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    We developed a new benchmark and datasets for generating conversational head movements in digital humans. This enables more realistic two-way interactions for virtual agents and social robots.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Artificial Intelligence

    Background:

    • Existing talking head generation focuses on one-way communication, limiting applications requiring interactive digital humans.
    • Synthesizing realistic two-way conversations requires modeling both speaking and listening behaviors.

    Purpose of the Study:

    • Introduce a novel benchmark and datasets (ViCo and ViCo-X) for conversation head generation.
    • Address the need for generating responsive and interactive interlocutor behaviors in face-to-face conversations.
    • Enable the creation of digital humans capable of multi-turn, two-way interactions.

    Main Methods:

    • Constructed two datasets: ViCo for sentence-level talking/listening and ViCo-X for multi-turn scenarios.
    • Defined three novel tasks: responsive listening, expressive talking, and integrated conversational head generation.
    • Proposed baseline solutions for each task to demonstrate feasibility.

    Main Results:

    • The proposed baseline methods successfully generate responsive and vivid conversational agents.
    • The generated agents can collaborate with humans to complete conversations.
    • The benchmark facilitates research in interactive digital human generation.

    Conclusions:

    • The developed benchmark and datasets advance the field of conversational AI and digital humans.
    • This work provides a foundation for creating more engaging and realistic virtual agents and social robots.
    • Future research can build upon these datasets and tasks to further improve interactive behavior synthesis.