34 citations · 67 across the 19 of their papers we have counts for
11 papers · 1 filter
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…