63 citations · 154 across the 11 of their papers we have counts for
12 papers
VideoForensicsHQ: Detecting High-quality Manipulated Face Videos
Gereon Fox, Wentao Liu, Hyeongwoo Kim +3
There are concerns that new approaches to the synthesis of high quality face videos may be misused to manipulate videos with malicious intent. The research community therefore deve…
Neural Human Video Rendering by Learning Dynamic Textures and Rendering-to-Video Translation
Lingjie Liu, Weipeng Xu, Marc Habermann +5
Synthesizing realistic videos of humans using neural networks has been a popular alternative to the conventional graphics-based rendering pipeline due to its high efficiency. Exist…
Neural Style-Preserving Visual Dubbing
Hyeongwoo Kim, Mohamed Elgharib, Michael Zollhöfer +4
Dubbing is a technique for translating video content from one language to another. However, state-of-the-art visual dubbing techniques directly copy facial expressions from source…
EgoFace: Egocentric Face Performance Capture and Videorealistic Reenactment
Mohamed Elgharib, Mallikarjun BR, Ayush Tewari +4
Face performance capture and reenactment techniques use multiple cameras and sensors, positioned at a distance from the face or mounted on heavy wearable devices. This limits their…
Neural Rendering and Reenactment of Human Actor Videos
Lingjie Liu, Weipeng Xu, Michael Zollhoefer +5
We propose a method for generating video-realistic animations of real humans under user control. In contrast to conventional human character rendering, we do not require the availa…
Deep Video Portraits
Hyeongwoo Kim, Pablo Garrido, Ayush Tewari +7
We present a novel approach that enables photo-realistic re-animation of portrait videos using only an input video. In contrast to existing approaches that are restricted to manipu…
Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz
Ayush Tewari, Michael Zollhöfer, Pablo Garrido +4
The reconstruction of dense 3D models of face geometry and appearance from a single image is highly challenging and ill-posed. To constrain the problem, many approaches rely on str…
InverseFaceNet: Deep Monocular Inverse Face Rendering
Hyeongwoo Kim, Michael Zollhöfer, Ayush Tewari +3
We introduce InverseFaceNet, a deep convolutional inverse rendering framework for faces that jointly estimates facial pose, shape, expression, reflectance and illumination from a s…
MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
Ayush Tewari, Michael Zollhöfer, Hyeongwoo Kim +4
In this work we propose 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…
Video Depth-From-Defocus
Hyeongwoo Kim, Christian Richardt, Christian Theobalt
Many compelling video post-processing effects, in particular aesthetic focus editing and refocusing effects, are feasible if per-frame depth information is available. Existing comp…
Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras
Christian Richardt, Hyeongwoo Kim, Levi Valgaerts +1
We propose a new technique for computing dense scene flow from two handheld videos with wide camera baselines and different photometric properties due to different sensors or camer…
Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications
Tae-Hyun Oh, Yu-Wing Tai, Jean-Charles Bazin +2
Robust Principal Component Analysis (RPCA) via rank minimization is a powerful tool for recovering underlying low-rank structure of clean data corrupted with sparse noise/outliers.…