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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.12662 |
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| _version_ | 1866912712904146944 |
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| author | Huang, Hongbin Li, Junwei Xie, Tianxin Li, Zhuang Weng, Cekai Yang, Yaodong Luo, Yue Liu, Li Tang, Jing Shao, Zhijing Wang, Zeyu |
| author_facet | Huang, Hongbin Li, Junwei Xie, Tianxin Li, Zhuang Weng, Cekai Yang, Yaodong Luo, Yue Liu, Li Tang, Jing Shao, Zhijing Wang, Zeyu |
| contents | High-fidelity digital humans are increasingly used in interactive applications, yet achieving both visual realism and real-time responsiveness remains a major challenge. We present a high-fidelity, real-time conversational digital human system that seamlessly combines a visually realistic 3D avatar, persona-driven expressive speech synthesis, and knowledge-grounded dialogue generation. To support natural and timely interaction, we introduce an asynchronous execution pipeline that coordinates multi-modal components with minimal latency. The system supports advanced features such as wake word detection, emotionally expressive prosody, and highly accurate, context-aware response generation. It leverages novel retrieval-augmented methods, including history augmentation to maintain conversational flow and intent-based routing for efficient knowledge access. Together, these components form an integrated system that enables responsive and believable digital humans, suitable for immersive applications in communication, education, and entertainment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12662 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hi-Reco: High-Fidelity Real-Time Conversational Digital Humans Huang, Hongbin Li, Junwei Xie, Tianxin Li, Zhuang Weng, Cekai Yang, Yaodong Luo, Yue Liu, Li Tang, Jing Shao, Zhijing Wang, Zeyu Computer Vision and Pattern Recognition High-fidelity digital humans are increasingly used in interactive applications, yet achieving both visual realism and real-time responsiveness remains a major challenge. We present a high-fidelity, real-time conversational digital human system that seamlessly combines a visually realistic 3D avatar, persona-driven expressive speech synthesis, and knowledge-grounded dialogue generation. To support natural and timely interaction, we introduce an asynchronous execution pipeline that coordinates multi-modal components with minimal latency. The system supports advanced features such as wake word detection, emotionally expressive prosody, and highly accurate, context-aware response generation. It leverages novel retrieval-augmented methods, including history augmentation to maintain conversational flow and intent-based routing for efficient knowledge access. Together, these components form an integrated system that enables responsive and believable digital humans, suitable for immersive applications in communication, education, and entertainment. |
| title | Hi-Reco: High-Fidelity Real-Time Conversational Digital Humans |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.12662 |