Generating Attribute-Aware Human Motions from Textual Prompt
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908649554706432 |
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| author | Wang, Xinghan Xu, Kun Li, Fei Sheng, Cao Yu, Jiazhong Mu, Yadong |
| author_facet | Wang, Xinghan Xu, Kun Li, Fei Sheng, Cao Yu, Jiazhong Mu, Yadong |
| contents | Text-driven human motion generation has recently attracted considerable attention, allowing models to generate human motions based on textual descriptions. However, current methods neglect the influence of human attributes-such as age, gender, weight, and height-which are key factors shaping human motion patterns. This work represents a pilot exploration for bridging this gap. We conceptualize each motion as comprising both attribute information and action semantics, where textual descriptions align exclusively with action semantics. To achieve this, a new framework inspired by Structural Causal Models is proposed to decouple action semantics from human attributes, enabling text-to-semantics prediction and attribute-controlled generation. The resulting model is capable of generating attribute-aware motion aligned with the user's text and attribute inputs. For evaluation, we introduce a comprehensive dataset containing attribute annotations for text-motion pairs, setting the first benchmark for attribute-aware motion generation. Extensive experiments validate our model's effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21912 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generating Attribute-Aware Human Motions from Textual Prompt Wang, Xinghan Xu, Kun Li, Fei Sheng, Cao Yu, Jiazhong Mu, Yadong Computer Vision and Pattern Recognition Multimedia Text-driven human motion generation has recently attracted considerable attention, allowing models to generate human motions based on textual descriptions. However, current methods neglect the influence of human attributes-such as age, gender, weight, and height-which are key factors shaping human motion patterns. This work represents a pilot exploration for bridging this gap. We conceptualize each motion as comprising both attribute information and action semantics, where textual descriptions align exclusively with action semantics. To achieve this, a new framework inspired by Structural Causal Models is proposed to decouple action semantics from human attributes, enabling text-to-semantics prediction and attribute-controlled generation. The resulting model is capable of generating attribute-aware motion aligned with the user's text and attribute inputs. For evaluation, we introduce a comprehensive dataset containing attribute annotations for text-motion pairs, setting the first benchmark for attribute-aware motion generation. Extensive experiments validate our model's effectiveness. |
| title | Generating Attribute-Aware Human Motions from Textual Prompt |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2506.21912 |