PolySLGen: 多人数対話におけるオンライン多感覚発話・傾聴反応生成
PolySLGen: Online Multimodal Speaking-Listening Reaction Generation in Polyadic Interaction
多人数対話での自然な反応生成のため、過去の会話と動作から対象者の発話または傾聴反応（音声・身体動作・発話状態スコア）をオンライン生成するフレームワークを提案した。
著者: Zhi-Yi Lin, Thomas Markhorst, Jouh Yeong Chew, Xucong Zhang
分類: cs.CV
原文アブストラクト
Human-like multimodal reaction generation is essential for natural group interactions between humans and embodied AI. However, existing approaches are limited to single-modality or speaking-only responses in dyadic interactions, making them unsuitable for realistic social scenarios. Many also overlook nonverbal cues and complex dynamics of polyadic interactions, both critical for engagement and conversational coherence. In this work, we present PolySLGen, an online framework for Polyadic multimodal Speaking and Listening reaction Generation. Given past conversation and motion from all participants, PolySLGen generates a future speaking or listening reaction for a target participant, including speech, body motion, and speaking state score. To model group interactions effectively, we propose a pose fusion module and a social cue encoder that jointly aggregate motion and social signals from the group. Extensive experiments, along with quantitative and qualitative evaluations, show that PolySLGen produces contextually appropriate and temporally coherent multi-modal reactions, outperforming several adapted and state-of-the-art baselines in motion quality, motion-speech alignment, speaking state prediction, and human-perceived realism.