PersonaBooth: Personalized Text-to-Motion Generation
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913749208662016 |
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| author | Kim, Boeun Jeong, Hea In Sung, JungHoon Cheng, Yihua Lee, Jeongmin Chang, Ju Yong Choi, Sang-Il Choi, Younggeun Shin, Saim Kim, Jungho Chang, Hyung Jin |
| author_facet | Kim, Boeun Jeong, Hea In Sung, JungHoon Cheng, Yihua Lee, Jeongmin Chang, Ju Yong Choi, Sang-Il Choi, Younggeun Shin, Saim Kim, Jungho Chang, Hyung Jin |
| contents | This paper introduces Motion Personalization, a new task that generates personalized motions aligned with text descriptions using several basic motions containing Persona. To support this novel task, we introduce a new large-scale motion dataset called PerMo (PersonaMotion), which captures the unique personas of multiple actors. We also propose a multi-modal finetuning method of a pretrained motion diffusion model called PersonaBooth. PersonaBooth addresses two main challenges: i) A significant distribution gap between the persona-focused PerMo dataset and the pretraining datasets, which lack persona-specific data, and ii) the difficulty of capturing a consistent persona from the motions vary in content (action type). To tackle the dataset distribution gap, we introduce a persona token to accept new persona features and perform multi-modal adaptation for both text and visuals during finetuning. To capture a consistent persona, we incorporate a contrastive learning technique to enhance intra-cohesion among samples with the same persona. Furthermore, we introduce a context-aware fusion mechanism to maximize the integration of persona cues from multiple input motions. PersonaBooth outperforms state-of-the-art motion style transfer methods, establishing a new benchmark for motion personalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07390 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PersonaBooth: Personalized Text-to-Motion Generation Kim, Boeun Jeong, Hea In Sung, JungHoon Cheng, Yihua Lee, Jeongmin Chang, Ju Yong Choi, Sang-Il Choi, Younggeun Shin, Saim Kim, Jungho Chang, Hyung Jin Computer Vision and Pattern Recognition This paper introduces Motion Personalization, a new task that generates personalized motions aligned with text descriptions using several basic motions containing Persona. To support this novel task, we introduce a new large-scale motion dataset called PerMo (PersonaMotion), which captures the unique personas of multiple actors. We also propose a multi-modal finetuning method of a pretrained motion diffusion model called PersonaBooth. PersonaBooth addresses two main challenges: i) A significant distribution gap between the persona-focused PerMo dataset and the pretraining datasets, which lack persona-specific data, and ii) the difficulty of capturing a consistent persona from the motions vary in content (action type). To tackle the dataset distribution gap, we introduce a persona token to accept new persona features and perform multi-modal adaptation for both text and visuals during finetuning. To capture a consistent persona, we incorporate a contrastive learning technique to enhance intra-cohesion among samples with the same persona. Furthermore, we introduce a context-aware fusion mechanism to maximize the integration of persona cues from multiple input motions. PersonaBooth outperforms state-of-the-art motion style transfer methods, establishing a new benchmark for motion personalization. |
| title | PersonaBooth: Personalized Text-to-Motion Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.07390 |