Name: Simon Giebenhain
Position: Ph.D Candidate
E-Mail: simon.giebenhain@tum.de
Phone: TBD
Room No: 02.07.035

Bio

Hi, I’m Simon. My main research interests are neural scene representations, neural rendering and geomeric deep learning. Prior to joining the Visual Computing Lab as a PhD. student under the supervision of Prof. Matthias Nießner, I completed my Bachelors and Masters in computer science at the University of Konstanz in the Computer Vision and Image Analysis Group of Prof. Bastian Goldlücke. During my Bachelors degree I visited the University of Toronto for two semesters as an exchange student. Homepage

Research Interest

3D Reconstruction, Neural Fields, Neural Rendering, Generative Modeling, Geometric Deep Learning

Publications

Preprints
HeadCraft: Modeling High-Detail Shape Variations for Animated 3DMMs
Artem Sevastopolsky, Philip-William Grassal, Simon Giebenhain, ShahRukh Athar, Luisa Verdoliva, Matthias Nießner
arXiv
We learn to generate large displacements for parametric head models, such as long hair, with high level of detail. The displacements can be added to an arbitrary head for animation and semantic editing.
[video][code][bibtex][project page]
2024
DiffusionAvatars: Deferred Diffusion for High-fidelity 3D Head Avatars
Tobias Kirschstein, Simon Giebenhain, Matthias Nießner
CVPR 2024
DiffusionAvatar uses diffusion-based, deferred neural rendering to translate geometric cues from an underlying neural parametric head model (NPHM) to photo-realistic renderings. The underlying NPHM provides accurate control over facial expressions, while the deferred neural rendering leverages the 2D prior of StableDiffusion, in order to generate compelling images.
[video][bibtex][project page]
MonoNPHM: Dynamic Head Reconstruction from Monoculuar Videos
Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos, Martin Rünz, Lourdes Agapito, Matthias Nießner
CVPR 2024
MonoNPHM is a neural parametric head model that disentangles geomery, appearance and facial expression into three separate latent spaces. Using MonoNPHM as a prior, we tackle the task of dynamic 3D head reconstruction from monocular RGB videos, using inverse, SDF-based, volumetric rendering.
[video][bibtex][project page]
GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians
Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, Matthias Nießner
CVPR 2024
We introduce GaussianAvatars, a new method to create photorealistic head avatars that are fully controllable in terms of expression, pose, and viewpoint. The core idea is a dynamic 3D representation based on 3D Gaussian splats that are rigged to a parametric morphable face model. This combination facilitates photorealistic rendering while allowing for precise animation control via the underlying parametric model.
[video][bibtex][project page]
2023
NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads
Tobias Kirschstein, Shenhan Qian, Simon Giebenhain, Tim Walter, Matthias Nießner
SIGGRAPH'23
We propose NeRSemble for high-quality novel view synthesis of human heads. We combine a deformation field modeling coarse motion with an ensemble of multi-resolution hash encodings to represent fine expression-dependent details. To train our model, we recorded a novel multi-view video dataset containing over 4700 sequences of human heads covering a variety of facial expressions.
[video][bibtex][project page]
Learning Neural Parametric Head Models
Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos, Martin Rünz, Lourdes Agapito, Matthias Nießner
CVPR 2023
We present Neural Parametric Head Models (NPHMs) for high fidelity representation of complete human heads. We utilize a hybrid representation for a person's canonical head geometry, which is deformed using a deformation field to model expressions. To train our model we captured a large dataset of high-end laser scans of 120 persons in 20 expressions each.
[video][bibtex][project page]