Real-time One-Step Diffusion-based Expressive Portrait Videos Generation

• URL: http://arxiv.org/abs/2412.13479v1
• Date: Wed, 18 Dec 2024 03:42:42 GMT
• Title: Real-time One-Step Diffusion-based Expressive Portrait Videos Generation
• Authors: Hanzhong Guo, Hongwei Yi, Daquan Zhou, Alexander William Bergman, Michael Lingelbach, Yizhou Yu,
• Abstract summary: We introduce OSA-LCM (One-Step Avatar Latent Consistency Model), paving the way for real-time diffusion-based avatars. Our method achieves comparable video quality to existing methods but requires only one sampling step, making it more than 10x faster.
• Score: 85.07446744308247
• License:
• Abstract: Latent diffusion models have made great strides in generating expressive portrait videos with accurate lip-sync and natural motion from a single reference image and audio input. However, these models are far from real-time, often requiring many sampling steps that take minutes to generate even one second of video-significantly limiting practical use. We introduce OSA-LCM (One-Step Avatar Latent Consistency Model), paving the way for real-time diffusion-based avatars. Our method achieves comparable video quality to existing methods but requires only one sampling step, making it more than 10x faster. To accomplish this, we propose a novel avatar discriminator design that guides lip-audio consistency and motion expressiveness to enhance video quality in limited sampling steps. Additionally, we employ a second-stage training architecture using an editing fine-tuned method (EFT), transforming video generation into an editing task during training to effectively address the temporal gap challenge in single-step generation. Experiments demonstrate that OSA-LCM outperforms existing open-source portrait video generation models while operating more efficiently with a single sampling step.

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