182 citations · 195 across the 8 of their papers we have counts for
9 papers · 1 filter
GaussianHeads: End-to-End Learning of Drivable Gaussian Head Avatars from Coarse-to-fine Representations
Kartik Teotia, Hyeongwoo Kim, Pablo Garrido +3
Real-time rendering of human head avatars is a cornerstone of many computer graphics applications, such as augmented reality, video games, and films, to name a few. Recent approach…
Implicit Neural Head Synthesis via Controllable Local Deformation Fields
Chuhan Chen, Matthew O'Toole, Gaurav Bharaj +1
High-quality reconstruction of controllable 3D head avatars from 2D videos is highly desirable for virtual human applications in movies, games, and telepresence. Neural implicit fi…
Few-shot Geometry-Aware Keypoint Localization
Xingzhe He, Gaurav Bharaj, David Ferman +2
Supervised keypoint localization methods rely on large manually labeled image datasets, where objects can deform, articulate, or occlude. However, creating such large keypoint labe…
HQ3DAvatar: High Quality Controllable 3D Head Avatar
Kartik Teotia, Mallikarjun B R, Xingang Pan +4
Multi-view volumetric rendering techniques have recently shown great potential in modeling and synthesizing high-quality head avatars. A common approach to capture full head dynami…
FML: Face Model Learning from Videos
Ayush Tewari, Florian Bernard, Pablo Garrido +6
Monocular image-based 3D reconstruction of faces is a long-standing problem in computer vision. Since image data is a 2D projection of a 3D face, the resulting depth ambiguity make…
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…
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…
Automatic Face Reenactment
Pablo Garrido, Levi Valgaerts, Ole Rehmsen +3
We propose an image-based, facial reenactment system that replaces the face of an actor in an existing target video with the face of a user from a source video, while preserving th…