arXiv:2606.13630v1 Announce Type: new 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 relat
Ongoing advancements in AI and machine learning, particularly in generative models, are enabling more sophisticated and nuanced applications of speech and facial synthesis.
This research contributes to the foundational understanding and development of highly realistic digital humans, impacting virtual communication, entertainment, and potentially human-computer interaction.
The ability to more effectively translate discrete speech representations into natural 3D facial animation could lead to more expressive and convincing AI-driven avatars and virtual agents.
- · AI developers (especially in generative AI)
- · Entertainment industry (film, gaming)
- · Virtual reality/Augmented reality platforms
- · Digital content creators
- · Companies relying on less expressive or realistic digital avatars
- · Traditional animation techniques for facial realism
Improved realism and expressiveness in AI-generated virtual characters and avatars.
Enhanced immersion and engagement in virtual environments, remote work, and digital entertainment.
The blurring of lines between real and synthetic human interaction, posing new challenges for content authentication and trust.
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Read at arXiv cs.CL