Consistent pose-driven character animation has achieved remarkable progress in single-character scenarios. However, extending these advances to multi-character settings is non-trivial, especially when position swap is involved. Beyond mere scaling, the core challenge lies in enforcing correct Identity Correspondence (IC) between characters in reference and generated frames. To address this, we introduce EverybodyDance, a systematic solution targeting IC correctness in multi-character animation. EverybodyDance is built around the Identity Matching Graph (IMG), which models characters in the generated and reference frames as two node sets in a weighted complete bipartite graph. Edge weights, computed via our proposed Mask-Query Attention (MQA), quantify the affinity between each pair of characters. Our key insight is to formalize IC correctness as a graph structural metric and to optimize it during training. We also propose a series of targeted strategies tailored for multi-character animation, including identity-embedded guidance, a multi-scale matching strategy, and pre-classified sampling, which work synergistically. Finally, to evaluate IC performance, we curate the Identity Correspondence Evaluation benchmark, dedicated to multi-character IC correctness. Extensive experiments demonstrate that EverybodyDance substantially outperforms state-of-the-art baselines in both IC and visual fidelity.

翻译：基于姿态驱动的单角色动画一致性已取得显著进展。然而，将此类方法扩展至多角色场景并非易事，尤其在涉及角色位置交换时。除规模扩展外，核心挑战在于确保参考帧与生成帧中角色间的正确身份对应关系。为此，我们提出EverybodyDance——一个针对多角色动画中身份对应关系正确性的系统性解决方案。该方法围绕身份匹配图构建，该图将生成帧与参考帧中的角色建模为加权完全二分图中的两个节点集。通过我们提出的掩码查询注意力机制计算边权重，以量化每对角色间的亲和度。我们的核心洞见是将身份对应正确性形式化为图结构度量指标，并在训练过程中进行优化。同时，我们提出一系列针对多角色动画的定制策略，包括身份嵌入引导、多尺度匹配策略及预分类采样，这些策略具有协同增强效应。最后，为评估身份对应性能，我们构建了专门用于多角色身份对应正确性评估的基准数据集。大量实验表明，EverybodyDance在身份对应准确性与视觉保真度方面均显著优于现有先进基线方法。