Embracing Aleatoric Uncertainty: Generating Diverse 3D Human Motion

• URL: http://arxiv.org/abs/2508.20604v1
• Date: Thu, 28 Aug 2025 09:49:27 GMT
• Title: Embracing Aleatoric Uncertainty: Generating Diverse 3D Human Motion
• Authors: Zheng Qin, Yabing Wang, Minghui Yang, Sanping Zhou, Ming Yang, Le Wang,
• Abstract summary: We propose a simple yet effective text-to-motion generation method, textiti.e., Diverse-T2M.<n>Our method introduces uncertainty into the generation process, enabling the generation of highly diverse motions.<n>Our results on text-to-motion generation benchmark datasets(HumanML3D and KIT-ML) demonstrate that our method significantly enhances diversity while maintaining state-of-the-art performance in text consistency.
• Score: 47.7415368268124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating 3D human motions from text is a challenging yet valuable task. The key aspects of this task are ensuring text-motion consistency and achieving generation diversity. Although recent advancements have enabled the generation of precise and high-quality human motions from text, achieving diversity in the generated motions remains a significant challenge. In this paper, we aim to overcome the above challenge by designing a simple yet effective text-to-motion generation method, \textit{i.e.}, Diverse-T2M. Our method introduces uncertainty into the generation process, enabling the generation of highly diverse motions while preserving the semantic consistency of the text. Specifically, we propose a novel perspective that utilizes noise signals as carriers of diversity information in transformer-based methods, facilitating a explicit modeling of uncertainty. Moreover, we construct a latent space where text is projected into a continuous representation, instead of a rigid one-to-one mapping, and integrate a latent space sampler to introduce stochastic sampling into the generation process, thereby enhancing the diversity and uncertainty of the outputs. Our results on text-to-motion generation benchmark datasets~(HumanML3D and KIT-ML) demonstrate that our method significantly enhances diversity while maintaining state-of-the-art performance in text consistency.

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