EmbedTalk: Talking Head Synthesis using Gaussian Embeddings

Arpita Saggar; Jonathan C. Darling; Duygu Sarikaya; David C. Hogg

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arXiv · 2603.07604

EmbedTalk: Talking Head Synthesis using Gaussian Embeddings

Arpita Saggar·Jonathan C. Darling·Duygu Sarikaya·David C. Hogg

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Abstract

Deformable 3D Gaussian Splatting (3DGS) has emerged as a popular method for real-time talking head synthesis, offering high-quality renderings at low latency. Tri-planes are a common choice for encoding Gaussians prior to deformation since they provide a compact and continuous representation. However, tri-plane encodings are limited by grid resolution and approximation errors introduced by projecting 3D volumetric fields onto 2D subspaces. Recent work has demonstrated the effectiveness of per-Gaussian embeddings for driving temporal deformations in 4D scene reconstruction. We introduce EmbedTalk, which leverages these embeddings to model speech-driven facial deformations for talking head synthesis. Comprehensive experiments show that EmbedTalk improves rendering quality, lip synchronisation, and motion consistency over previous 3DGS-based methods, while remaining competitive with state-of-the-art generative models. Replacing tri-plane features with embeddings also yields significantly more compact models that achieve 60+ FPS on a laptop GPU (RTX 2060 6 GB). Our code will be placed in the public domain on acceptance.

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BibTeX RIS

Arpita Saggar, Jonathan C. Darling, Duygu Sarikaya, David C. Hogg. 2026-03-08. EmbedTalk: Talking Head Synthesis using Gaussian Embeddings. https://arxiv.org/abs/2603.07604

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