Ex-Omni: Enabling 3D Facial Animation Generation for Omni-modal Large Language Models

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 underexplored. A key challenge is the mismatch between the discrete semantic reasoning of LLMs and the dense temporal dynamics required for 3D facial motion. We propose Expressive Omni (Ex-Omni), a framework that augments OLLMs with speech-accompanied 3D facial animation. Ex-Omni decouples semantic reasoning from temporal generation through a speech-unit generator with blendshape co-supervision and a non-autoregressive blendshape decoder, where speech units provide temporal scaffolding and hidden speech representations carry facially relevant cues. We further introduce a token-as-query gated fusion (TQGF) interface for controlled semantic injection, as well as InstructS2SF-1200K, a 1.2M-sample weakly supervised dataset for speech-accompanied facial animation. Extensive experiments show that Ex-Omni retains competitive speech QA capability while natively generating coordinated text, speech, and 3D facial animation, and approaches the Audio2Face-3D teacher cascade in synchronization and human preference.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2602.07106 [cs.CV]

(or arXiv:2602.07106v3 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2602.07106 Focus to learn more arXiv-issued DOI via DataCite

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