activity
20242026
collaborators

11 papers

cs.CV2026

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…

cs.GR2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…