Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908948850802688 |
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| author | Kazerouni, Amirhossein Suin, Maitreya Aumentado-Armstrong, Tristan Honari, Sina Walia, Amanpreet Mohomed, Iqbal Derpanis, Konstantinos G. Taati, Babak Levinshtein, Alex |
| author_facet | Kazerouni, Amirhossein Suin, Maitreya Aumentado-Armstrong, Tristan Honari, Sina Walia, Amanpreet Mohomed, Iqbal Derpanis, Konstantinos G. Taati, Babak Levinshtein, Alex |
| contents | Recent advances in image restoration have enabled high-fidelity recovery of faces from degraded inputs using reference-based face restoration models (Ref-FR). However, such methods focus solely on facial regions, neglecting degradation across the full scene, including body and background, which limits practical usability. Meanwhile, full-scene restorers often ignore degradation cues entirely, leading to underdetermined predictions and visual artifacts. In this work, we propose Face2Scene, a two-stage restoration framework that leverages the face as a perceptual oracle to estimate degradation and guide the restoration of the entire image. Given a degraded image and one or more identity references, we first apply a Ref-FR model to reconstruct high-quality facial details. From the restored-degraded face pair, we extract a face-derived degradation code that captures degradation attributes (e.g., noise, blur, compression), which is then transformed into multi-scale degradation-aware tokens. These tokens condition a diffusion model to restore the full scene in a single step, including the body and background. Extensive experiments demonstrate the superior effectiveness of the proposed method compared to state-of-the-art methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_16570 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration Kazerouni, Amirhossein Suin, Maitreya Aumentado-Armstrong, Tristan Honari, Sina Walia, Amanpreet Mohomed, Iqbal Derpanis, Konstantinos G. Taati, Babak Levinshtein, Alex Computer Vision and Pattern Recognition Recent advances in image restoration have enabled high-fidelity recovery of faces from degraded inputs using reference-based face restoration models (Ref-FR). However, such methods focus solely on facial regions, neglecting degradation across the full scene, including body and background, which limits practical usability. Meanwhile, full-scene restorers often ignore degradation cues entirely, leading to underdetermined predictions and visual artifacts. In this work, we propose Face2Scene, a two-stage restoration framework that leverages the face as a perceptual oracle to estimate degradation and guide the restoration of the entire image. Given a degraded image and one or more identity references, we first apply a Ref-FR model to reconstruct high-quality facial details. From the restored-degraded face pair, we extract a face-derived degradation code that captures degradation attributes (e.g., noise, blur, compression), which is then transformed into multi-scale degradation-aware tokens. These tokens condition a diffusion model to restore the full scene in a single step, including the body and background. Extensive experiments demonstrate the superior effectiveness of the proposed method compared to state-of-the-art methods. |
| title | Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.16570 |