InterActHuman: Multi-Concept Human Animation with Layout-Aligned Audio Conditions
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911485250240512 |
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| author | Wang, Zhenzhi Yang, Jiaqi Jiang, Jianwen Liang, Chao Lin, Gaojie Zheng, Zerong Yang, Ceyuan Zhang, Yuan Gao, Mingyuan Lin, Dahua |
| author_facet | Wang, Zhenzhi Yang, Jiaqi Jiang, Jianwen Liang, Chao Lin, Gaojie Zheng, Zerong Yang, Ceyuan Zhang, Yuan Gao, Mingyuan Lin, Dahua |
| contents | End-to-end human animation with rich multi-modal conditions, e.g., text, image and audio has achieved remarkable advancements in recent years. However, most existing methods could only animate a single subject and inject conditions in a global manner, ignoring scenarios where multiple concepts could appear in the same video with rich human-human interactions and human-object interactions. Such a global assumption prevents precise and per-identity control of multiple concepts including humans and objects, therefore hinders applications. In this work, we discard the single-entity assumption and introduce a novel framework that enforces strong, region-specific binding of conditions from modalities to each identity's spatiotemporal footprint. Given reference images of multiple concepts, our method could automatically infer layout information by leveraging a mask predictor to match appearance cues between the denoised video and each reference appearance. Furthermore, we inject local audio condition into its corresponding region to ensure layout-aligned modality matching in an iterative manner. This design enables the high-quality generation of human dialogue videos between two to three people or video customization from multiple reference images. Empirical results and ablation studies validate the effectiveness of our explicit layout control for multi-modal conditions compared to implicit counterparts and other existing methods. Video demos are available at https://zhenzhiwang.github.io/interacthuman/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09984 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | InterActHuman: Multi-Concept Human Animation with Layout-Aligned Audio Conditions Wang, Zhenzhi Yang, Jiaqi Jiang, Jianwen Liang, Chao Lin, Gaojie Zheng, Zerong Yang, Ceyuan Zhang, Yuan Gao, Mingyuan Lin, Dahua Computer Vision and Pattern Recognition Artificial Intelligence Sound End-to-end human animation with rich multi-modal conditions, e.g., text, image and audio has achieved remarkable advancements in recent years. However, most existing methods could only animate a single subject and inject conditions in a global manner, ignoring scenarios where multiple concepts could appear in the same video with rich human-human interactions and human-object interactions. Such a global assumption prevents precise and per-identity control of multiple concepts including humans and objects, therefore hinders applications. In this work, we discard the single-entity assumption and introduce a novel framework that enforces strong, region-specific binding of conditions from modalities to each identity's spatiotemporal footprint. Given reference images of multiple concepts, our method could automatically infer layout information by leveraging a mask predictor to match appearance cues between the denoised video and each reference appearance. Furthermore, we inject local audio condition into its corresponding region to ensure layout-aligned modality matching in an iterative manner. This design enables the high-quality generation of human dialogue videos between two to three people or video customization from multiple reference images. Empirical results and ablation studies validate the effectiveness of our explicit layout control for multi-modal conditions compared to implicit counterparts and other existing methods. Video demos are available at https://zhenzhiwang.github.io/interacthuman/ |
| title | InterActHuman: Multi-Concept Human Animation with Layout-Aligned Audio Conditions |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Sound |
| url | https://arxiv.org/abs/2506.09984 |