Recently, multi-person video generation has started to gain prominence. While a few preliminary works have explored audio-driven multi-person talking video generation, they often face challenges due to the high costs of diverse multi-person data collection and the difficulty of driving multiple identities with coherent interactivity. To address these challenges, we propose AnyTalker, a multi-person generation framework that features an extensible multi-stream processing architecture. Specifically, we extend Diffusion Transformer's attention block with a novel identity-aware attention mechanism that iteratively processes identity-audio pairs, allowing arbitrary scaling of drivable identities. Besides, training multi-person generative models demands massive multi-person data. Our proposed training pipeline depends solely on single-person videos to learn multi-person speaking patterns and refines interactivity with only a few real multi-person clips. Furthermore, we contribute a targeted metric and dataset designed to evaluate the naturalness and interactivity of the generated multi-person videos. Extensive experiments demonstrate that AnyTalker achieves remarkable lip synchronization, visual quality, and natural interactivity, striking a favorable balance between data costs and identity scalability.
@article{arxiv.2511.23475,
title = {AnyTalker: Scaling Multi-Person Talking Video Generation with Interactivity Refinement},
author = {Zhizhou Zhong and Yicheng Ji and Zhe Kong and Yiying Liu and Jiarui Wang and Jiasun Feng and Lupeng Liu and Xiangyi Wang and Yanjia Li and Yuqing She and Ying Qin and Huan Li and Shuiyang Mao and Wei Liu and Wenhan Luo},
journal= {arXiv preprint arXiv:2511.23475},
year = {2025}
}