| 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 LearningPublications
2024
| DiffusionAvatars: Deferred Diffusion for High-fidelity 3D Head Avatars |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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] |