Paper
ReactMotion: Generating Reactive Listener Motions from Speaker Utterance
Authors
Cheng Luo, Bizhu Wu, Bing Li, Jianfeng Ren, Ruibin Bai, Rong Qu, Linlin Shen, Bernard Ghanem
Abstract
In this paper, we introduce a new task, Reactive Listener Motion Generation from Speaker Utterance, which aims to generate naturalistic listener body motions that appropriately respond to a speaker's utterance. However, modeling such nonverbal listener behaviors remains underexplored and challenging due to the inherently non-deterministic nature of human reactions. To facilitate this task, we present ReactMotionNet, a large-scale dataset that pairs speaker utterances with multiple candidate listener motions annotated with varying degrees of appropriateness. This dataset design explicitly captures the one-to-many nature of listener behavior and provides supervision beyond a single ground-truth motion. Building on this dataset design, we develop preference-oriented evaluation protocols tailored to evaluate reactive appropriateness, where conventional motion metrics focusing on input-motion alignment ignore. We further propose ReactMotion, a unified generative framework that jointly models text, audio, emotion, and motion, and is trained with preference-based objectives to encourage both appropriate and diverse listener responses. Extensive experiments show that ReactMotion outperforms retrieval baselines and cascaded LLM-based pipelines, generating more natural, diverse, and appropriate listener motions.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.15083v1</id>\n <title>ReactMotion: Generating Reactive Listener Motions from Speaker Utterance</title>\n <updated>2026-03-16T10:37:42Z</updated>\n <link href='https://arxiv.org/abs/2603.15083v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.15083v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>In this paper, we introduce a new task, Reactive Listener Motion Generation from Speaker Utterance, which aims to generate naturalistic listener body motions that appropriately respond to a speaker's utterance. However, modeling such nonverbal listener behaviors remains underexplored and challenging due to the inherently non-deterministic nature of human reactions. To facilitate this task, we present ReactMotionNet, a large-scale dataset that pairs speaker utterances with multiple candidate listener motions annotated with varying degrees of appropriateness. This dataset design explicitly captures the one-to-many nature of listener behavior and provides supervision beyond a single ground-truth motion. Building on this dataset design, we develop preference-oriented evaluation protocols tailored to evaluate reactive appropriateness, where conventional motion metrics focusing on input-motion alignment ignore. We further propose ReactMotion, a unified generative framework that jointly models text, audio, emotion, and motion, and is trained with preference-based objectives to encourage both appropriate and diverse listener responses. Extensive experiments show that ReactMotion outperforms retrieval baselines and cascaded LLM-based pipelines, generating more natural, diverse, and appropriate listener motions.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.HC'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.MM'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.SD'/>\n <published>2026-03-16T10:37:42Z</published>\n <arxiv:comment>42 pages, 11 tables, 8 figures</arxiv:comment>\n <arxiv:primary_category term='cs.CV'/>\n <author>\n <name>Cheng Luo</name>\n </author>\n <author>\n <name>Bizhu Wu</name>\n </author>\n <author>\n <name>Bing Li</name>\n </author>\n <author>\n <name>Jianfeng Ren</name>\n </author>\n <author>\n <name>Ruibin Bai</name>\n </author>\n <author>\n <name>Rong Qu</name>\n </author>\n <author>\n <name>Linlin Shen</name>\n </author>\n <author>\n <name>Bernard Ghanem</name>\n </author>\n </entry>"
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