novel explainable AI approach that can help to improve the performance of general CNN-based classifiers with ease by leveraging visual analytics to improve CNN model structure and identify issues in the training data 


Yuanzhe Chen, Panpan Xu, and Liu Ren, "Sequence Synopsis: Optimize Visual Summary of Temporal Event Data", video, supplementary material,  IEEE Transactions on Visualization and Computer Graphics (IEEE VAST'17).

A novel event sequence summarization and visualization approach based on the minimum description length (MDL) principle addressing several key AI application areas (e.g., predictive diagnostics for connected vehicles)

Amin Jourabloo,  Xiaoming Liu,  Mao Ye, and  Liu Ren
, "Pose-Invariant Face Alignment with a Single CNN"video,  International Conference on Computer Vision (ICCV 2017).

A novel large-pose face alignment method with fast end-to-end training in a single CNN

Panpan Xu, Honghui Mei, Liu Ren,
  and Wei Chen, 
ViDX: Visual Diagnostics of Assembly Line Performance in Smart Factories”,   videoIEEE Transactions on Visualization and Computer Graphics (IEEE VAST'16),  Best Paper Honorable Mention Award.

Summary: A first visual analytics approach to addressing the needs (e.g., easy process optimization and intuitive troubleshooting for manufacturing)  of a new application domain-Industry 4.0 or Connected Industry 


ilal Alsallakh and Liu Ren
PowerSet: A Comprehensive Visualization of Set Intersections”, videoIEEE Transactions on Visualization and Computer Graphics (IEEE Info VIS 2016).

Summary: A simple and novel set visualization approach for various kinds of big data analytics applications (e.g., predictive analytics) 


Chao Du, Yen-Lin Chen, Mao Ye, and Liu Ren
Edge Snapping-Based Depth Enhancement for Dynamic Occlusion Handling in Augmented Reality”,  videoIEEE International Symposium on Mixed and Augmented Reality 2016,  (full paper for ISMAR 2016).

Summary: A near real-time approach to dealing with dynamic occlusion handling challenges with high quality visual presentations for both video see-through and optics see-through AR applications  


Benzun Wisely Babu, Soohwan Kim, Zhixin Yan, and Liu Ren
σ-DVO: Sensor Noise Model Meets Dense Visual Odometry”, videoIEEE International Symposium on Mixed and Augmented Reality 2016,  (full paper for ISMAR 2016).

Summary: A novel dense tracking approach by incorporating uncertainty modeling of depth measurements in the optimization framework of RGBD sensing for AR,  leading to a drastic 25% error reduction compared to the state-of-the art solution 



Jennifer Chandler, Lei Yang and Liu Ren"Procedural Window Lighting Effects for Real-Time City Rendering", videoACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D) 2015.

Summary: A fast and scalable approach to addressing challenges in large scale  night view rendering for big cities with applications in computer graphics and real-time geo-visualization for future infotainment systems


Mao Ye, Xianwang Wang, Ruigang Yang, Liu Ren, and Marc Pollefeys, "Accurate 3D Body Pose Estimation From a Single Depth Image", Proceedings of International Conference on Computer Vision (ICCV 2011), November, 2011 

Summary: A high accurate human pose estimation algorithm for entertainment applications that employ a depth sensor as input

Xinyu Huang, Liu Ren, and Ruigang Yang, "Image Deblurring for Less Intrusive Iris Capture",  IEEE Computer Society Coference on Computer Vision and Pattern Recognition (CVPR 2009), June, 2009

Summary: A long range and non-intrusive iris capture and recognition system featuring a novel image deblurring algorithm to handle the limitation of low cost hardware (patent granted)


Liu Ren, Alton Patrick, Alexei Efros, Jessica Hodgins, and James Rehg, "A Data-Driven Approach to Quantifying Natural Human Motion", ACM Transactions on Graphics (SIGGRAPH 2005),  August, 2005.

Summary: The first approach to automatic human animation quality evaluation


Liu Ren, Gregory Sharknarovich, Jessica Hodgins, Hanspeter Pfister and Paul Viola, "Learning Silhouette Features for Control of Human Motion", ACM Transactions on Graphics (SIGGRAPH 2004 Recommendation),  October, 2005.

Summary: A novel and low cost vision-based interface for "do-as-I-do" applications in entertainment industry



Wei Chen, Liu Ren, Matthias Zwicker and Hanspeter Pfister, "Hardware-Accelerated Adpative EWA Volume Splatting", IEEE Visualization 2004, October, 2004.

Summary: The first GPU-based approach to high quality volume splatting with EWA filtering (patent granted)



Liu Ren, Hanspeter Pfister and Matthias Zwicker, "Object Space EWA Surface Splatting: A Hardware Accelerated Approach to High Quality Point Rendering",  Computer Graphics Forum 21(3)( EUROGRAPHICS 2002, Best Paper Nominee), September, 2002.

Summary: The first GPU-based approach to high quality point-based rendering with EWA filtering (patent granted)

Selected Products 
3D artMap (Berlin, watercolor, day mode)

3D artMap (watercolor & pencil hatching style, Berlin and Stuttgart)

3D artMap is
the world's first stylized 3D navigation map powered by non-photorealistic (artistic ) rendering technologies.  3D artMap is the key feature
in Robert Bosch's first iPhone app product (successfully launched in 2010.12 for 19 european countries) and several in-car navigation products. 

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