no code implementations • 21 Dec 2023 • Artem Sevastopolsky, Philip-William Grassal, Simon Giebenhain, ShahRukh Athar, Luisa Verdoliva, Matthias Niessner
The decomposition of the parametric model and high-quality vertex displacements allows us to animate the model and modify it semantically.
no code implementations • 11 Dec 2023 • Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos, Martin Rünz, Lourdes Agapito, Matthias Nießner
We present Monocular Neural Parametric Head Models (MonoNPHM) for dynamic 3D head reconstructions from monocular RGB videos.
1 code implementation • 4 Dec 2023 • Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, Matthias Nießner
We introduce GaussianAvatars, a new method to create photorealistic head avatars that are fully controllable in terms of expression, pose, and viewpoint.
no code implementations • 30 Nov 2023 • Tobias Kirschstein, Simon Giebenhain, Matthias Nießner
DiffusionAvatars synthesizes a high-fidelity 3D head avatar of a person, offering intuitive control over both pose and expression.
no code implementations • 4 May 2023 • Tobias Kirschstein, Shenhan Qian, Simon Giebenhain, Tim Walter, Matthias Nießner
We focus on reconstructing high-fidelity radiance fields of human heads, capturing their animations over time, and synthesizing re-renderings from novel viewpoints at arbitrary time steps.
no code implementations • CVPR 2023 • Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos, Martin Rünz, Lourdes Agapito, Matthias Nießner
We propose a novel 3D morphable model for complete human heads based on hybrid neural fields.
1 code implementation • 22 Oct 2021 • Simon Giebenhain, Bastian Goldlücke
This paper introduces Attentive Implicit Representation Networks (AIR-Nets), a simple, but highly effective architecture for 3D reconstruction from point clouds.