34 citations · 67 across the 19 of their papers we have counts for
22 papers · 1 filter
ViDS: Video Diffusion Shader using 3D Face Tracking
Wenbo Ji, Davide Davoli, Zhe Chen +3
We introduce ViDS, a Video Diffusion Shader that leverages 3D face tracking for expressive and identity-preserving portrait animation. We first reconstruct the identity-specific 3D…
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…
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…
FactorPortrait: Controllable Portrait Animation via Disentangled Expression, Pose, and Viewpoint
Jiapeng Tang, Kai Li, Chengxiang Yin +8
We introduce FactorPortrait, a video diffusion method for controllable portrait animation that enables lifelike synthesis from disentangled control signals of facial expressions, h…
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…
ROGR: Relightable 3D Objects using Generative Relighting
Jiapeng Tang, Matthew Levine, Dor Verbin +5
We introduce ROGR, a novel approach that reconstructs a relightable 3D model of an object captured from multiple views, driven by a generative relighting model that simulates the e…
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…
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…
GeomHair: Reconstruction of Hair Strands from Colorless 3D Scans
Rachmadio Noval Lazuardi, Artem Sevastopolsky, Egor Zakharov +2
We propose a novel method that reconstructs hair strands directly from colorless 3D scans by leveraging multi-modal hair orientation extraction. Hair strand reconstruction is a fun…
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…
SHeaP: Self-Supervised Head Geometry Predictor Learned via 2D Gaussians
Liam Schoneveld, Zhe Chen, Davide Davoli +4
Accurate, real-time 3D reconstruction of human heads from monocular images and videos underlies numerous visual applications. As 3D ground truth data is hard to come by at scale, p…
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…
Zero-Shot Detection of AI-Generated Images
Davide Cozzolino, Giovanni Poggi, Matthias Nießner +1
Detecting AI-generated images has become an extraordinarily difficult challenge as new generative architectures emerge on a daily basis with more and more capabilities and unpreced…
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…
Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and Tracking
Wei Cao, Chang Luo, Biao Zhang +2
We introduce Motion2VecSets, a 4D diffusion model for dynamic surface reconstruction from point cloud sequences. While existing state-of-the-art methods have demonstrated success i…
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…