Human Avatar Animation

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Human avatar animation studies how to create digital people whose appearance and motion remain coherent as they speak, gesture, change pose, or interact. Systems reconstruct avatars from images or video using meshes and parametric models such as FLAME and SMPL-X, neural radiance fields, or 3D Gaussian Splatting, then animate them with pose retargeting, learned deformation, or physical simulation. Diffusion Transformers and flow-matching models increasingly generate motion from audio, video references, or semantic controls, with evaluations covering identity, lip synchronization, expression, and temporal consistency. A central challenge is balancing realistic, varied motion and generalization to new people with identity fidelity, stable long-form behavior, and real-time performance.

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AGORA: Adversarial Generation Of Real-time Animatable 3D Gaussian Head Avatars
Dec 6, 2025Ramazan Fazylov, Sergey Zagoruyko, Aleksandr Parkin +23D Gaussian Splatting 3D Asset Generation
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Polynome AI, Dubai, UAE · MTS AI, Moscow, Russia

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AGORA: Adversarial Generation Of Real-time Animatable 3D Gaussian Head Avatars

Ramazan Fazylov, Sergey Zagoruyko, Aleksandr Parkin, Stamatis Lefkimmiatis+1

Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Polynome AI, Dubai, UAE · MTS AI, Moscow, Russia

3D Gaussian Splatting 3D Asset Generation 3D Avatar Generation Human Avatar Animation

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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 key contribution is a lightweight, FLAME-conditioned deformation branch that predicts per-Gaussian residuals, enabling identity-preserving, fine-grained expression control while allowing real-time inference. Identity is further preserved through spatial shape conditioning of the identity branch, and expression fidelity is enforced via a dual-discriminator training scheme leveraging synthetic renderings of the parametric mesh. AGORA generates avatars that are not only visually realistic but also precisely controllable. Quantitatively, we outperform state-of-the-art NeRF-based methods on expression accuracy while rendering at 250 FPS on a single GPU and, notably, at

\sim

9 FPS under CPU-only inference -- to our knowledge the first demonstration of CPU-only animatable 3DGS avatar synthesis. This work represents a significant step toward practical, high-performance digital humans. Project website: https://ramazan793.github.io/AGORA/