We study the problem of directly deriving an initial human reenactment from a monocular video of a non-human character. Our goal is not to reconstruct the source character itself but to reinterpret its motion as a plausible and editable human performance for downstream animation authoring. This task is challenging because existing video-based motion capture methods are largely restricted to human-centric structural spaces, while motion retargeting methods typically require structured 3D source motions and known source topologies. Our key insight is that sparse local articulated motion cues can preserve essential dynamics across large structural differences, providing a stable bridge from character video to human reenactment. Based on this observation, we propose AnyAct, which formulates character-video-driven human reenactment as conditional human motion generation from transferable sparse local 2D articulated motion. To make this practical, we introduce three key designs: human-motion-only supervision via augmented 3D-to-2D projection, progressive 3D-to-2D training to alleviate conditioning ambiguity, and global-local motion decoupling for reliable local motion control. We further construct a benchmark primarily covering diverse non-human character videos. Experiments on the benchmark show that AnyAct produces high-fidelity initial human reenactments that preserve the essential dynamics of the characters in reference videos, and further ablation studies validate the effectiveness of its core designs.

翻译：我们研究了从非人类角色的单目视频直接推导出初始人类动作再现的问题。我们的目标并非重建源角色本身，而是将其动作重新诠释为合理且可编辑的人类表演，以供下游动画创作使用。这一任务充满挑战，因为现有的基于视频的动作捕捉方法大多局限于以人为中心的结构空间，而动作重定向方法通常需要结构化的三维源动作和已知的源拓扑结构。我们的关键洞察在于，稀疏的局部铰接动作线索可以跨越巨大的结构差异保留本质动态，为从角色视频到人类动作再现提供稳定的桥梁。基于这一观察，我们提出了AnyAct方法，它将角色视频驱动的人类动作再现形式化为从可迁移的稀疏局部二维铰接动作进行的条件化人类动作生成。为实现这一目标，我们引入了三个关键设计：通过增强的三维到二维投影实现仅基于人类动作的监督、渐进式三维到二维训练以缓解条件模糊性、以及全局-局部动作解耦以实现可靠的局部动作控制。我们进一步构建了一个主要涵盖多样化非人类角色视频的基准测试。在该基准上的实验表明，AnyAct能够生成高保真的初始人类动作再现，保留参考视频中角色的本质动态，而进一步的消融研究验证了其核心设计的有效性。