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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2605.14731 |
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| _version_ | 1866909043450183680 |
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| author | Zhan, Xiaoyu Fu, Xinyu Yang, Chenghao Zhang, Xiaohong Fu, Dongjie Fang, Pengcheng Sun, Tengjiao Cai, Xiaohao Kim, Hansung Li, Yuanqi Guo, Jie Guo, Yanwen |
| author_facet | Zhan, Xiaoyu Fu, Xinyu Yang, Chenghao Zhang, Xiaohong Fu, Dongjie Fang, Pengcheng Sun, Tengjiao Cai, Xiaohao Kim, Hansung Li, Yuanqi Guo, Jie Guo, Yanwen |
| contents | Speech-driven gestures and facial animations are fundamental to expressive digital avatars in games, virtual production, and interactive media. However, existing methods are either limited to a single modality for audio motion alignment, failing to fully utilize the potential of massive human motion data, or are constrained by the representation ability and throughput of multimodal models, which makes it difficult to achieve high-quality motion generation or real-time performance. We present UMo, a unified sparse motion modeling architecture for real-time co-speech avatars, which processes text, audio, and motion tokens within a unified formulation. Leveraging a spatially sparse Mixture-of-Experts framework and a temporally sparse, keyframe-centric design, UMo efficiently performs real-time dense reconstruction, enabling temporally coherent and high-fidelity animation generation for both facial expressions and gestures. Furthermore, we implement a multi-stage training strategy with targeted audio augmentation to enhance acoustic diversity and semantic consistency. Consequently, UMo preserves fine-grained speech-motion alignment even under strict latency constraints. Extensive quantitative and qualitative evaluations show that UMo achieves better output quality under low latency and real-time performance constraints, offering a practical solution for high-fidelity real-time co-speech avatars. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14731 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | UMo: Unified Sparse Motion Modeling for Real-Time Co-Speech Avatars Zhan, Xiaoyu Fu, Xinyu Yang, Chenghao Zhang, Xiaohong Fu, Dongjie Fang, Pengcheng Sun, Tengjiao Cai, Xiaohao Kim, Hansung Li, Yuanqi Guo, Jie Guo, Yanwen Graphics Computer Vision and Pattern Recognition Sound Speech-driven gestures and facial animations are fundamental to expressive digital avatars in games, virtual production, and interactive media. However, existing methods are either limited to a single modality for audio motion alignment, failing to fully utilize the potential of massive human motion data, or are constrained by the representation ability and throughput of multimodal models, which makes it difficult to achieve high-quality motion generation or real-time performance. We present UMo, a unified sparse motion modeling architecture for real-time co-speech avatars, which processes text, audio, and motion tokens within a unified formulation. Leveraging a spatially sparse Mixture-of-Experts framework and a temporally sparse, keyframe-centric design, UMo efficiently performs real-time dense reconstruction, enabling temporally coherent and high-fidelity animation generation for both facial expressions and gestures. Furthermore, we implement a multi-stage training strategy with targeted audio augmentation to enhance acoustic diversity and semantic consistency. Consequently, UMo preserves fine-grained speech-motion alignment even under strict latency constraints. Extensive quantitative and qualitative evaluations show that UMo achieves better output quality under low latency and real-time performance constraints, offering a practical solution for high-fidelity real-time co-speech avatars. |
| title | UMo: Unified Sparse Motion Modeling for Real-Time Co-Speech Avatars |
| topic | Graphics Computer Vision and Pattern Recognition Sound |
| url | https://arxiv.org/abs/2605.14731 |