GAN-Avatar: Controllable Personalized GAN-based Human Head Avatars | Neural Capture and Synthesis – Max Planck Institute for Intelligent Systems

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Research Overview

Digital Humans in Motion

Digital Multi-Media Forensics

Digitization of Objects and Scenes

Capturing Humans, Objects and Scenes

MICA: Towards Metrical Reconstruction of Human Faces

GAN-Avatar: Controllable Personalized GAN-based Human Head Avatars

INSTA: Instant Volumetric Head Avatars

Motion Synthesis

Imitator: Personalized Speech-driven 3D Facial Animation

FaceTalk: Audio-driven Motion Diffusion for Neural Parametric Head Models

3DiFace: Synthesizing and Editing Holistic 3D Facial Animations

(Generative) Appearance Modelling and Editing

HAAR: Tex-conditioned Generative Model of 3D Strand-based Human Hairstyles

High-Res Facial Appearance Capture from Polarized Smartphone Images

Stable Video Portraits

Neural Capture and Synthesis

GAN-Avatar: Controllable Personalized GAN-based Human Head Avatars

Ganavatar — Learning the distribution of facial expressions to construct a 3D personalized avatar.

We propose to learn person-specific animatable avatars from images without assuming to have access to precise facial expression tracking. At the core of our method, we leverage a 3D-aware generative model that is trained to reproduce the distribution of facial expressions from the training data.