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Ayush Tewari
Researcher at Max Planck Society
Publications - 68
Citations - 4673
Ayush Tewari is an academic researcher from Max Planck Society. The author has contributed to research in topics: Computer science & Face (geometry). The author has an hindex of 20, co-authored 49 publications receiving 2381 citations.
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Journal ArticleDOI
Deep video portraits
Hyeongwoo Kim,Pablo Garrido,Ayush Tewari,Weipeng Xu,Justus Thies,Matthias Niessner,Patrick Pérez,Christian Richardt,Michael Zollhöfer,Christian Theobalt +9 more
TL;DR: In this paper, a generative neural network with a novel space-time architecture is proposed to transfer the full 3D head position, head rotation, face expression, eye gaze, and eye blinking from a source actor to a portrait video of a target actor.
Posted Content
MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
Ayush Tewari,Michael Zollhöfer,Hyeongwoo Kim,Pablo Garrido,Florian Bernard,Patrick Pérez,Christian Theobalt +6 more
TL;DR: A novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image and can be trained end-to-end in an unsupervised manner, which renders training on very large real world data feasible.
Proceedings ArticleDOI
MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
Ayush Tewari,Michael Zollhöfer,Hyeongwoo Kim,Pablo Garrido,Florian Bernard,Patrick Pérez,Christian Theobalt +6 more
TL;DR: A novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image and can be trained end-to-end in an unsupervised manner, which renders training on very large real world data feasible.
Proceedings ArticleDOI
Self-Supervised Multi-level Face Model Learning for Monocular Reconstruction at Over 250 Hz
Ayush Tewari,Michael Zollhöfer,Pablo Garrido,Florian Bernard,Hyeongwoo Kim,Patrick Pérez,Christian Theobalt +6 more
TL;DR: This first approach that jointly learns a regressor for face shape, expression, reflectance and illumination on the basis of a concurrently learned parametric face model is presented, which compares favorably to the state-of-the-art in terms of reconstruction quality, better generalizes to real world faces, and runs at over 250 Hz.
Journal ArticleDOI
3D Morphable Face Models—Past, Present, and Future
Bernhard Egger,William A. P. Smith,Ayush Tewari,Stefanie Wuhrer,Michael Zollhoefer,Thabo Beeler,Florian Bernard,Timo Bolkart,Adam Kortylewski,Sami Romdhani,Christian Theobalt,Volker Blanz,Thomas Vetter +12 more
TL;DR: A detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed is provided in this paper, where the challenges in building and applying these models, namely, capture, modeling, image formation, and image analysis, are still active research topics, and the state-of-the-art in each of these areas are reviewed.