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arxiv: 2606.13630 · v1 · pith:J4WGAQPZnew · submitted 2026-06-11 · 💻 cs.CL

From Tokens to Faces: Investigating Discrete Speech Representations for 3D Facial Animation

Pedro Correa , Olivier Perrotin , Samir Sadok , Paula Costa , Thomas Hueber This is my paper

Pith reviewed 2026-06-27 06:44 UTC · model grok-4.3

classification 💻 cs.CL
keywords speech-driven 3D facial animationdiscrete speech representationsphonetic encodingaudio-visual text-to-speechtokenized speechfacial synthesisprobing analysis
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The pith

Encoding phonetic classes in discrete speech tokens produces accurate 3D facial animation with quality comparable to semantic representations.

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

The paper evaluates four families of speech representations for driving 3D facial animation, including self-supervised learning features, neural codecs, and label-based spaces. It compares their output quality using objective metrics, human perceptual tests, and two different facial decoders. Probing experiments link the tokenized outputs to phonetic units and to physical mouth and face deformations. The central result is that representations which encode phonetic classes support more accurate animation predictions, whether the tokens are semantic or strictly label-based, and that the final animation quality remains comparable across these choices. The authors then build an audio-visual text-to-speech system that re-uses the same discrete token space to generate both audio and 3D facial motion.

Core claim

The authors establish that encoding phonetic classes is beneficial for accurate facial animation prediction on both semantic and label-based representations with comparable facial animation quality. Probing analyses connect the tokenized representations to phonetic units and articulatory deformations. From these observations they construct an Audio Visual Text-to-Speech pipeline that treats the discrete representations as a shared space for decoding both speech and 3D facial motion.

What carries the argument

Evaluation of four speech representation families for 3D facial synthesis together with probing of tokenized outputs against phonetic units and articulatory deformations; the mechanism is phonetic-class encoding inside discrete tokens.

If this is right

  • Phonetic encoding improves prediction accuracy for facial animation across representation types.
  • Semantic and label-based representations reach similar animation quality once phonetic information is present.
  • Discrete token spaces can serve as a shared representation for simultaneous speech and 3D facial motion synthesis.
  • Probing shows direct relations between the tokens and both phonetic categories and physical articulatory changes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A single discrete token stream might support joint training of models that output text, audio, and face motion without separate feature pipelines.
  • The same phonetic-token approach could be tested for driving full-body gestures or head motion in addition to facial animation.
  • If the benefit of phonetic encoding persists across languages, the method could reduce reliance on language-specific semantic extractors in animation systems.

Load-bearing premise

The chosen objective metrics and perceptual evaluation protocol are sufficient to establish that phonetic encoding produces meaningfully better or equivalent facial animation in practical use cases.

What would settle it

A perceptual test in which listeners consistently rate the naturalness of facial animations driven by phonetic-encoded tokens as lower than those driven by non-phonetic tokens would falsify the claim of comparable quality.

Figures

Figures reproduced from arXiv: 2606.13630 by Olivier Perrotin, Paula Costa, Pedro Correa, Samir Sadok, Thomas Hueber.

Figure 1
Figure 1. Figure 1: Speech representations (middle) are extracted either from input speech using a speech encoder (top) or produced within a text-to-speech (TTS) pathway conditioned on text and reference speech (bottom). Both discrete and continuous repre￾sentations are illustrated, with colour coding indicating empha￾sis on acoustic vs. semantic information. The shared represen￾tation can then be decoded into speech audio (s… view at source ↗
Figure 2
Figure 2. Figure 2: Sequence of rendered blendshapes across different models and audio snippets. With each different line, we are able to visually assess mouth and lip shapes across different groups of phonemes with significantly distinct visemes. Mesh model adapted from EmoTalk [24]. mantic distillation; WavTokenizer employs a single-codebook with extreme compression aiming at acoustic reconstruction; and CosyVoice2 relies o… view at source ↗
Figure 3
Figure 3. Figure 3: MUSHRA-like scores of the perceptual evaluation across three different models and the reference. N.S. means no statistical significance between the pair of distributions. been favoured by a fully supervised discrete output training task. Finally, compared to HuBERT, all three tokenized rep￾resentations display a very low R 2 metric, which measures how accurately a blendshape trajectory can be predicted fro… view at source ↗
read the original abstract

The choice of speech representation is critical in speech-driven 3D facial animation. Representations differ in what they encode: SSL features emphasize segmental and semantic cues, neural codecs yield latents optimized for acoustic reconstruction, and ASR-style objectives produce label-based spaces. We evaluate four speech representation families for 3D facial synthesis, comparing their facial reconstruction quality across two facial decoders using objective metrics and a perceptual evaluation. We additionally conduct probing analyses that relate tokenized representations to phonetic units and to articulatory deformations. We found that encoding phonetic classes is beneficial for accurate facial animation prediction on both semantic and label-based representations with comparable facial animation quality. From the latter, we introduce an Audio Visual Text-to-Speech (AVTTS) pipeline that leverages, as a shared space, discrete representations to decode speech and 3D facial motion.

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.

Referee Report

2 major / 1 minor

Summary. The manuscript evaluates four speech representation families for 3D facial synthesis using two facial decoders. It compares their reconstruction quality with objective metrics and perceptual evaluation, conducts probing analyses relating tokens to phonetic units and articulatory deformations, and concludes that phonetic class encoding is beneficial for accurate facial animation on semantic and label-based representations with comparable quality. It also introduces an AVTTS pipeline using discrete representations as a shared space.

Significance. If the results are robust, this work would advance the understanding of how different speech representations, particularly those encoding phonetic classes, impact 3D facial animation quality. The introduction of the AVTTS pipeline could have practical implications for audio-visual synthesis systems. The probing analyses provide additional insight into the relationship between discrete tokens and phonetic/articulatory features.

major comments (2)
  1. [§5] §5 (Experiments): The quality comparison between representations does not include an ablation that holds discretization and training objective fixed while varying phonetic supervision, so the attribution of benefits to phonetic encoding rather than other properties of the discrete spaces remains unsupported.
  2. [Perceptual evaluation section] Perceptual evaluation section: The protocol's sensitivity, effect sizes, inter-rater reliability, and controls for representation dimensionality or training regime are not shown, weakening the claim that phonetic encoding produces 'comparable' or 'beneficial' animation quality.
minor comments (1)
  1. The abstract could more explicitly name the four representation families evaluated.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment point by point below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [§5] §5 (Experiments): The quality comparison between representations does not include an ablation that holds discretization and training objective fixed while varying phonetic supervision, so the attribution of benefits to phonetic encoding rather than other properties of the discrete spaces remains unsupported.

    Authors: We agree that the current design compares established representation families that differ along multiple axes (discretization method, training objective, and supervision type), so a direct causal attribution to phonetic encoding alone is not isolated. The probing experiments provide supporting correlational evidence linking tokens to phonetic units, but this does not substitute for a controlled ablation. In the revision we will explicitly qualify the claims to reflect the comparative nature of the evaluation across families and add a limitations paragraph acknowledging the absence of such an ablation. A full controlled ablation would require substantial additional training and is not feasible within the current revision timeline; we therefore treat this as a limitation rather than performing the experiment. revision: partial

  2. Referee: [Perceptual evaluation section] Perceptual evaluation section: The protocol's sensitivity, effect sizes, inter-rater reliability, and controls for representation dimensionality or training regime are not shown, weakening the claim that phonetic encoding produces 'comparable' or 'beneficial' animation quality.

    Authors: We accept that the perceptual evaluation section is missing these quantitative details. In the revised manuscript we will report effect sizes, inter-rater reliability (e.g., Krippendorff’s alpha), and an assessment of protocol sensitivity. We will also include a brief discussion of dimensionality differences across representations and note that training regimes were held as consistent as possible given the source models. These additions will allow us to support the “comparable quality” statement with appropriate statistical context. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical evaluation with no derivations or fitted predictions

full rationale

The paper reports an empirical comparison of four speech representation families for 3D facial animation synthesis, using objective metrics, perceptual evaluation, and probing analyses. No equations, derivations, or 'predictions' derived from fitted parameters appear; the central claim is framed as an observation from experiments rather than a quantity obtained by construction from inputs. No self-citation chains, uniqueness theorems, or ansatzes are invoked to justify load-bearing steps. The work is self-contained as a standard experimental study against external benchmarks (metrics and human raters), with any limitations in ablation design falling under evidence strength rather than circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only input supplies no information on free parameters, background axioms, or newly postulated entities.

pith-pipeline@v0.9.1-grok · 5677 in / 936 out tokens · 19053 ms · 2026-06-27T06:44:11.786380+00:00 · methodology

discussion (0)

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Reference graph

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    Method 2.1. Experimental Setup To investigate which speech tokens can serve as effective rep- resentations for speech-driven 3D facial animation, we adopted a comparative experimental framework. The general pipeline of our method is illustrated in Fig. 1. We evaluated four speech encoders: HuBERT (HB) [6], SpeechTokenizer (ST) [14], Wav- Tokenizer (WT) [1...

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    Acknowledgments This work was funded by the S ˜ao Paulo Research Foundation (FAPESP) under grant #2025/09875-7 through a Research In- ternship Abroad (BEPE) scholarship in the GIPSA-lab (Uni- versit´e Grenoble Alpes), supported by FAPESP under grant #2020/09838-0 (BI0S - Brazilian Institute of Data Science), and partially funded by the Coordenac ¸˜ao de A...

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