LaughTalk: Expressive 3D Talking Head Generation with Laughter
Kim Sung-Bin Lee Hyun Da Hye Hong Suekyeong Nam Janghoon Ju Tae-Hyun Oh
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 social 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.