Ex-Omni: Enabling 3D Facial Animation Generation for Omni-modal Large Language Models
This addresses the problem of natural interaction in multimodal AI by enabling speech-accompanied 3D facial animation, representing an incremental advancement in omni-modal frameworks.
The paper tackles the challenge of generating 3D facial animation with speech for omni-modal large language models, proposing Ex-Omni, which decouples semantic reasoning from temporal generation and achieves competitive performance in stable aligned generation.
Omni-modal large language models (OLLMs) aim to unify multimodal understanding and generation, yet incorporating speech with 3D facial animation remains largely unexplored despite its importance for natural interaction. A key challenge arises from the representation mismatch between discrete, token-level semantic reasoning in LLMs and the dense, fine-grained temporal dynamics required for 3D facial motion, which makes direct modeling difficult to optimize under limited data. We propose Expressive Omni (Ex-Omni), an open-source omni-modal framework that augments OLLMs with speech-accompanied 3D facial animation. Ex-Omni reduces learning difficulty by decoupling semantic reasoning from temporal generation, leveraging speech units as temporal scaffolding and a unified token-as-query gated fusion (TQGF) mechanism for controlled semantic injection. We further introduce InstructEx, a dataset aims to facilitate augment OLLMs with speech-accompanied 3D facial animation. Extensive experiments demonstrate that Ex-Omni performs competitively against existing open-source OLLMs while enabling stable aligned speech and facial animation generation.