AnyTalker: Scaling Multi-Person Talking Video Generation with Interactivity Refinement
Authors: name: Zhizhou Zhong; name: Yicheng Ji; name: Zhe Kong; name: Yiying Liu; name: Jiarui Wang; name: Jiasun Feng; name: Lupeng Liu; name: Xiangyi Wang; name: Yanjia Li; name: Yuqing She; name: Ying Qin; name: Huan Li; name: Shuiyang Mao; name: Wei Liu; name: Wenhan Luo
Published: 2025-11-28 18:59:01

Abstract: 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.