103 citations · 238 across the 18 of their papers we have counts for
19 papers
Head Avatars with Dynamic Explicit Hair
Vanessa Sklyarova, Haonan Chen, Berna Kabadayi +8
We present DynHair, a novel method for tracking and modeling dynamic hair for human head avatars. From video input, we reconstruct a dynamic head avatar with an explicit strand-bas…
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…
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…
PercHead: Perceptual Head Model for Single-Image 3D Head Reconstruction & Editing
Antonio Oroz, Matthias Nießner, Tobias Kirschstein
We present PercHead, a model for single-image 3D head reconstruction and disentangled 3D editing - two tasks that are inherently challenging due to ambiguity in plausible explanati…
HairGS: Hair Strand Reconstruction based on 3D Gaussian Splatting
Yimin Pan, Matthias Nießner, Tobias Kirschstein
Human hair reconstruction is a challenging problem in computer vision, with growing importance for applications in virtual reality and digital human modeling. Recent advances in 3D…
3DGH: 3D Head Generation with Composable Hair and Face
Chengan He, Junxuan Li, Tobias Kirschstein +7
We present 3DGH, an unconditional generative model for 3D human heads with composable hair and face components. Unlike previous work that entangles the modeling of hair and face, w…
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…
Avat3r: Large Animatable Gaussian Reconstruction Model for High-fidelity 3D Head Avatars
Tobias Kirschstein, Javier Romero, Artem Sevastopolsky +2
Traditionally, creating photo-realistic 3D head avatars requires a studio-level multi-view capture setup and expensive optimization during test-time, limiting the use of digital hu…
GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusion
Jiapeng Tang, Davide Davoli, Tobias Kirschstein +2
We propose a novel approach for reconstructing animatable 3D Gaussian avatars from monocular videos captured by commodity devices like smartphones. Photorealistic 3D head avatar re…
GaussianSpeech: Audio-Driven Gaussian Avatars
Shivangi Aneja, Artem Sevastopolsky, Tobias Kirschstein +3
We introduce GaussianSpeech, a novel approach that synthesizes high-fidelity animation sequences of photo-realistic, personalized 3D human head avatars from spoken audio. To captur…
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…
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…
Language-Agnostic Representation Learning of Source Code from Structure and Context
Daniel Zügner, Tobias Kirschstein, Michele Catasta +2
Source code (Context) and its parsed abstract syntax tree (AST; Structure) are two complementary representations of the same computer program. Traditionally, designers of machine l…