World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
4655
World Ranking
13133
National Ranking
5275

Overview

Sean Ryan Fanello is affiliated with Google in the United States and has contributed extensively to the fields of computer science and engineering. Their research primarily focuses on computer vision and pattern recognition, with additional work in computer graphics and computer-aided design, computational mechanics, control and systems engineering, and media technology.

The scientist's main topics of study include advanced vision and imaging, computer graphics and visualization techniques, 3D shape modeling and analysis, image enhancement techniques, generative adversarial networks and image synthesis, human pose and action recognition, and face recognition and analysis.

Sean Ryan Fanello has published in a variety of venues, with frequent contributions to arXiv (Cornell University), ACM Transactions on Graphics, the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Computer Graphics Forum, and GetMobile Mobile Computing and Communications. These venues reflect the scientist's focus on graphics, vision, and computational methods.

Recent papers authored or co-authored by Sean Ryan Fanello include the following:

  • Total relighting, 2021, ACM Transactions on Graphics
  • Light stage super-resolution, 2020, ACM Transactions on Graphics
  • Multiresolution Deep Implicit Functions for 3D Shape Representation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • State of the Art on Neural Rendering, 2020, Computer Graphics Forum
  • Neural rendering in a room, 2022, ACM Transactions on Graphics

The scientist frequently collaborates with peers in related research, including Yinda Zhang, Feitong Tan, Rohit Pandey, Sofien Bouaziz, and Qiangeng Xu.

Best Publications

  • Holoportation: Virtual 3D Teleportation in Real-time

    Sergio Orts-Escolano;Christoph Rhemann;Sean Fanello;Wayne Chang

  • Fusion4D: real-time performance capture of challenging scenes

    Mingsong Dou;Sameh Khamis;Yury Degtyarev;Philip Davidson

  • StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction

    Sameh Khamis;Sean Ryan Fanello;Christoph Rhemann;Adarsh Kowdle

  • State of the Art on Neural Rendering

    Ayush Tewari;Ohad Fried;Justus Thies;Vincent Sitzmann

  • The relightables: volumetric performance capture of humans with realistic relighting

    Kaiwen Guo;Peter Lincoln;Philip Davidson;Jay Busch

  • In-air gestures around unmodified mobile devices

    Jie Song;Gábor Sörös;Fabrizio Pece;Sean Ryan Fanello

  • HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching

    Vladimir Tankovich;Christian Hane;Yinda Zhang;Adarsh Kowdle

  • Motion2fusion: real-time volumetric performance capture

    Mingsong Dou;Philip Davidson;Sean Ryan Fanello;Sameh Khamis

  • HyperDepth: Learning Depth from Structured Light without Matching

    Sean Ryan Fanello;Christoph Rhemann;Vladimir Tankovich;Adarsh Kowdle

  • LookinGood: enhancing performance capture with real-time neural re-rendering

    Ricardo Martin-Brualla;Rohit Pandey;Shuoran Yang;Pavel Pidlypenskyi

  • Keep it simple and sparse: real-time action recognition

    Sean Ryan Fanello;Ilaria Gori;Giorgio Metta;Francesca Odone

  • Total relighting: learning to relight portraits for background replacement

    Rohit Pandey;Sergio Orts Escolano;Chloe Legendre;Christian Häne

  • ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems

    Yinda Zhang;Yinda Zhang;Sameh Khamis;Christoph Rhemann;Julien P. C. Valentin

  • Depth from motion for smartphone AR

    Julien Valentin;Adarsh Kowdle;Jonathan T. Barron;Neal Wadhwa

  • Learning to be a depth camera for close-range human capture and interaction

    Sean Ryan Fanello;Cem Keskin;Shahram Izadi;Pushmeet Kohli

  • FlexSense: a transparent self-sensing deformable surface

    Christian Rendl;David Kim;Sean Fanello;Patrick Parzer

  • Deep reflectance fields: high-quality facial reflectance field inference from color gradient illumination

    Abhimitra Meka;Christian Häne;Rohit Pandey;Michael Zollhöfer

  • UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems

    Sean Ryan Fanello;Julien Valentin;Christoph Rhemann;Adarsh Kowdle

  • Filter Forests for Learning Data-Dependent Convolutional Kernels

    Sean Ryan Fanello;Cem Keskin;Pushmeet Kohli;Shahram Izadi

  • Total relighting

    Unknown

  • Advances in neural rendering

    A. Tewari;O. Fried;J. Thies;V. Sitzmann

  • Deep relightable textures: volumetric performance capture with neural rendering

    Abhimitra Meka;Rohit Pandey;Christian Häne;Sergio Orts-Escolano

  • HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching

    Vladimir Tankovich;Christian Häne;Sean Fanello;Yinda Zhang

  • ActiveStereoNet: Unsupervised End-to-End Learning for Active Stereo Systems

    Yinda Zhang;Sameh Khamis;Christoph Rhemann;Julien Valentin

Frequent Co-Authors

Shahram Izadi
Shahram Izadi Google (United States)
Christoph Rhemann
Christoph Rhemann Google (United States)
Yinda Zhang
Yinda Zhang Google (United States)
Giorgio Metta
Giorgio Metta Italian Institute of Technology
Paul Debevec
Paul Debevec Google (United States)
David Kim
David Kim Microsoft (United States)
Andrea Tagliasacchi
Andrea Tagliasacchi Simon Fraser University
Thomas Funkhouser
Thomas Funkhouser Google (United States)
Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Christian Theobalt
Christian Theobalt Max Planck Institute for Informatics

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