IAM：身份感知的人体动作与形态联合生成

摘要

近年来，文本驱动人体运动生成技术的最新进展使得模型能够根据自然语言描述合成逼真的运动序列。然而，现有方法大多假设身份中立的运动，采用标准人体表征生成动作，忽略了身体形态对运动动态的显著影响。实际上，身体比例、质量分布和年龄等属性会显著影响动作的执行方式，忽视这种耦合关系往往导致物理不一致的运动生成。我们提出了一种身份感知运动生成框架，通过显式建模身体形态与运动动态的关联关系。该方法不依赖显式几何测量，而是采用自然语言描述和视觉线索等多模态信号表征身份信息。我们进一步引入了联合运动-形态生成范式，可同步合成运动序列与身体形状参数，使身份特征能直接调制运动动态。基于运动捕捉数据集和大规模真实场景视频的广泛实验表明，该方法在保持高运动质量的同时，显著提升了运动真实感与运动-身份一致性。项目页面：https://vjwq.github.io/IAM

English

Recent advances in text-driven human motion generation enable models to synthesize realistic motion sequences from natural language descriptions. However, most existing approaches assume identity-neutral motion and generate movements using a canonical body representation, ignoring the strong influence of body morphology on motion dynamics. In practice, attributes such as body proportions, mass distribution, and age significantly affect how actions are performed, and neglecting this coupling often leads to physically inconsistent motions. We propose an identity-aware motion generation framework that explicitly models the relationship between body morphology and motion dynamics. Instead of relying on explicit geometric measurements, identity is represented using multimodal signals, including natural language descriptions and visual cues. We further introduce a joint motion-shape generation paradigm that simultaneously synthesizes motion sequences and body shape parameters, allowing identity cues to directly modulate motion dynamics. Extensive experiments on motion capture datasets and large-scale in-the-wild videos demonstrate improved motion realism and motion-identity consistency while maintaining high motion quality. Project page: https://vjwq.github.io/IAM

IAM：身份感知的人体动作与形态联合生成

IAM: Identity-Aware Human Motion and Shape Joint Generation

摘要

Support