Meta's FAIR team has introduced audiovisual behavioral motion models aimed at enhancing human connection technology through realistic virtual interactions. These models can create fully embodied avatars in 2D and 3D, significantly improving telepresence technologies. Supporting this initiative, the Seamless Interaction Dataset comprises over 4,000 hours of diverse two-person interactions, serving as a resource for understanding human-like social behaviors. The Audio-Visual Dyadic Motion Models generate facial expressions and body gestures based on audio inputs, enabling virtual agents to engage in lifelike conversations with intricate dynamics of speech and gestures.
Meta's Audio-Visual Dyadic Motion Models utilize audio inputs from two individuals or language models to generate realistic facial expressions and body gestures, enhancing virtual interactions.
The Seamless Interaction Dataset features 4,000 hours of diverse human conversations, providing critical data for developing models that replicate natural social behaviors and interactions.
By modeling conversational dynamics, these innovations facilitate the creation of more relatable virtual avatars, capturing the intricate interplay of speech, gestures, and listening in real-time engagement.
Meta's advancements promise to revolutionize telepresence in virtual and augmented reality, allowing avatars to exhibit lifelike expressiveness that mimics human conversation and interaction.
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