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Overcoming False Illusions in Real-World Face Restoration with Multi-Modal Guided Diffusion Model
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We introduce a novel Multi-modal Guided Real-World Face Restoration (MGFR) technique designed to improve the quality of facial image restoration from low-quality inputs. Leveraging a blend of attribute text prompts, high-quality reference images, and identity information, MGFR can mitigate the generation of false facial attributes and identities often associated with generative face restoration methods. By incorporating a dual-control adapter and a two-stage training strategy, our method effectively utilizes multi-modal prior information for targeted restoration tasks. We also present the Reface-HQ dataset, comprising over 21,000 high-resolution facial images across 4800 identities, to address the need for reference face training images. Our approach achieves superior visual quality in restoring facial details under severe degradation and allows for controlled restoration processes, enhancing the accuracy of identity preservation and attribute correction. Including negative quality samples and attribute prompts in the training further refines the model's ability to generate detailed and perceptually accurate images.
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Cited by 4 Pith papers
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Robust ID-Specific Face Restoration via Alignment Learning
RIDFR injects a reference person's identity into diffusion-based face restoration and uses Alignment Learning across multiple same-identity references to suppress pose, expression, and makeup interference.
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RefSTAR: Blind Facial Image Restoration with Reference Selection, Transfer, and Reconstruction
A reference-based face restoration method that explicitly selects which reference regions to transfer, uses dual-stream attention to force feature transfer, and adds a mask-compatible cycle loss, achieving state-of-th...
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Show and Polish: Reference-Guided Identity Preservation in Face Video Restoration
IP-FVR restores degraded face videos with consistent identity by conditioning a video diffusion model on a reference photo of the same person.
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Personalized Face Super-Resolution with Identity Decoupling and Fitting
IDFSR combines masked-diffusion face restoration with a per-identity embedding, fitted on a few same-person images, to improve ID consistency under 8x and 16x degradation.
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