DualTalk: Dual-Speaker Interaction for 3D Talking Head Conversations
This work addresses the challenge of unnatural interactions in 3D talking head generation for applications like virtual avatars and human-computer interaction, though it is incremental as it builds on existing models by adding dual-speaker dynamics.
The paper tackles the problem of generating realistic 3D talking heads in conversations by addressing the lack of seamless switching between speaking and listening roles, proposing DualTalk to handle both behaviors and showing significant improvements in naturalness and expressiveness.
In face-to-face conversations, individuals need to switch between speaking and listening roles seamlessly. Existing 3D talking head generation models focus solely on speaking or listening, neglecting the natural dynamics of interactive conversation, which leads to unnatural interactions and awkward transitions. To address this issue, we propose a new task -- multi-round dual-speaker interaction for 3D talking head generation -- which requires models to handle and generate both speaking and listening behaviors in continuous conversation. To solve this task, we introduce DualTalk, a novel unified framework that integrates the dynamic behaviors of speakers and listeners to simulate realistic and coherent dialogue interactions. This framework not only synthesizes lifelike talking heads when speaking but also generates continuous and vivid non-verbal feedback when listening, effectively capturing the interplay between the roles. We also create a new dataset featuring 50 hours of multi-round conversations with over 1,000 characters, where participants continuously switch between speaking and listening roles. Extensive experiments demonstrate that our method significantly enhances the naturalness and expressiveness of 3D talking heads in dual-speaker conversations. We recommend watching the supplementary video: https://ziqiaopeng.github.io/dualtalk.