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arXiv Computer Vision

EmbedTalk: Talking Head Synthesis using Gaussian Embeddings

EmbedTalk introduces per‑Gaussian embeddings to drive speech‑driven facial deformations in real‑time talking head synthesis, replacing traditional tri‑plane encodings. This approach improves rendering quality, lip synchronisation, and motion consistency compared to prior 3D Gaussian Splatting methods while producing more compact models that run at 60+ FPS on a laptop GPU. The technique demonstrates competitive performance against state‑of‑the‑art generative models.

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GaussianEmoTalker: Real-Time Emotional Talking Head Synthesis with Audio-Driven and Blendshape-Based 3D Gaussian Splatting

Audio-driven talking head synthesis has achieved impressive progress in lip synchronization and visual quality, yet generating expressive emotional avatars with controllable intensity remains challenging, especially under real-time constraints. In this paper, we present GaussianEmoTalker, an audio-driven framework for real-time emotional talking head synthesis based on 3D Gaussian Splatting.

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arXiv Machine Learning
Jul 2

Large-Scale High-Quality 3D Gaussian Head Reconstruction from Multi-View Captures

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arXiv Machine Learning
Sep 29

Seeing Speech: Learning Visible Articulatory Dynamics for Speech-Driven 3D Facial Animation

The paper introduces a new framework for speech‑driven 3D facial animation that explicitly models visible articulatory dynamics. It uses a Speech‑Articulatory Memory (SAM) to link speech to three directional articulatory motions—spreading, opening, and protrusion—under phonetic context, and a Topology‑aware Articulatory Composition (TAC) to integrate these motions into surface‑consistent facial motion. Experiments on VOCASET and TFHP demonstrate state‑of‑the‑art reconstruction quality and improved lip articulation metrics, with a user study confirming better lip sync and realism.

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arXiv Machine Learning
Oct 9

GHARP: Real-time Gaussian Head Animation from Large-scale Reconstruction Prior

GHARP is a two‑stage method that animates 3D human heads in real time using a large‑scale reconstruction prior. The first offline identity stage builds a compact Gaussian representation of a subject’s geometry and appearance, while the lightweight runtime animation stage predicts expression‑dependent residuals in a semantically structured latent space. A body alignment network further refines the animation by aligning body pose and clothing, enabling state‑of‑the‑art quality on the Ava‑256 benchmark with significantly faster performance and fewer Gaussians.

By Ali Benlalah, Sepehr Johari, Patricia Vitoria, Armin Kappeler, Artem Sevastopolsky, Alexander Jung, Gabriele Fanelli, Kevin Mader, Manuel Breitenstein, Claudia Pl\"uss, Jan R\"uegg, Simon Biland, Thomas Etterlin, Dmitry Kostiaev, Mathias Deschler, Brian Amberg, Sebastian Martin
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