My research interests span physics-based simulations and perceptual evaluation as well as data-driven approaches with machine learning. I have been investigating into more effective ways to simulate natural phenomena via utilization of refined data and to acquire reliable data for machine learning. Moreover, I have been studying human visual perception of simulations for not only computer graphics but also engineering applications while aiming for new evaluation approaches and better understanding of a variety of numerical methods.
I am working as a postdoc in Nils Thuerey’s group at the Technical University of Munich (TUM), Germany. Before joining the TUM, I worked as a postdoc at Korea University from which I earned my Ph.D. in computer science and engineering under JungHyun Han’s supervision.
E-mail: kiwon.um (at) tum (dot) de
- Marie-Lena Eckert, Kiwon Um, and Nils Thuerey, “ScalarFlow: A Large-Scale Volumetric Data Set of Real-world Scalar Transport Flows for Computer Animation and Machine Learning,” ACM Transactions on Graphics (the 12th SIGGRAPH Asia Conference, November 17-20, 2019, Brisbane, Australia). (conditionally accepted) [Project]
- Kiwon Um, Xiangyu Hu, Bing Wang, and Nils Thuerey, “Spot the Difference: Accuracy of Numerical Simulations via the Human Visual System,” arXiv 2019. [Project]
- Kiwon Um, Xiangyu Hu, and Nils Thuerey, “Liquid Splash Modeling with Neural Networks,” Computer Graphics Forum (the 17th annual Symposium on Computer Animation, July 11-13, 2018, Paris, France), Vol. 37, No. 8, pp. 171-182. [Project]
- Kiwon Um, Xiangyu Hu, and Nils Thuerey, “Perceptual Evaluation of Liquid Simulation Methods,” ACM Transactions on Graphics (the 44th SIGGRAPH Conference, July 30-August 3, 2017, Los Angeles, USA), Vol. 36, Issue 4, Article 143. [Project]
- Kiwon Um, Seungho Baek, and JungHyun Han, “Correspondence-based Fluid Control with a Hybrid Particle-Grid Simulation Method,” Proceedings of the 30th International Conference on Computer Animation and Social Agents, May 22-24, 2017, Seoul, Korea. (short paper)
- Kiwon Um, Xiangyu Hu, and Nils Thuerey, “Analysis of Free Surface Simulation using Breaking-dam Benchmark,” Proceedings of the 11th International SPHERIC Workshop, June 13–16, 2016, Munich, Germany, pp. 410-415.
- Seungho Baek, Kiwon Um, and JungHyun Han, “Muddy Water Animation with Different Details,” Computer Animation and Virtual Worlds (the 28th International Conference on Computer Animation and Social Agents, May 11-13, 2015, Singapore), Vol. 26, No. 3-4, pp. 347-355.
- Kiwon Um, Seungho Baek, and JungHyun Han, “Advanced Hybrid Particle-Grid Method with Sub-Grid Particle Correction,” Computer Graphics Forum (the 22nd Pacific Graphics, October 8-10, 2014, Seoul, Korea), Vol. 33, No. 7, pp. 209-218. [Project]
- Wonbae Yoon, Namil Lee, Kiwon Um, and JungHyun Han, “Computer-generated Iron Filing Art,” The Visual Computer (the 31st Computer Graphics International, June 10-13, 2014, Sydney, Australia), Vol. 30, No. 6-8, pp. 889-895.
- Kiwon Um, Tae-Yong Kim, Youngdon Kwon, and JungHyun Han, “Porous Deformable Shell Simulation with Surface Water Flow and Saturation,” Computer Animation and Virtual Worlds (the 26th International Conference on Computer Animation and Social Agents, May 16-18, 2013, Istanbul, Turkey), Vol. 24, No. 3-4, pp. 247-254. [Project]
- Kiwon Um and JungHyun Han, “Leaking Fluids,” Proceedings of International Symposium on Visual Computing, December 1-3, 2008, Las Vegas, Nevada, USA, pp. 1135-1143.
- Kiwon Um, “Fluid Simulation in Hybrid Frameworks and Porous Shell Simulation with Water,” Ph.D. Thesis, Korea University, February 2014. (English)
- Kiwon Um, “Leaking Fluids,” M.Sc. Thesis, Korea University, February 2008. (Korean)
- Summer 2019: Seminar – Deep Learning in Physics
- Winter 2018/19: Seminar – Deep Learning in Computer Graphics
- Summer 2018: Advanced Deep Learning for Physics
- Summer 2018: Seminar – Deep Learning in Computer Graphics
- Winter 2017/18: Seminar – Machine Learning in Graphics
- Summer 2017: Seminar – Machine Learning in Graphics
- Winter 2016/17: Seminar – Machine Learning in Graphics
- Summer 2016: Seminar – Recent Highlights in Graphics, Special Effects, and Visualization