Hao (Richard) Zhang
Most Recent Affiliation(s):
- Simon Fraser University, Tsinghua University, BNRist and School of Software
Bio:
SIGGRAPH 2010
Hao (Richard) Zhang is an Associate Professor in the School of Computing Science at Simon Fraser University, Canada, and he co-directs the Graphics, Usability, and Visualization (GrUVi) Lab. He received his Ph.D. from the University of Toronto in 2003 and M. Math. and B. Math. degrees from the University of Waterloo. His research interests include geometry processing, shape analysis, and computer graphics. He gave a Eurographics State-of-the- art report on spectral methods for mesh processing and analysis in 2007 and wrote the first comprehensive survey on this topic. Recently, he has served on the program committees of Eurographics, ACM/EG SGP, ACM/SIAM GPM, and IEEE SMI. He was a winner of the Best Paper Award from SGP 2008.
Learning Category: Jury Member:
Learning Category: Presentation(s):
![Zero-shot Image-to-image Translation](https://history.siggraph.org/wp-content/uploads/2024/02/2023_Technical-Paper_Parmar_Zero-shot-Image-to-Image-Translation-150x150.jpg)
Type: [Technical Papers]
Zero-shot Image-to-image Translation Presenter(s): [Parmar] [Singh] [Zhang] [Li] [Lu] [Zhu]
[SIGGRAPH 2023]
![Graph2Plan: Learning Floorplan Generation From Layout Graphs](https://history.siggraph.org/wp-content/uploads/2023/02/2020-Technical-Papers-Hu_Graph2Plan-150x150.jpg)
Type: [Technical Papers]
Graph2Plan: Learning Floorplan Generation From Layout Graphs Presenter(s): [Hu] [Huang] [Tang] [Kaick] [Zhang] [Huang]
[SIGGRAPH 2020]
![TilinGNN: Learning to Tile With Self-supervised Graph Neural Network](https://history.siggraph.org/wp-content/uploads/2023/02/2020-Technical-Papers-Xu_TilinGNN-150x150.jpg)
Type: [Technical Papers]
TilinGNN: Learning to Tile With Self-supervised Graph Neural Network Presenter(s): [Xu] [Hui] [Fu] [Zhang]
[SIGGRAPH 2020]
![DSCarver: decompose-and-spiral-carve for subtractive manufacturing](https://history.siggraph.org/wp-content/uploads/2023/02/2018-Technical-Papers-Zhao_DSCarver_-Decompose-and-Spiral-Carve-for-Subtractive-Manufacturing-150x150.jpg)
Type: [Technical Papers]
DSCarver: decompose-and-spiral-carve for subtractive manufacturing Presenter(s): [Zhang] [Zhang] [Xin] [Deng] [Tu] [Wang] [Cohen-Or]
Entry No.: [137]
[SIGGRAPH 2018]
![P2P-NET: bidirectional point displacement net for shape transform](https://history.siggraph.org/wp-content/uploads/2023/01/2018-Technical-Papers-Yin_P2P-NET-150x150.jpg)
Type: [Technical Papers]
P2P-NET: bidirectional point displacement net for shape transform Presenter(s): [Yin] [Huang] [Cohen-Or] [Zhang]
Entry No.: [152]
[SIGGRAPH 2018]
![Predictive and generative neural networks for object functionality](https://history.siggraph.org/wp-content/uploads/2023/02/2018-Technical-Papers-Hu_Predictive-and-Generative-Neural-Networks-for-Object-Functionality-150x150.jpg)
Type: [Technical Papers]
Predictive and generative neural networks for object functionality Presenter(s): [Hu] [Yan] [Zhang] [Kaick] [Shamir] [Zhang] [Huang]
Entry No.: [151]
[SIGGRAPH 2018]
![Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines](https://history.siggraph.org/wp-content/uploads/2022/07/2018-SIGGRAPH-Image-Not-Available-150x150.jpg)
Type: [Technical Papers]
Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines Presenter(s): [Yu] [Zhang] [Zhang] [Mahdavi-Amiri] [Zhang]
[SIGGRAPH 2018]
![GRASS: generative recursive autoencoders for shape structures](https://history.siggraph.org/wp-content/uploads/2023/02/2017-Technical-Papers-Li_GRASS_-Generative-Recursive-Autoencoders-for-Shape-Structures-150x150.jpg)
Type: [Technical Papers]
GRASS: generative recursive autoencoders for shape structures Presenter(s): [Liu] [Xu] [Chaudhuri] [Yumer] [Zhang] [Guibas]
[SIGGRAPH 2017]
![Interaction context (ICON): towards a geometric functionality descriptor](https://history.siggraph.org/wp-content/uploads/2023/04/2015-Technical-Papers-Hu_Interaction-Context-ICON-Towards-a-Geometric-Functionality-Descriptor-150x150.jpg)
Type: [Technical Papers]
Interaction context (ICON): towards a geometric functionality descriptor Presenter(s): [Hu] [Zhu] [Kaick] [Liu] [Shamir] [Zhang]
[SIGGRAPH 2015]
![Structure-Aware Shape Processing](https://history.siggraph.org/wp-content/uploads/2022/01/2014-13-Structure-Aware-Shape-Processing-150x150.jpg)
Type: [Courses]
Structure-Aware Shape Processing Organizer(s): [Mitra]
Presenter(s): [Mitra] [Wand] [Zhang] [Cohen-Or] [Kim] [Huang]
Entry No.: [13]
[SIGGRAPH 2014]
![Topology-varying 3D shape creation via structural blending](https://history.siggraph.org/wp-content/uploads/2023/02/2014-Technical-Papers-Alhashim_Topology-Varying-3D-Shape-Creation-via-Structural-Blending-150x150.jpg)
Type: [Technical Papers]
Topology-varying 3D shape creation via structural blending Presenter(s): [Alhashim] [Li] [Xu] [Cao] [Ma] [Zhang]
[SIGGRAPH 2014]
![Fit and diverse: set evolution for inspiring 3D shape galleries](https://history.siggraph.org/wp-content/uploads/2023/03/2012-Technical-Papers-Xu_Fit-and-Diverse-Set-Evolution-for-Inspiring-3D-Shape-Galleries-150x150.jpg)
Type: [Technical Papers]
Fit and diverse: set evolution for inspiring 3D shape galleries Presenter(s): [Xu] [Zhang] [Cohen-Or] [Chen]
[SIGGRAPH 2012]
Learning Category: Moderator:
![A deep learning framework for character motion synthesis and editing](https://history.siggraph.org/wp-content/uploads/2023/02/2016-Technical-Papers-Holden_A-Deep-Learning-Framework-for-Character-Motion-Synthesis-and-Editing-150x150.jpg)
Type: [Technical Papers]
A deep learning framework for character motion synthesis and editing Presenter(s): [Holden] [Saito] [Komura]
[SIGGRAPH 2016]
![Modeling dense inflorescences](https://history.siggraph.org/wp-content/uploads/2023/02/2016-Technical-Papers-Owens_Modeling-Dense-Inflorescences-150x150.jpg)
Type: [Technical Papers]
Modeling dense inflorescences Presenter(s): [Owens] [Cieslak] [Hart] [Classen-Bockhoff] [Prusinkiewicz]
[SIGGRAPH 2016]