OmniResponse: Online Multimodal Conversational Response Generation in
  Dyadic Interactions
Authors: name: Cheng Luo, name: Jianghui Wang, name: Bing Li, name: Siyang Song, name: Bernard Ghanem

In this paper, we introduce Online Multimodal Conversational Response
Generation (OMCRG), a novel task that aims to online generate synchronized
verbal and non-verbal listener feedback, conditioned on the speaker's
multimodal input. OMCRG reflects natural dyadic interactions and poses new
challenges in achieving synchronization between the generated audio and facial
responses of the listener. To address these challenges, we innovatively
introduce text as an intermediate modality to bridge the audio and facial
responses. We hence propose OmniResponse, a Multimodal Large Language Model
(MLLM) that autoregressively generates high-quality multi-modal listener
responses. OmniResponse leverages a pretrained LLM enhanced with two novel
components: Chrono-Text, which temporally anchors generated text tokens, and
TempoVoice, a controllable online TTS module that produces speech synchronized
with facial reactions. To support further OMCRG research, we present
ResponseNet, a new dataset comprising 696 high-quality dyadic interactions
featuring synchronized split-screen videos, multichannel audio, transcripts,
and facial behavior annotations. Comprehensive evaluations conducted on
ResponseNet demonstrate that OmniResponse significantly outperforms baseline
models in terms of semantic speech content, audio-visual synchronization, and
generation quality.