Abstract
Existing stylized motion generation models have shown their remarkable ability to understand specific style information from the style motion, and insert it into the content motion. However, capturing intra-style diversity, where a single style should correspond to diverse motion variations, remains a significant challenge. In this paper, we propose a clustering-based framework, ClusterStyle, to address this limitation. Instead of learning an unstructured embedding from each style motion, we leverage a set of prototypes to effectively model diverse style patterns across motions belonging to the same style category. We consider two types of style diversity: global-level diversity among style motions of the same category, and local-level diversity within the temporal dynamics of motion sequences. These components jointly shape two structured style embedding spaces, i.e., global and local, optimized via alignment with non-learnable prototype anchors. Furthermore, we augment the pretrained text-to-motion generation model with the Stylistic Modulation Adapter (SMA) to integrate the style features. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art models in stylized motion generation and motion style transfer.
Abstract (translated)
现有的风格化运动生成模型已经展示了其从风格动作中理解特定风格信息，并将其插入内容运动的卓越能力。然而，捕捉跨风格多样性（即单一风格应当对应多种多样的运动变化）仍然是一个重大挑战。在本文中，我们提出了一种基于聚类的框架ClusterStyle来解决这一限制。该方法不再从每个风格化的动作学习无结构嵌入，而是利用一组原型有效建模同一类别内不同运动中的多样风格模式。我们考虑两种类型的风格多样性：同类风格动作之间的全局层次多样性以及运动序列时间动态内的局部层次多样性。这些组成部分共同塑造了两个有组织的风格嵌入空间，即全局和局部，并通过与非可学习的原型锚点对齐进行优化。 此外，我们将预训练的文本到运动生成模型用带有风格化调制适配器（SMA）增强，以整合风格特征。广泛的实验表明，我们的方法在风格化动作生成和动作风格转换方面超过了现有的最先进的模型性能。
URL
https://arxiv.org/abs/2512.02453
