Ayush Tewari

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.

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.

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.

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.

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.