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ARAşTıRMA · BILGISAYARLı GöRü arXiv:2609.35728 28 Eyl 2026 · v1

FlowAct-R2: Beyond Talking Avatar via Streaming Multimodal References and Proactive Agent Planning

Ziyao Huang, Zhengkun Rong, Shiyang Qin, Shuang Liang, Wentao Hu, +3 yazar

YAYIN:28 Eyl 2026 ALAN:cs.CV OKUMA:36

Özet

We present FlowAct-R2, a framework for interactive humanoid video generation that combines continuous multimodal control with proactive agent planning. Our method consists of two coupled components. First, a Streaming Multimodal Reference Diffusion Transformer adapts the pretrained Seedance 2.0 Mini reference-to-video backbone to accept rolling action prompts, streaming audio, and dynamically updated image, audio, and video references. Video-driven rotary positional embeddings align reference chunks with the generation timeline, while reference-plus-image conditioning and partially noised historical motion frames preserve appearance and avoid accumulated drift. Second, a Proactive Interaction Agent separates pre-online planning from online scheduling and response: it prepares a persona, a long-horizon agenda, and reusable multimodal skills in advance, then autonomously schedules behaviors, responds to audience input, and handles interruptions during a live session. FlowAct-R2 supports real-time 720p generation and hour-scale streaming across entertainment streaming, live shopping, video chatting, and live vlogging.

Özetle: We present FlowAct-R2, a framework for interactive humanoid video generation that combines continuous multimodal control with proactive agent planning.

Özet

We present FlowAct-R2, a framework for interactive humanoid video generation that combines continuous multimodal control with proactive agent planning. Our method consists of two coupled components. First, a Streaming Multimodal Reference Diffusion Transformer adapts the pretrained Seedance 2.0 Mini reference-to-video backbone to accept rolling action prompts, streaming audio, and dynamically updated image, audio, and video references. Video-driven rotary positional embeddings align reference chunks with the generation timeline, while reference-plus-image conditioning and partially noised historical motion frames preserve appearance and avoid accumulated drift. Second, a Proactive Interaction Agent separates pre-online planning from online scheduling and response: it prepares a persona, a long-horizon agenda, and reusable multimodal skills in advance, then autonomously schedules behaviors, responds to audience input, and handles interruptions during a live session. FlowAct-R2 supports real-time 720p generation and hour-scale streaming across entertainment streaming, live shopping, video chatting, and live vlogging.

Orijinal Özet (İngilizce)

We present FlowAct-R2, a framework for interactive humanoid video generation that combines continuous multimodal control with proactive agent planning. Our method consists of two coupled components. First, a Streaming Multimodal Reference Diffusion Transformer adapts the pretrained Seedance 2.0 Mini reference-to-video backbone to accept rolling action prompts, streaming audio, and dynamically updated image, audio, and video references. Video-driven rotary positional embeddings align reference chunks with the generation timeline, while reference-plus-image conditioning and partially noised historical motion frames preserve appearance and avoid accumulated drift. Second, a Proactive Interaction Agent separates pre-online planning from online scheduling and response: it prepares a persona, a long-horizon agenda, and reusable multimodal skills in advance, then autonomously schedules behaviors, responds to audience input, and handles interruptions during a live session. FlowAct-R2 supports real-time 720p generation and hour-scale streaming across entertainment streaming, live shopping, video chatting, and live vlogging.

Kaynak: arXiv:2609.35728 · PDF

BibTeX

@article{huang2026flowactr,
  title   = {FlowAct-R2: Beyond Talking Avatar via Streaming Multimodal References and Proactive Agent Planning},
  author  = {Ziyao Huang and Zhengkun Rong and Shiyang Qin and Shuang Liang and Wentao Hu and Yuxuan Luo and Yuan Zhang and Mingyuan Gao},
  journal = {arXiv preprint arXiv:2609.35728},
  year    = {2026},
  url     = {https://arxiv.org/abs/2609.35728}
}

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