Embracing Aleatoric Uncertainty: Generating Diverse 3D Human Motion
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915467993546752 |
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| author | Qin, Zheng Wang, Yabing Yang, Minghui Zhou, Sanping Yang, Ming Wang, Le |
| author_facet | Qin, Zheng Wang, Yabing Yang, Minghui Zhou, Sanping Yang, Ming Wang, Le |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20604 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Embracing Aleatoric Uncertainty: Generating Diverse 3D Human Motion Qin, Zheng Wang, Yabing Yang, Minghui Zhou, Sanping Yang, Ming Wang, Le Computer Vision and Pattern Recognition 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. |
| title | Embracing Aleatoric Uncertainty: Generating Diverse 3D Human Motion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.20604 |