AGORA: Adversarial Generation Of Real-time Animatable 3D Gaussian Head Avatars
Ramazan Fazylov, Sergey Zagoruyko, Aleksandr Parkin, Stamatis Lefkimmiatis, Ivan Laptev

TL;DR
AGORA is a novel framework that generates high-fidelity, animatable 3D head avatars using a GAN-based approach, enabling real-time rendering and on-device animation with state-of-the-art quality.
Contribution
It extends 3D Gaussian Splatting with adversarial training to produce controllable, realistic, and animatable 3D head avatars suitable for real-time applications.
Findings
Achieves 560 FPS rendering on GPU and 60 FPS on mobile.
Outperforms existing methods in avatar realism and controllability.
Provides a simple inference-time approach for on-device animation.
Abstract
The generation of high-fidelity, animatable 3D human avatars remains a core challenge in computer graphics and vision, with applications in VR, telepresence, and entertainment. Existing approaches based on implicit representations like NeRFs suffer from slow rendering and dynamic inconsistencies, while 3D Gaussian Splatting (3DGS) methods are typically limited to static head generation, lacking dynamic control. We bridge this gap by introducing AGORA, a novel framework that extends 3DGS within a generative adversarial network to produce animatable avatars. Our formulation combines spatial shape conditioning with a dual-discriminator training strategy that supervises both rendered appearance and synthetic geometry cues, improving expression fidelity and controllability. To enable practical deployment, we further introduce a simple inference-time approach that extracts Gaussian…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Face recognition and analysis · 3D Shape Modeling and Analysis
