Anton S. Kaplanyan
Most Recent Affiliation(s):
- Crytek GmbH and NVIDIA, Graphics Researcher
Other Affiliation(s):
- Karlsruhe Institute of Technology
- Facebook Reality Labs
Bio:
SIGGRAPH 2014
Anton S. Kaplanyan is a graphics researcher at Karlsruhe Institute of Technology (KIT), Germany. Additionally he is pursuing a Ph.D. title. His primary research and recent publications are about advanced light transport methods for global illumination. Prior to joining academia Anton had been working at Crytek for three years at various positions from senior R&D graphics engineer to lead researcher. He received his M.Sc. in Applied Mathematics at National Research University of Electronic Technology, Moscow in 2007.
SIGGRAPH 2013
Anton S. Kaplanyan is a graphics researcher at Karlsruhe Institute of Technology (KIT), Germany. Additionally he is pursuing a Ph.D. title. His primary research and recent publications are about advanced light transport methods for global illumination. Prior to joining academia Anton had been working at Crytek for three years at various positions from senior R&D graphics engineer to lead researcher. He received his M.Sc. in Applied Mathematics at National Research University of Electronic Technology, Moscow in 2007.
SIGGRAPH 2010
Anton Kaplanyan is a lead researcher of the R&D department at Crytek. During the development of CryEngine 3 he was responsible for multiple researches on graphics techniques and performance optimizations for current generation of consoles and PC. Currently he is busy working on the next iteration of the engine to keep pushing future PC and next-gen console technology. Prior to joining Crytek he received his M.S. in Computer Science at Moscow University of Electronic Engineering, Russia in early 2007.
Experience(s):
Learning Presentation(s):
Type: [Technical Papers]
Neural Supersampling for Real-time Rendering Presenter(s): [Xiao] [Nouri] [Chapman] [Fix] [Lanman] [Kaplanyan]
[SIGGRAPH 2020]
Type: [Talks (Sketches)]
DeepFovea: Neural Reconstruction for Foveated Rendering and Video Compression using Learned Natural Video Statistics Presenter(s): [Kaplanyan] [Sochenov] [Leimkühler] [Okunev] [Goodall] [Rufo]
Entry No.: [58]
[SIGGRAPH 2019]
Type: [Talks (Sketches)]
Multiple Scattering using Machine Learning Presenter(s): [Xie] [Kaplanyan] [Hunt] [Hanrahan]
Entry No.: [70]
[SIGGRAPH 2019]
Type: [Talks (Sketches)]
DeepFocus: Learned Image Synthesis for Computational Display Presenter(s): [Xiao] [Kaplanyan] [Fix] [Chapman] [Lanman]
Entry No.: [04]
[SIGGRAPH 2018]
Type: [Technical Papers]
Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder Presenter(s): [Chaitanya] [Kaplanyan] [Schied] [Salvi] [Lefohn] [Nowrouzezahrai] [Aila]
[SIGGRAPH 2017]
Type: [Talks (Sketches)]
Estimating Local Beckmann Roughness for Complex BSDFs Presenter(s): [Holzschuch] [Kaplanyan] [Hanika] [Dachsbacher]
Entry No.: [66]
[SIGGRAPH 2016]
Type: [Technical Papers]
Multiplexed metropolis light transport Presenter(s): [Hachisuka] [Kaplanyan] [Dachsbacher]
[SIGGRAPH 2014]
Type: [Courses]
Recent Advances in Light-Transport Simulation: Some Theory and a Lot of Practice Organizer(s): [Křivánek] [Keller]
Presenter(s): [Křivánek] [Keller] [Georgiev] [Kaplanyan] [Fajardo] [Meyer] [Nahmias] [Karlík] [Canada]
Entry No.: [17]
[SIGGRAPH 2014]
Type: [Technical Papers]
The natural-constraint representation of the path space for efficient light transport simulation Presenter(s): [Kaplanyan] [Hanika] [Dachsbacher]
[SIGGRAPH 2014]
Type: [Technical Papers]
Adaptive Progressive Photon Mapping Presenter(s): [Kaplanyan] [Dachsbacher]
[SIGGRAPH 2013]
Type: [Technical Papers]
Path-space manipulation of physically-based light transport Presenter(s): [Schmidt] [Novák] [Meng] [Kaplanyan] [Reiner] [Nowrouzezahrai] [Dachsbacher]
[SIGGRAPH 2013]
