Multi-human Interactive Talking Dataset
Authors: name: Zeyu Zhu, name: Weijia Wu, name: Mike Zheng Shou

Existing studies on talking video generation have predominantly focused on
single-person monologues or isolated facial animations, limiting their
applicability to realistic multi-human interactions. To bridge this gap, we
introduce MIT, a large-scale dataset specifically designed for multi-human
talking video generation. To this end, we develop an automatic pipeline that
collects and annotates multi-person conversational videos. The resulting
dataset comprises 12 hours of high-resolution footage, each featuring two to
four speakers, with fine-grained annotations of body poses and speech
interactions. It captures natural conversational dynamics in multi-speaker
scenario, offering a rich resource for studying interactive visual behaviors.
To demonstrate the potential of MIT, we furthur propose CovOG, a baseline model
for this novel task. It integrates a Multi-Human Pose Encoder (MPE) to handle
varying numbers of speakers by aggregating individual pose embeddings, and an
Interactive Audio Driver (IAD) to modulate head dynamics based on
speaker-specific audio features. Together, these components showcase the
feasibility and challenges of generating realistic multi-human talking videos,
establishing MIT as a valuable benchmark for future research. The code is
avalibale at: https://github.com/showlab/Multi-human-Talking-Video-Dataset.