Researchers Compare Speech Representations for AI-Generated 3D Facial Animation
A new study accepted at Interspeech 2026 evaluates how different types of speech representations—including self-supervised learning features, neural codecs, and ASR-based systems—perform when used to generate synchronized 3D facial animations. The research found that representations encoding phonetic information produce the best facial animation quality across different decoder architectures. The findings could improve speech-driven animation systems used in virtual avatars, film, and interactive media.
Researchers have systematically compared four families of speech representations to determine which best captures the information needed for accurate 3D facial animation synthesis. The study evaluated self-supervised learning (SSL) features, neural codecs optimized for acoustic reconstruction, and ASR-style label-based representations using objective metrics and human perceptual evaluation across two facial decoders. Through probing analyses, the team identified that phonetic encoding is particularly beneficial for facial animation prediction. The work introduces an Audio Visual Text-to-Speech (AVTTS) pipeline that uses discrete speech representations as a shared space to jointly decode speech and 3D facial motion, potentially enabling more natural and synchronized animated avatars.
What's missing
The study does not specify which two facial decoders were used for comparison, limiting reproducibility assessment. The paper does not discuss computational costs or inference speed differences between the representation families, which would be relevant for practical deployment. Specific performance metrics and numerical comparisons between representation types are not provided in the abstract.
What different sources said
- arXiv cs.CLCenter
From Tokens to Faces: Investigating Discrete Speech Representations for 3D Facial Animation
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