Visual Style Prompt Learning Using Diffusion Models for Blind Face Restoration

• URL: http://arxiv.org/abs/2412.21042v1
• Date: Mon, 30 Dec 2024 16:05:40 GMT
• Title: Visual Style Prompt Learning Using Diffusion Models for Blind Face Restoration
• Authors: Wanglong Lu, Jikai Wang, Tao Wang, Kaihao Zhang, Xianta Jiang, Hanli Zhao,
• Abstract summary: Blind face restoration aims to recover high-quality facial images from various unidentified sources of degradation. Prior knowledge-based methods, leveraging geometric priors and facial features, have led to advancements in face restoration but often fall short of capturing fine details. We introduce a visual style prompt learning framework that utilizes diffusion probabilistic models to explicitly generate visual prompts.
• Score: 16.67947885664477
• License:
• Abstract: Blind face restoration aims to recover high-quality facial images from various unidentified sources of degradation, posing significant challenges due to the minimal information retrievable from the degraded images. Prior knowledge-based methods, leveraging geometric priors and facial features, have led to advancements in face restoration but often fall short of capturing fine details. To address this, we introduce a visual style prompt learning framework that utilizes diffusion probabilistic models to explicitly generate visual prompts within the latent space of pre-trained generative models. These prompts are designed to guide the restoration process. To fully utilize the visual prompts and enhance the extraction of informative and rich patterns, we introduce a style-modulated aggregation transformation layer. Extensive experiments and applications demonstrate the superiority of our method in achieving high-quality blind face restoration. The source code is available at \href{https://github.com/LonglongaaaGo/VSPBFR}{https://github.com/LonglongaaaGo/VSPBFR}.

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