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arxiv: 2604.16108 · v1 · submitted 2026-04-17 · 💻 cs.CV

Polyglot: Multilingual Style Preserving Speech-Driven Facial Animation

Federico Nocentini , Kwanggyoon Seo , Qingju Liu , Claudio Ferrari , Stefano Berretti , David Ferman , Hyeongwoo Kim , Pablo Garrido
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Akin Caliskan
This is my paper

Pith reviewed 2026-05-10 08:24 UTC · model grok-4.3

classification 💻 cs.CV
keywords languagefacialmultilingualstylesdfapolyglotanimationconditioning
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The pith

Polyglot introduces a unified diffusion model for multilingual speech-driven facial animation that jointly conditions on language via transcript embeddings and personal style via reference sequences without requiring explicit labels.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Speech-driven facial animation creates moving digital faces that match spoken audio. Current systems usually work for only one language because different languages have unique sounds, rhythms, and mouth movements. They also often ignore that each person has their own habits like how much they raise eyebrows or tilt their head when talking. Polyglot tries to fix both problems at once. It takes the written transcript of the speech to understand the language and pulls style information from short example videos of the speaker. These two pieces of information are fed into a diffusion model, which is a type of AI good at creating smooth, realistic sequences over time. The system learns everything without being told in advance which language or which person it is seeing. This self-supervised approach lets it handle new languages and new speakers. The result is supposed to be animations that match the speech timing and the individual's natural expressions, staying consistent from frame to frame. The paper claims this works better than previous methods that handled either language or style but not both together.

Core claim

By jointly conditioning on language and style, it captures expressive traits such as rhythm, articulation, and habitual facial movements, producing temporally coherent and realistic animations. Experiments show improved performance in both monolingual and multilingual settings, providing a unified framework for modeling language and personal style in SDFA.

Load-bearing premise

That transcript embeddings sufficiently encode language-specific phonetic and rhythmic information and that style embeddings extracted from reference facial sequences can capture individual speaking characteristics, with their combination in the diffusion model generalizing across unseen languages and speakers via self-supervised learning alone.

Figures

Figures reproduced from arXiv: 2604.16108 by Akin Caliskan, Claudio Ferrari, David Ferman, Federico Nocentini, Hyeongwoo Kim, Kwanggyoon Seo, Pablo Garrido, Qingju Liu, Stefano Berretti.

Figure 1
Figure 1. Figure 1: Polyglot, a deep learning architecture for speech-driven facial animation that preserves language and personal speaking styles during animation. II. RELATED WORKS In recent years, a wide range of models and methods have been developed to tackle the challenge of synchronizing facial animation with speech. Early approaches focused on procedural techniques [10], [24], [6], [43], [47], relying heavily on visem… view at source ↗
Figure 2
Figure 2. Figure 2: Left: Polyglot architecture. Audio A0:T is processed by mHuBERT EA, Whisper ER, and CLIP ET to extract features, transcripts, and language embeddings. A style embedding S is computed from input motion M0 0:T via style encoder ES. Conditioned on identity β, language tˆ, style S, and timestep n, the diffusion decoder T D denoises noisy parameters Mn into motion M0 . Right: The style encoder ES extracts per-f… view at source ↗
Figure 3
Figure 3. Figure 3: Japanese [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 6
Figure 6. Figure 6: t-SNE visualization of text embeddings produced by concatenating [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: t-SNE plot of personal style embeddings S for 20 identities across Ukrainian and Thai. The clusters show that the personal speaking style encoder ES consistently captures identity-specific personal speaking styles [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: t-SNE visualization of personal style embeddings [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
read the original abstract

Speech-Driven Facial Animation (SDFA) has gained significant attention due to its applications in movies, video games, and virtual reality. However, most existing models are trained on single-language data, limiting their effectiveness in real-world multilingual scenarios. In this work, we address multilingual SDFA, which is essential for realistic generation since language influences phonetics, rhythm, intonation, and facial expressions. Speaking style is also shaped by individual differences, not only by language. Existing methods typically rely on either language-specific or speaker-specific conditioning, but not both, limiting their ability to model their interaction. We introduce Polyglot, a unified diffusion-based architecture for personalized multilingual SDFA. Our method uses transcript embeddings to encode language information and style embeddings extracted from reference facial sequences to capture individual speaking characteristics. Polyglot does not require predefined language or speaker labels, enabling generalization across languages and speakers through self-supervised learning. By jointly conditioning on language and style, it captures expressive traits such as rhythm, articulation, and habitual facial movements, producing temporally coherent and realistic animations. Experiments show improved performance in both monolingual and multilingual settings, providing a unified framework for modeling language and personal style in SDFA.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on standard machine learning assumptions about the representational power of embeddings and diffusion models rather than any explicitly stated axioms or invented entities in the abstract.

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