INFP: Audio-Driven Interactive Head Generation in Dyadic Conversations
Yongming Zhu, Longhao Zhang, Zhengkun Rong, Tianshu Hu, Shuang Liang, Zhipeng Ge
Abstract
Figure 1. We present INFP, an audio-driven interactive head generation framework for dyadic conversations. Given the dual-track audio in dyadic conversations and a single portrait image of arbitrary agent, our framework can dynamically synthesize verbal, non-verbal and interactive agent videos with lifelike facial expressions and rhythmic head pose movements. Additionally, our framework is lightweight yet powerful, making it practical in instant communication scenarios with acceptable latency, such as the video conferencing. INFP denotes our method is Interactive, Natural, Flash and Person-generic.
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Cited by top-tier papers9
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- StreamAvatar: Streaming Diffusion Models for Real-Time Interactive Human AvatarsZhiyao Sun, Ziqiao Peng, Yifeng Ma, Yi Chen et al.CVPR 2026 · 26 citations
- Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural ConversationTaekyung Ki, Sangwon Jang, Jaehyeong Jo, Jaehong Yoon et al.CVPR 2026 · 18 citations
- DreamActor-M1: Holistic, Expressive and Robust Human Image Animation with Hybrid GuidanceYuxuan Luo, Zhengkun Rong, Lizhen Wang, Longhao Zhang et al.ICCV 2025 · 5 citations
- PolySLGen: Online Multimodal Speaking-Listening Reaction Generation in Polyadic InteractionZhi-Yi Lin, Thomas Markhorst, Jouh Yeong Chew, Xucong ZhangCVPR 2026 · 3 citations
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