I'm a fourth-year PhD student in Computer Graphics and Vision at University of California, San Diego. My advisor is Prof. Ravi Ramamoorthi. Prior to it, I graduated from New York University with B.A. in Computer Science and Mathematics. My undergraduate advisor was Prof. Denis Zorin and I worked on fluid simulation.
Email: a1kuznet [at] ucsd [dot] edu
My research interests lie at the intersection of physically-based rendering and machine learning. My research focuses on applying deep learning techniques to accelerate physically-based rendering or to represent complex material appearances.
Rendering specular material appearance is a core problem of computer graphics. Instead of explicit modeling and simulation of the surface microstructure (which was explored in previous work), we propose a novel direction: learning the high-frequency directional patterns from synthetic or measured examples, by training a generative adversarial network (GAN). We also introduce a novel method for partial evaluation of the generator network. The benefits of our approach include the ability to synthesize spatially large results without repetition, support for learning from measured data, and evaluation performance independent of the complexity of the dataset synthesis or measurement.Paper: pdf, video
Recently, deep learning approaches have proven successful at removing noise from Monte Carlo (MC) rendered images at extremely low sampling rates, e.g., 1-4 samples per pixel (spp). While these methods provide dramatic speedups, they operate on uniformly sampled MC rendered images. We address this issue by proposing a deep learning approach for joint adaptive sampling and reconstruction of MC rendered images with extremely low sample counts.Paper: pdf
We present a novel filter for efficient rendering of combined effects, involving soft shadows and depth of field, with global (diffuse indirect) illumination. We approximate the wedge spectrum with multiple axis-aligned filters, marrying the speed of axis-aligned filtering with an even more accurate (compact and tighter) representation than sheared filtering.Paper: pdf