Laughter is a unique expression, essential to affirmative social interactions of humans. Although current 3D talking head generation methods produce convincing verbal articulations, they often fail to capture the vitality and subtleties of laughter and smiles despite their importance in context. In this paper, we introduce a novel task to generate 3D talking heads capable of both articulate speech and authentic laughter. Our newly curated dataset comprises 2D laughing videos paired with pseudo-annotated and human-validated 3D FLAME parameters and vertices. Given our proposed dataset, we present a strong baseline with a two-stage training scheme: the model first learns to talk and then acquires the ability to express laughter. Extensive experiments demonstrate that our method performs favorably compared to existing approaches in both talking head generation and expressing laughter signals. We further explore potential applications on top of our proposed method for rigging realistic avatars.
@inproceeding{sung2024laughtalk,
title={LaughTalk: Expressive 3D Talking Head Generation with Laughter},
author={Sung-Bin, Kim and Hyun, Lee and Hong, Da Hye and Nam, Suekyeong and Ju, Janghoon and Oh, Tae-Hyun},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={6404--6413},
year={2024}
}This work was supported by IITP grant funded by Korea government (MSIT) (No.2021-0-02068, Artificial Intelligence Innovation Hub; No.RS-2023-00225630, Development of Artificial Intelligence for Text-based 3D Movie Generation; No.2022-0-00290, Visual Intelligence for Space-Time Understanding and Generation based on Multi-layered Visual Common Sense; No.2022-0-00124, Development of Artificial Intelligence Technology for Self-Improving Competency-Aware Learning Capabilities).