GLARE: Generating Listening Heads with Appropriate Reactions
While talking head generation has advanced rapidly, generating natural listener behavior in dyadic conversations, which know when to react, how to react, and with what type of response, remains underexplored. Existing dyadic datasets lack fine-grained listener reaction annotations, and prevailing evaluation metrics inherited from talking-head and video generation measure visual realism rather than whether a listener reacted appropriately. We address these gaps along three aspects. First, we curate a listening-head-specific dataset built from RealTalk and Seamless Interaction, comprising approximately 147 hours of paired speaker-listener videos with 64,557 event-level reaction annotations ...
| Authors | Zikai Liao, Yumin Suh, Yi Ouyang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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