让多人互动动作更自然，通过交错学习捕捉真实场景中的动态协同。
InterSyn: Interleaved Learning for Dynamic Motion Synthesis in the Wild
- 采用交错学习策略，统一建模个人与多人交互行为。
- 生成动作与文本描述对齐度更高，且多样性优于现有方法。
- 适合虚拟人交互、游戏动画等需要真实多人协作的场景。
我们提出了一种名为InterSyn的新框架，旨在从融合了独处与多人动态的动作数据中生成逼真的交互动作。不同于以往将两者分开处理的方法，InterSyn采用交错学习策略，捕捉现实场景中自然的动态交互与细微协调。其核心包含两个模块：第一人称视角下联合建模独处与交互行为的交错交互生成（INS）模块，以及优化角色间相互动态、确保动作同步的相对协调精炼（REC）模块。实验表明，InterSyn生成的动作序列在文本到动作对齐度和多样性方面均优于当前主流方法，为鲁棒且自然的动作合成树立了新基准。未来将开源代码，推动该领域研究发展。
原文摘要 · Abstract (English)
We present Interleaved Learning for Motion Synthesis (InterSyn), a novel framework that targets the generation of realistic interaction motions by learning from integrated motions that consider both solo and multi-person dynamics. Unlike previous methods that treat these components separately, InterSyn employs an interleaved learning strategy to capture the natural, dynamic interactions and nuanced coordination inherent in real-world scenarios. Our framework comprises two key modules: the Interleaved Interaction Synthesis (INS) module, which jointly models solo and interactive behaviors in a unified paradigm from a first-person perspective to support multiple character interactions, and the Relative Coordination Refinement (REC) module, which refines mutual dynamics and ensures synchronized motions among characters. Experimental results show that the motion sequences generated by InterSyn exhibit higher text-to-motion alignment and improved diversity compared with recent methods, setting a new benchmark for robust and natural motion synthesis. Additionally, our code will be open-sourced in the future to promote further research and development in this area.
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