Human motion style transfer allows characters to appear less rigidity and more realism with specific style. Traditional arbitrary image style transfer typically process mean and variance which is proved effective. Meanwhile, similar methods have been adapted for motion style transfer. However, due to the fundamental differences between images and motion, relying on mean and variance is insufficient to fully capture the complex dynamic patterns and spatiotemporal coherence properties of motion data. Building upon this, our key insight is to bring two more coefficient, skewness and kurtosis, into the analysis of motion style. Specifically, we propose a novel Adaptive Statistics Fusor (AStF) which consists of Style Disentanglement Module (SDM) and High-Order Multi-Statistics Attention (HOS-Attn). We trained our AStF in conjunction with a Motion Consistency Regularization (MCR) discriminator. Experimental results show that, by providing a more comprehensive model of the spatiotemporal statistical patterns inherent in dynamic styles, our proposed AStF shows proficiency superiority in motion style transfers over state-of-the-arts. Our code and model are available at https://github.com/CHMimilanlan/AStF.

翻译：人体运动风格迁移可使角色呈现更低的僵硬感和更高的真实感，并赋予其特定风格。传统的任意图像风格迁移通常处理均值与方差，该方法已被证明是有效的。同时，类似方法已被应用于运动风格迁移。然而，由于图像与运动数据存在本质差异，仅依赖均值与方差不足以充分捕捉运动数据中复杂的动态模式及时空相干特性。基于此，我们的核心见解是引入偏度与峰度这两个额外统计量来分析运动风格。具体而言，我们提出了一种新颖的自适应统计融合器（AStF），其包含风格解耦模块（SDM）与高阶多统计量注意力机制（HOS-Attn）。我们结合运动一致性正则化（MCR）判别器对AStF进行训练。实验结果表明，通过更全面地建模动态风格中固有的时空统计模式，我们提出的AStF在运动风格迁移任务中展现出优于现有先进方法的性能优势。代码与模型已发布于 https://github.com/CHMimilanlan/AStF。