I am an Associate Research Scientist at DisneyResearch|Studios, Zürich, Switzerland. Prior to joining Disney as an employee, I was a joint PhD student at Computer Graphics Lab, ETH Zürich and DisneyResearch|Studios, advised by Prof. Markus Gross and Dr. Marios Papas. I am interested in rendering algorithms with path tracing as well as the application of machine learning techniques to improve rendering efficiency.

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(Publications at the bottom of the page :)

Education

2019.08 - 2024.03

ETH Zürich and DisneyResearch|Studios, Zürich, Switzerland

Joint PhD student in Computer Science

2017.09 - 2019.07

ETH Zürich, Zürich, Switzerland

MSc in Computer Science (with distinction)

2013.09 - 2017.07

Peking University, Beijing, China

BSc in Intelligence Science and Technology (with distinction)

Skills

  • Programming - proficient: Python, C/C++, MATLAB
  • Programming - familiar: LaTeX, HTML, JavaScript, CUDA C/C++
  • Toolboxes and libraries: TensorFlow, Dash/Plotly, Pandas, OpenCV, Torch, CGAL
  • Languages: English (professional), Mandarin Chinese (native), German (intermediate)

Professional Experience

2024.04 - present
2024.04 - present

Associate Research Scientist

DisneyResearch|Studios (Zürich)
  • Research interest: physically based rendering, deep learning, image processing
2018.04 - 2018.09
2018.04 - 2018.09

Research Intern

Disney Research (Zürich)
  • Implementation of deep-learning-based denoising algorithm of Monte Carlo renders
  • Generation of datasets
  • Training and testing different network structures

Academic Experience

2019.08 - 2024.03
2019.08 - 2024.03

Doctoral Research Assistant

@ Computer Graphics Lab, ETH Zürich and DisneyResearch|Studios
Supervisor: Prof. Dr. Markus Gross, Dr. Marios Papas
Project: (Dissertation) Denoising Monte Carlo Renderings: a Sub-Pixel Exploration with Deep Learning
  • More effective deep learning techniques for denoising Monte Carlo renderings
  • Using sub-pixel information and techniques including decomposition, auxiliary features and depth separation
2018.11 - 2019.05
2018.11 - 2019.05

Master Thesis

@ Computer Graphics Lab, ETH Zürich
Supervisor: Dr. Marios Papas, Thomas Müller, Thijs Vogels
Project: Denoising Radiance Samples in Scene Space with Order-Independent Neural Networks
  • Design and Implementation of an algorithm to filter raw radiance samples in scene space using neural networks
  • Exploration of appropriate network architecture
2016.11 - 2017.06
2016.11 - 2017.06

Bachelor Thesis

@ Key Laboratory of Machine Perception (MoE), Peking University
Supervisor: Prof. Zhouchen Lin
Project: Fast Multiple Affine Template Matching
  • Implementation of an algorithm to locate multiple matches of a template in a single target image
  • Combination of several previous methods to reach high accuracy and speed
2015.12 - 2016.11
2015.12 - 2016.11

Research Assistant

@ National Engineering Laboratory for Video Technology, Peking University
Supervisor: Prof. Yizhou Wang
Project: Video Inpainting with GAN
  • Application of generative adversarial networks to video inpainting, achieving plausible results
  • Collection of training and testing clips from news program

Teaching Experience

Fall 2018/19/20/22
Fall 2018/19/20/22

Computer Graphics

Teaching Assistant @ Department of Computer Science, ETH Zürich
Instructor(s): Dr. Marios Papas, Prof. Markus Gross
  • Master-level course of computer graphics, covering a spectrum of areas in CG with a focus on physically-based rendering
  • Head TA for 2019-2022, responsible for general organization of the course
Spring 2020/21/22/23
Spring 2020/21/22/23

Scientific Visualization

Teaching Assistant @ Department of Computer Science, ETH Zürich
Instructor(s): Prof. Tobias Günther, Prof. Markus Gross
  • Master-level course of scientific visualization, introducing techniques of visualization of scientific and abstract data

Publications

Xianyao Zhang, Gerhard Röthlin, Marco Manzi, Markus Gross, Marios Papas
Eurographics Symposium on Rendering 2023 (EGSR2023), Industry Track

Deep Compositional Denoising on Frame Sequences

  • Extending deep compositional denoising to produce high-quality and temporally stable frame sequences
  • Sharing the per-frame decomposition results by applying motion compensation between decomposition and denoising
  • Reducing RAM consumption by 8-bit quantization of intermediate tensors
  • Denoising
  • learned decomposition
  • temporal reuse
  • quantization
Shilin Zhu, Xianyao Zhang, Gerhard Röthlin, Marios Papas, Mark Meyer
SIGGRAPH 2023 Talks

Denoising Production Volumetric Rendering

  • Using volume-specific features to enhance volume denoising in production
  • Applying feature selection to detect the best feature set
  • Denoising
  • feature selection
  • volume rendering
  • production
Xianyao Zhang, Melvin Ott, Marco Manzi, Markus Gross, Marios Papas
Computer Graphics Forum 41(4)
Proceedings of Eurographics Symposium on Rendering 2022 (EGSR2022)

Automatic Feature Selection for Denoising Volumetric Renderings

  • Selecting most impactful physically-based auxiliary features (scattering coefficient, volume albedo, density gradient etc.) for denoising volumetric renderings
  • Training with feature dropout to enable efficient evaluation of impacts of different feature sets
  • Improved denoising quality for different network architectures
  • Denoising
  • feature selection
  • volume rendering
Xianyao Zhang, Marco Manzi, Thijs Vogels, Henrik Dahlberg, Markus Gross, Marios Papas
Computer Graphics Forum 40(4)
Proceedings of Eurographics Symposium on Rendering 2021 (EGSR2021)

Deep Compositional Denoising for High-quality Monte Carlo Rendering

  • Learning an image-space decomposition of the noisy image to facilitate kernel-predicting denoising
  • Improved denoising quality on datasets rendered with both academic and production renderers
  • Possible combination with user-defined decompositions (e.g. diffuse-specular, direct-indirect)
  • Denoising
  • kernel prediction
  • learned decomposition