Recent progress in video diffusion models has markedly advanced character animation, which synthesizes motioned videos by animating a static identity image according to a driving video. Explicit methods represent motion using skeleton, DWPose or other explicit structured signals, but struggle to handle spatial mismatches and varying body scales. %proportions. Implicit methods, on the other hand, capture high-level implicit motion semantics directly from the driving video, but suffer from identity leakage and entanglement between motion and appearance. To address the above challenges, we propose a novel implicit motion representation that compresses per-frame motion into compact 1D motion tokens. This design relaxes strict spatial constraints inherent in 2D representations and effectively prevents identity information leakage from the motion video. Furthermore, we design a temporally consistent mask token-based retargeting module that enforces a temporal training bottleneck, mitigating interference from the source images' motion and improving retargeting consistency. Our methodology employs a three-stage training strategy to enhance the training efficiency and ensure high fidelity. Extensive experiments demonstrate that our implicit motion representation and the propose IM-Animation's generative capabilities are achieve superior or competitive performance compared with state-of-the-art methods.

翻译：近期视频扩散模型的进展显著推动了角色动画领域的发展，该领域通过根据驱动视频对静态身份图像进行动画处理来合成运动视频。显式方法使用骨架、DWPose或其他显式结构化信号来表示运动，但难以处理空间不匹配和变化的身体尺度。另一方面，隐式方法直接从驱动视频中捕获高级隐式运动语义，但存在身份泄漏以及运动与外观纠缠的问题。为解决上述挑战，我们提出了一种新颖的隐式运动表示方法，将每帧运动压缩为紧凑的一维运动标记。这种设计放松了二维表示中固有的严格空间约束，并有效防止了来自运动视频的身份信息泄漏。此外，我们设计了一个基于时间一致掩码标记的重定向模块，该模块施加了时间训练瓶颈，减轻了源图像运动的干扰，并提高了重定向的一致性。我们的方法采用三阶段训练策略以提高训练效率并确保高保真度。大量实验表明，与最先进的方法相比，我们的隐式运动表示以及所提出的IM-Animation的生成能力实现了优越或具有竞争力的性能。