AUHead: Realistic Emotional Talking Head Generation via Action Units Control
Abstract
Realistic talking-head video generation is critical for virtual avatars, film production, and interactive systems. Current methods struggle with nuanced emotional expressions due to the lack of fine-grained emotion control. To address this issue, we introduce a novel two-stage method (AUHead) to disentangle fine-grained emotion control, i.e. , Action Units (AUs), from audio and achieve controllable generation. In the first stage, we explore the AU generation abilities of large audio-language models (ALMs), by spatial-temporal AU tokenization and an "emotion-then-AU" chain-of-thought mechanism. It aims to disentangle AUs from raw speech, effectively capturing subtle emotional cues. In the second stage, we propose an AU-driven controllable diffusion model that synthesizes realistic talking-head videos conditioned on AU sequences. Specifically, we first map the AU sequences into the structured 2D facial representation to enhance spatial fidelity, and then model the AU-vision interaction within cross-attention modules. To achieve flexible AU-quality trade-off control, we introduce an AU disentanglement guidance strategy during inference, further refining the emotional expressiveness and identity consistency of the generated videos. Results on benchmark datasets demonstrate that our approach achieves competitive performance in emotional realism, accurate lip synchronization, and visual coherence, significantly surpassing existing techniques. Our implementation is available at https://github.com/laura990501/AUHead_ICLR
Cite
@article{arxiv.2602.09534,
title = {AUHead: Realistic Emotional Talking Head Generation via Action Units Control},
author = {Jiayi Lyu and Leigang Qu and Wenjing Zhang and Hanyu Jiang and Kai Liu and Zhenglin Zhou and Xiaobo Xia and Jian Xue and Tat-Seng Chua},
journal= {arXiv preprint arXiv:2602.09534},
year = {2026}
}
Comments
https://openreview.net/forum?id=dmzlAUkulz&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DICLR.cc%2F2026%2FConference%2FAuthors%23your-submissions) Accepted at the 14th International Conference on Learning Representations (ICLR 2026)