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Ondřej Texler

Research Scientist

  NEON         CTU in Prague

  ondrej.texler@gmail.com

Short Bio

I am a senior research scientist at NEON, Samsung Research America. I obtained my PhD at CTU in Prague under the supervision of prof. Daniel Sýkora. I received my BSc and MSc degrees from Computer Science at the same university. My primary research interest lies in computer graphics, image processing, computer vision, and deep learning; I specialize in generating realistically looking images according to certain conditions or examples. In recent years, I worked with Adobe Research on several publications and research-engineering projects, and I was granted the opportunity to join them as a research scientist intern in San Jose, California (2017) and then in Seattle, Washington (2018). After that, I joined Snap Inc. as a research scientist intern in Santa Monica, Los Angeles, California (2019). For 2020, I joined Samsung Research America, NEON team as a research scientist intern.

Research Interests

  • Computer Graphics
  • Generative Networks
  • Computer Vision
  • Deep Learning & AI

News

  • [03/2021]: I moved to California, and started a full-time position at NEON, Samsung Research America!
  • [02/2021]: Our paper FaceBlit: Instant Real-time Example-based Style Transfer to Facial Videos has been accepted to i3D 2021.
  • [11/2020]: I gave a talk for BBC News Arabic regarding our latest research.
  • [10/2020]: Our paper StyleProp: Real-time Example-based Stylization of 3D Models has been accepted to PacificGraphics 2020, Wellington, New Zealand.
  • [8/2020]: We presented our paper Interactive Video Stylization Using Few-Shot Patch-Based Training at SIGGRAPH2020 full paper session, as a short oral at ECCV2020 Deep Internal Learning workshop, and at Siggraph RealTime Live, where we have won the Best in Show Award!
  • [6/2020]: Recently, I defended "a proposal of a dissertation thesis" and passed a Doctoral State Exam. I plan to finish my PhD in early 2021.
  • [5/2020]: Our paper Interactive Video Stylization Using Few-Shot Patch-Based Training has been accepted to SIGGRAPH 2020.
  • [4/2020]: For the rest of the year 2020, I am joining Samsung Research America, the NEON team!
  • [12/2019]: Our paper Arbitrary Style Transfer Using Neurally-Guided Patch-Based Synthesis has been accepted to Computers & Graphics journal.
  • [4/2019]: For Summer/Fall 2019 I will join Snap Research at Santa Monica, Los Angeles, CA.
  • [4/2019]: Our paper Stylizing Video by Example has been accepted to SIGGRAPH 2019, Los Angeles, CA.
  • [4/2019]: On 5-May I am presenting Enhancing Neural Style Transfer using Patch-Based Synthesis at Expressive 2019, Genoa, Italy.
  • [3/2019]: On 6-May I am presenting Fast Example-Based Stylization with Local Guidance at EuroGraphics 2019, Genoa, Italy.

Publications

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FaceBlit: Instant Real-time Example-based Style Transfer to Facial Videos

A. Texler, O. Texler, M. Kučera, M. Chai, and D. Sýkora

In Proceedings of the ACM in Computer Graphics and Interactive Techniques, 4(1), 2021 (I3D 2021)

Project Page Paper Supplementary Video BibTeX
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StyleProp: Real-time Example-based Stylization of 3D Models

F. Hauptfleisch, O. Texler, A. Texler, J. Křivánek, and D. Sýkora

In Computer Graphics Forum 39(7):575-586  (PacificGraphics 2020)

Project Page Paper Supplementary Video BibTeX
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Interactive Video Stylization Using Few-Shot Patch-Based Training

O. Texler, D. Futschik, M. Kučera, O. Jamriška, Š. Sochorová, M. Chai, S. Tulyakov, and D. Sýkora

In ACM Transactions on Graphics 39(4):73  (SIGGRAPH 2020), Best in Show Award at SIGGRAPH Real-Time Live!

Project Page Paper SIGGRAPH Talk GitHub Supplementary Presentation BibTeX
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Arbitrary Style Transfer Using Neurally-Guided Patch-Based Synthesis

O. Texler, D. Futschik, J. Fišer, M. Lukáč, J. Lu, E. Shechtman, and D. Sýkora

In Computers & Graphics 87:62-71  (January 2020)

Project Page Paper GitHub BibTeX
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Stylizing Video by Example

O. Jamriška, Š. Sochorová, O. Texler, M. Lukáč, J. Fišer, J. Lu, E. Shechtman, and D. Sýkora

In ACM Transactions on Graphics 38(4):107  (SIGGRAPH 2019, Los Angeles, California, July 2019)

Project Page Paper Supplementary Demo BibTeX
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Enhancing Neural Style Transfer using Patch-Based Synthesis

O. Texler, J. Fišer, M. Lukáč, J. Lu, E. Shechtman, and D. Sýkora

In Proceedings of the 8th ACM/EG Expressive Symposium, pp. 43-50  (Expressive 2019, Genoa, Italy, May 2019)

Project Page Paper GitHub Interactive Supplementary Presentation BibTeX
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StyleBlit: Fast Example-Based Stylization with Local Guidance

D. Sýkora, O. Jamriška, O. Texler, J. Fišer, M. Lukáč, J. Lu and E. Shechtman

In Computer Graphics Forum 38(2):83-91  (Eurographics 2019, Genoa, Italy, May 2019)

Project Page Paper GitHub Supplementary Presentation Unity3D Asset BibTeX
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Example-Based Stylization of Navigation Maps on Mobile Devices

O. Texler and D. Sýkora

In Proceedings of the 22nd Central European Seminar on Computer Graphics.  (CESCG 2018, Smolenice, Slovakia, 2018)

Paper Presentation

Supervised Thesis

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Example-Based Stylization on Mobile Devices

A. Moravcová, O. Texler

Master's Thesis, CTU in Prague

Master's Thesis

Education

  • 2018 ‒ 2021
    Doctoral degree study (PhD)

    Computer Graphics,
    FEE, CTU in Prague.

    Dissertation Thesis: Example-based Style Transfer.

  • 2016 ‒ 2018
    Master degree study (MSc)

    Computer Science,
    FIT, CTU in Prague.

    Master Thesis: Digital Image Processing and Image Stylization.

  • 2012 ‒ 2015
    Bachelor degree study (BSc)

    Computer Science,
    FIT, CTU in Prague.

    Bachelor Thesis: Architecture design and implementation of a large software system.

  • 2004 ‒ 2012
    High school

    Mathematics, Physics, and Descriptive Geometry specialization, Gymnasium of Christian Doppler.

Professional Experience

present
Samsung Research America, California

Research & Development. Research and implementation of computer vision and deep learning techniques related to creating a virtual artificial human. Part of the NEON.life team.

Samsung Research America, California

Research & Development. Research and implementation of various image-to-image and video-to-video translation neural networks. Part of the NEON.life team.

Snap Inc., Los Angeles, California

Research & Development. Research of new techniques on training generative adversarial networks for style transfer tasks; focused on a scenario where a minimal amount of data is available, and an interactive response is required. Furthermore, developing a shader-based real-time stylization for human portraits.

Adobe Research, USA

Research & Development. Remote collaboration on several research projects, publications, and tech transfer project. Computer graphics; patch-based style transfer; neural-network-based style transfer.

Adobe Research, Seattle, Washington

Research & Development. Combining neural-network-based and patch-based style transfer methods. Chunk-based style transfer method with focus on a real-time performance.

Adobe Research, San Jose, California

Research & Development. Guiding patch-based style transfer method using convolutional neural networks, image harmonization, and histogram optimization. Integrating developed style transfer method into Adobe Photoshop.

Dynavix, Prague, Czechia

Software Architecture & Development. The navigation application for smartphones, tablets, and PND devices. C++, Java (Android), JavaEE, Objective-C (iOS), C#.

World of Warcraft game server, Prague, Czechia

Software & Database Development. The World of Warcraft game server. Extending game mechanics, scripting artificial intelligence, data-mining. C++, C#.

2013