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.
arXiv:2608. 05218v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) enables fast, photorealistic talking-head rendering, yet accurate lip articulation remains elusive: mouth motion is often over-smoothed and may violate hard articulatory constraints such as bilabial closures, producing the notorious ``leaky mouth'' artifact.
arXiv:2606. 28568v1 Announce Type: cross Abstract: Speech-driven 3D facial animation methods face significant challenges in simultaneously achieving high-fidelity motion and precise artistic control at production quality.
arXiv:2606. 16327v1 Announce Type: cross Abstract: Recent acoustic-to-articulatory inversion (AAI) models rely on electromagnetic articulography (EMA) data, which are costly and limited in scale.
arXiv:2606.15848v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has shown strong potential for high-fidelity talking head synthesis. However, enabling fine-grained, interpretable, an...
arXiv:2609.17422v1 Announce Type: new Abstract: Audio-driven digital human generation plays an important role in virtual communication, immersive interaction, and media production. With the developme...
arXiv:2602. 07106v2 Announce Type: replace-cross Abstract: Omni-modal large language models (OLLMs) aim to unify multimodal understanding and generation, yet extending them to jointly produce speech and 3D facial animation remains largely unexplored despite its importance for natural human-computer interaction.
KoUniTalk is a lightweight, articulation‑centered benchmark that unifies Korean and English 3D talking‑face datasets onto a single mesh topology. By retargeting VOCASET and Korean speech‑based 3D data to a shared 1,176‑vertex template, it reduces output dimensionality from tens of thousands to 3,528 dimensions, focusing on the mouth and adjacent lower‑face regions. The benchmark includes 22 speakers, 4,978 sequences, and 642,781 frames, enabling controlled speech‑driven facial articulation training and cross‑dataset evaluation in a compact, identity‑neutral space.
arXiv:2609.18632v1 Announce Type: new Abstract: Multi-modal talking avatar synthesis aims to generate realistic talking videos from a reference portrait and speech. Despite rapid progress in diffusio...
Multi-modal talking avatar synthesis aims to generate realistic talking videos from a reference portrait and speech. Despite rapid progress in diffusion-based methods, existing approaches still strugg...
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.
arXiv:2503. 14295v3 Announce Type: replace-cross Abstract: Recent advancements in audio-driven talking face generation have made great progress in lip synchronization.
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.