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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.10103 |
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| _version_ | 1866914256240246784 |
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| author | Wang, Lizhen Zhu, Yongming Ge, Zhipeng Zheng, Youwei Zhang, Longhao Hu, Tianshu Qin, Shiyang Luo, Mingshuang Zhang, Jiaxu Chen, Xin Wang, Yulong Zheng, Zerong Jiang, Jianwen Liang, Chao Chen, Weifeng Wang, Xing Zhang, Yuan Gao, Mingyuan |
| author_facet | Wang, Lizhen Zhu, Yongming Ge, Zhipeng Zheng, Youwei Zhang, Longhao Hu, Tianshu Qin, Shiyang Luo, Mingshuang Zhang, Jiaxu Chen, Xin Wang, Yulong Zheng, Zerong Jiang, Jianwen Liang, Chao Chen, Weifeng Wang, Xing Zhang, Yuan Gao, Mingyuan |
| contents | Interactive humanoid video generation aims to synthesize lifelike visual agents that can engage with humans through continuous and responsive video. Despite recent advances in video synthesis, existing methods often grapple with the trade-off between high-fidelity synthesis and real-time interaction requirements. In this paper, we propose FlowAct-R1, a framework specifically designed for real-time interactive humanoid video generation. Built upon a MMDiT architecture, FlowAct-R1 enables the streaming synthesis of video with arbitrary durations while maintaining low-latency responsiveness. We introduce a chunkwise diffusion forcing strategy, complemented by a novel self-forcing variant, to alleviate error accumulation and ensure long-term temporal consistency during continuous interaction. By leveraging efficient distillation and system-level optimizations, our framework achieves a stable 25fps at 480p resolution with a time-to-first-frame (TTFF) of only around 1.5 seconds. The proposed method provides holistic and fine-grained full-body control, enabling the agent to transition naturally between diverse behavioral states in interactive scenarios. Experimental results demonstrate that FlowAct-R1 achieves exceptional behavioral vividness and perceptual realism, while maintaining robust generalization across diverse character styles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_10103 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FlowAct-R1: Towards Interactive Humanoid Video Generation Wang, Lizhen Zhu, Yongming Ge, Zhipeng Zheng, Youwei Zhang, Longhao Hu, Tianshu Qin, Shiyang Luo, Mingshuang Zhang, Jiaxu Chen, Xin Wang, Yulong Zheng, Zerong Jiang, Jianwen Liang, Chao Chen, Weifeng Wang, Xing Zhang, Yuan Gao, Mingyuan Computer Vision and Pattern Recognition Artificial Intelligence Interactive humanoid video generation aims to synthesize lifelike visual agents that can engage with humans through continuous and responsive video. Despite recent advances in video synthesis, existing methods often grapple with the trade-off between high-fidelity synthesis and real-time interaction requirements. In this paper, we propose FlowAct-R1, a framework specifically designed for real-time interactive humanoid video generation. Built upon a MMDiT architecture, FlowAct-R1 enables the streaming synthesis of video with arbitrary durations while maintaining low-latency responsiveness. We introduce a chunkwise diffusion forcing strategy, complemented by a novel self-forcing variant, to alleviate error accumulation and ensure long-term temporal consistency during continuous interaction. By leveraging efficient distillation and system-level optimizations, our framework achieves a stable 25fps at 480p resolution with a time-to-first-frame (TTFF) of only around 1.5 seconds. The proposed method provides holistic and fine-grained full-body control, enabling the agent to transition naturally between diverse behavioral states in interactive scenarios. Experimental results demonstrate that FlowAct-R1 achieves exceptional behavioral vividness and perceptual realism, while maintaining robust generalization across diverse character styles. |
| title | FlowAct-R1: Towards Interactive Humanoid Video Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2601.10103 |