11 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…
FlexAvatar: Learning Complete 3D Head Avatars with Partial Supervision
Tobias Kirschstein, Simon Giebenhain, Matthias NieÃner
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…
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…
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…