103 citations · 175 across the 14 of their papers we have counts for
15 papers
How to Build Digital Humans? From Priors to Photorealistic Avatars
Wojciech Zielonka, Tobias Kirschstein, Timo Bolkart +8
This state-of-the-art report provides an overview of controllable 3D human avatar creation. We describe current 3D avatar systems, which typically consist of three stages: (i) lear…
Face Anything: 4D Face Reconstruction from Any Image Sequence
Umut Kocasari, Simon Giebenhain, Richard Shaw +1
Accurate reconstruction and tracking of dynamic human faces from image sequences is challenging because non-rigid deformations, expression changes, and viewpoint variations occur s…
Pix2NPHM: Learning to Regress NPHM Reconstructions From a Single Image
Simon Giebenhain, Tobias Kirschstein, Liam Schoneveld +3
Neural Parametric Head Models (NPHMs) are a recent advancement over mesh-based 3d morphable models (3DMMs) to facilitate high-fidelity geometric detail. However, fitting NPHMs to v…
FlexAvatar: Learning Complete 3D Head Avatars with Partial Supervision
Tobias Kirschstein, Simon Giebenhain, Matthias Nießner
We introduce FlexAvatar, a method for creating high-quality and complete 3D head avatars from a single image. A core challenge lies in the limited availability of multi-view data a…
BecomingLit: Relightable Gaussian Avatars with Hybrid Neural Shading
Jonathan Schmidt, Simon Giebenhain, Matthias Niessner
We introduce BecomingLit, a novel method for reconstructing relightable, high-resolution head avatars that can be rendered from novel viewpoints at interactive rates. Therefore, we…
Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction
Simon Giebenhain, Tobias Kirschstein, Martin Rünz +2
We address the 3D reconstruction of human faces from a single RGB image. To this end, we propose Pixel3DMM, a set of highly-generalized vision transformers which predict per-pixel…
GGHead: Fast and Generalizable 3D Gaussian Heads
Tobias Kirschstein, Simon Giebenhain, Jiapeng Tang +2
Learning 3D head priors from large 2D image collections is an important step towards high-quality 3D-aware human modeling. A core requirement is an efficient architecture that scal…
NPGA: Neural Parametric Gaussian Avatars
Simon Giebenhain, Tobias Kirschstein, Martin Rünz +2
The creation of high-fidelity, digital versions of human heads is an important stepping stone in the process of further integrating virtual components into our everyday lives. Cons…
HeadCraft: Modeling High-Detail Shape Variations for Animated 3DMMs
Artem Sevastopolsky, Philip-William Grassal, Simon Giebenhain +3
Current advances in human head modeling allow the generation of plausible-looking 3D head models via neural representations, such as NeRFs and SDFs. Nevertheless, constructing comp…
MonoNPHM: Dynamic Head Reconstruction from Monocular Videos
Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos +3
We present Monocular Neural Parametric Head Models (MonoNPHM) for dynamic 3D head reconstructions from monocular RGB videos. To this end, we propose a latent appearance space that…
GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians
Shenhan Qian, Tobias Kirschstein, Liam Schoneveld +3
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…
DiffusionAvatars: Deferred Diffusion for High-fidelity 3D Head Avatars
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. We propose a diffusion-based neural renderer that…
NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads
Tobias Kirschstein, Shenhan Qian, Simon Giebenhain +2
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 tim…
Learning Neural Parametric Head Models
Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos +3
We propose a novel 3D morphable model for complete human heads based on hybrid neural fields. At the core of our model lies a neural parametric representation that disentangles ide…
AIR-Nets: An Attention-Based Framework for Locally Conditioned Implicit Representations
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. Since representing…