Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length
Existing diffusion-based video generation methods are fundamentally constrained by sequential computation and long-horizon inconsistency, limiting their practical adoption in real-time, streaming audio-driven avatar synthesis. We present Live Avatar, an algorithm-system co-designed framework that enables efficient, high-fidelity, and infinite-length avatar generation using a 14-billion-parameter diffusion model. Our approach introduces Timestep-forcing Pipeline Parallelism (TPP), a distributed inference paradigm that pipelines denoising steps across multiple GPUs, effectively breaking the autoregressive bottleneck and ensuring stable, low-latency real-time streaming. To further enhance temporal consistency and mitigate identity drift and color artifacts, we propose the Rolling Sink Frame Mechanism (RSFM), which maintains sequence fidelity by dynamically recalibrating appearance using a cached reference image. Additionally, we leverage Self-Forcing Distribution Matching Distillation to facilitate causal, streamable adaptation of large-scale models without sacrificing visual quality. Live Avatar demonstrates state-of-the-art performance, reaching 20 FPS end-to-end generation on 5 H800 GPUs, and, to the best of our knowledge, is the first to achieve practical, real-time, high-fidelity avatar generation at this scale. Our work establishes a new paradigm for deploying advanced diffusion models in industrial long-form video synthesis applications.
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Yubo Huang
admin_assigned
2025-12-08T08:55:32.421Z
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Yubo Huang
Yubo-Huang
user
Yubo-Huang
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Hailong Guo
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Fangtai Wu
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Shifeng Zhang
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Shijie Huang
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Qijun Gan
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Lin Liu
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Sirui Zhao
693251d36d1060ca587a274e
Enhong Chen
admin_assigned
2025-12-08T08:56:08.195Z
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EnHong Chen
Hongni
user
Hongni
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Jiaming Liu
claimed_verified
2025-12-05T08:29:19.340Z
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LIU JIAMING
jamesliu1217
user
jamesliu1217
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Steven Hoi
admin_assigned
2025-12-08T08:56:14.636Z
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Steven hoi
stevenhoi
user
stevenhoi
Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length
Existing diffusion-based video generation methods are fundamentally constrained by sequential computation and long-horizon inconsistency, limiting their practical adoption in real-time, streaming audio-driven avatar synthesis. We present Live Avatar, an algorithm-system co-designed framework that enables efficient, high-fidelity, and infinite-length avatar generation using a 14-billion-parameter diffusion model. Our approach introduces Timestep-forcing Pipeline Parallelism (TPP), a distributed inference paradigm that pipelines denoising steps across multiple GPUs, effectively breaking the autoregressive bottleneck and ensuring stable, low-latency real-time streaming. To further enhance temporal consistency and mitigate identity drift and color artifacts, we propose the Rolling Sink Frame Mechanism (RSFM), which maintains sequence fidelity by dynamically recalibrating appearance using a cached reference image. Additionally, we leverage Self-Forcing Distribution Matching Distillation to facilitate causal, streamable adaptation of large-scale models without sacrificing visual quality. Live Avatar demonstrates state-of-the-art performance, reaching 20 FPS end-to-end generation on 5 H800 GPUs, and, to the best of our knowledge, is the first to achieve practical, real-time, high-fidelity avatar generation at this scale. Our work establishes a new paradigm for deploying advanced diffusion models in industrial long-form video synthesis applications.
Live Avatar uses a 14-billion-parameter diffusion model with Timestep-forcing Pipeline Parallelism and Rolling Sink Frame Mechanism to achieve real-time, high-fidelity avatar generation.
diffusion-based video generation sequential computation long-horizon inconsistency real-time streaming audio-driven avatar synthesis Timestep-forcing Pipeline Parallelism distributed inference paradigm pipeline parallelism denoising steps autoregressive bottleneck low-latency real-time streaming Rolling Sink Frame Mechanism sequence fidelity Self-Forcing Distribution Matching Distillation causal streamable adaptation visual quality end-to-end generation H800 GPUs