FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration

• URL: http://arxiv.org/abs/2311.15445v1
• Date: Sun, 26 Nov 2023 22:09:18 GMT
• Title: FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration
• Authors: Zihao Zou and Jiaming Liu and Shirin Shoushtari and Yubo Wang and Weijie Gan and Ulugbek S. Kamilov
• Abstract summary: We present a new conditional diffusion framework called FLAIR for face video restoration. FLAIR ensures temporal consistency across frames in a computationally efficient fashion. Our experiments show superiority of FLAIR over the current state-of-the-art (SOTA) for video super-resolution, deblurring, JPEG restoration, and space-time frame on two high-quality face video datasets.
• Score: 14.17192434286707
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face video restoration (FVR) is a challenging but important problem where one seeks to recover a perceptually realistic face videos from a low-quality input. While diffusion probabilistic models (DPMs) have been shown to achieve remarkable performance for face image restoration, they often fail to preserve temporally coherent, high-quality videos, compromising the fidelity of reconstructed faces. We present a new conditional diffusion framework called FLAIR for FVR. FLAIR ensures temporal consistency across frames in a computationally efficient fashion by converting a traditional image DPM into a video DPM. The proposed conversion uses a recurrent video refinement layer and a temporal self-attention at different scales. FLAIR also uses a conditional iterative refinement process to balance the perceptual and distortion quality during inference. This process consists of two key components: a data-consistency module that analytically ensures that the generated video precisely matches its degraded observation and a coarse-to-fine image enhancement module specifically for facial regions. Our extensive experiments show superiority of FLAIR over the current state-of-the-art (SOTA) for video super-resolution, deblurring, JPEG restoration, and space-time frame interpolation on two high-quality face video datasets.

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