Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation
Talking head generation creates lifelike avatars from static portraits for virtual communication and content creation. However, current models do not yet convey the feeling of truly interactive communication, often generating one-way responses that lack emotional engagement. We identify two key challenges toward truly interactive avatars: generating motion in real-time under causal constraints and learning expressive, vibrant reactions without additional labeled data. To address these challenges, we propose Avatar Forcing, a new framework for interactive head avatar generation that models real-time user-avatar interactions through diffusion forcing. This design allows the avatar to process real-time multimodal inputs, including the user's audio and motion, with low latency for instant reactions to both verbal and non-verbal cues such as speech, nods, and laughter. Furthermore, we introduce a direct preference optimization method that leverages synthetic losing samples constructed by dropping user conditions, enabling label-free learning of expressive interaction. Experimental results demonstrate that our framework enables real-time interaction with low latency (approximately 500ms), achieving 6.8X speedup compared to the baseline, and produces reactive and expressive avatar motion, which is preferred over 80% against the baseline.
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Taekyung Ki
claimed_verified
2026-01-05T08:27:19.100Z
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Taekyung Ki
taekyungki
user
taekyungki
695b237a832867f253525d71
Sangwon Jang
claimed_verified
2026-01-29T09:17:58.520Z
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Sangwon
agwmon
user
agwmon
695b237a832867f253525d72
Jaehyeong Jo
claimed_verified
2026-01-27T16:23:06.813Z
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Jaehyeong Jo
harryjo97
user
harryjo97
695b237a832867f253525d73
Jaehong Yoon
claimed_verified
2026-01-05T08:27:17.228Z
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Jaehong Yoon
jaehong31
user
jaehong31
695b237a832867f253525d74
Sung Ju Hwang
Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation
Talking head generation creates lifelike avatars from static portraits for virtual communication and content creation. However, current models do not yet convey the feeling of truly interactive communication, often generating one-way responses that lack emotional engagement. We identify two key challenges toward truly interactive avatars: generating motion in real-time under causal constraints and learning expressive, vibrant reactions without additional labeled data. To address these challenges, we propose Avatar Forcing, a new framework for interactive head avatar generation that models real-time user-avatar interactions through diffusion forcing. This design allows the avatar to process real-time multimodal inputs, including the user's audio and motion, with low latency for instant reactions to both verbal and non-verbal cues such as speech, nods, and laughter. Furthermore, we introduce a direct preference optimization method that leverages synthetic losing samples constructed by dropping user conditions, enabling label-free learning of expressive interaction. Experimental results demonstrate that our framework enables real-time interaction with low latency (approximately 500ms), achieving 6.8X speedup compared to the baseline, and produces reactive and expressive avatar motion, which is preferred over 80% against the baseline.
Avatar Forcing framework enables real-time interactive head avatar generation with low latency and expressive motion through diffusion forcing and label-free preference optimization.
diffusion forcing real-time interaction multimodal inputs audio motion causal constraints synthetic losing samples direct preference optimization interactive head avatar generation