ClusterStyle: 通过原型聚类建模内在风格多样性以实现风格化动作生成
ClusterStyle: Modeling Intra-Style Diversity with Prototypical Clustering for Stylized Motion Generation
AI总结：
本文提出ClusterStyle框架，通过原型聚类建模风格内多样性，生成结构化风格嵌入空间，提升风格化动作生成和迁移性能。
摘要：
现有风格化动作生成模型在理解特定风格信息并插入内容动作方面表现出色，但捕捉单个风格对应的多样化动作变化仍具挑战性。本文提出ClusterStyle框架，通过原型聚类建模同一风格类别中动作的多样化模式。考虑全局和局部层面的风格多样性，共同塑造全局和局部结构化风格嵌入空间，通过与非学习原型锚点对齐进行优化。此外，通过Stylistic Modulation Adapter (SMA)增强预训练文本到动作生成模型，整合风格特征。大量实验表明，本文方法在风格化动作生成和动作风格迁移中优于现有最先进模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
