PersonaLive! Expressive Portrait Image Animation for Live Streaming
Current diffusion-based portrait animation models predominantly focus on enhancing visual quality and expression realism, while overlooking generation latency and real-time performance, which restricts their application range in the live streaming scenario. We propose PersonaLive, a novel diffusion-based framework towards streaming real-time portrait animation with multi-stage training recipes. Specifically, we first adopt hybrid implicit signals, namely implicit facial representations and 3D implicit keypoints, to achieve expressive image-level motion control. Then, a fewer-step appearance distillation strategy is proposed to eliminate appearance redundancy in the denoising process, greatly improving inference efficiency. Finally, we introduce an autoregressive micro-chunk streaming generation paradigm equipped with a sliding training strategy and a historical keyframe mechanism to enable low-latency and stable long-term video generation. Extensive experiments demonstrate that PersonaLive achieves state-of-the-art performance with up to 7-22x speedup over prior diffusion-based portrait animation models.
693f75c3f516c693246811ed
Zhiyuan Li
claimed_verified
2025-12-17T10:09:36.556Z
6376393490ce7ffb7a960e5e
https://cdn-avatars.huggingface.co/v1/production/uploads/6376393490ce7ffb7a960e5e/gyS6iVaK_fJ5uH3X36LdI.jpeg
Zhiyuan Li
huaichang
user
huaichang
693f75c3f516c693246811ee
Chi-Man Pun
693f75c3f516c693246811ef
Chen Fang
693f75c3f516c693246811f0
Jue Wang
693f75c3f516c693246811f1
Xiaodong Cun
claimed_verified
2025-12-15T08:08:36.497Z
63184c517ca1b876d99b7e0e
https://cdn-avatars.huggingface.co/v1/production/uploads/63184c517ca1b876d99b7e0e/b-qDExoeJuDXK0cJBZKnz.jpeg
Xiaodong Cun
vinthony
user
vinthony
PersonaLive! Expressive Portrait Image Animation for Live Streaming
Current diffusion-based portrait animation models predominantly focus on enhancing visual quality and expression realism, while overlooking generation latency and real-time performance, which restricts their application range in the live streaming scenario. We propose PersonaLive, a novel diffusion-based framework towards streaming real-time portrait animation with multi-stage training recipes. Specifically, we first adopt hybrid implicit signals, namely implicit facial representations and 3D implicit keypoints, to achieve expressive image-level motion control. Then, a fewer-step appearance distillation strategy is proposed to eliminate appearance redundancy in the denoising process, greatly improving inference efficiency. Finally, we introduce an autoregressive micro-chunk streaming generation paradigm equipped with a sliding training strategy and a historical keyframe mechanism to enable low-latency and stable long-term video generation. Extensive experiments demonstrate that PersonaLive achieves state-of-the-art performance with up to 7-22x speedup over prior diffusion-based portrait animation models.
PersonaLive is a diffusion-based portrait animation framework that improves real-time performance through hybrid implicit signals, appearance distillation, and autoregressive streaming generation.
diffusion-based portrait animation hybrid implicit signals implicit facial representations 3D implicit keypoints appearance distillation autoregressive micro-chunk streaming generation sliding training strategy historical keyframe mechanism