MoFRR: Mixture of Diffusion Models for Face Retouching Restoration

• URL: http://arxiv.org/abs/2507.19770v1
• Date: Sat, 26 Jul 2025 03:45:53 GMT
• Title: MoFRR: Mixture of Diffusion Models for Face Retouching Restoration
• Authors: Jiaxin Liu, Qichao Ying, Zhenxing Qian, Sheng Li, Runqi Zhang, Jian Liu, Xinpeng Zhang,
• Abstract summary: Face Retouching Restoration (FRR) is a novel computer vision task aimed at restoring original faces from retouched counterparts.<n>MoFRR uses sparse activation of specialized experts handling distinct retouching types and the engagement of a shared expert dealing with universal retouching traces.<n>Experiments on a newly constructed face retouching dataset, RetouchingFFHQ++, demonstrate the effectiveness of MoFRR for FRR.
• Score: 36.309979915418296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The widespread use of face retouching on social media platforms raises concerns about the authenticity of face images. While existing methods focus on detecting face retouching, how to accurately recover the original faces from the retouched ones has yet to be answered. This paper introduces Face Retouching Restoration (FRR), a novel computer vision task aimed at restoring original faces from their retouched counterparts. FRR differs from traditional image restoration tasks by addressing the complex retouching operations with various types and degrees, which focuses more on the restoration of the low-frequency information of the faces. To tackle this challenge, we propose MoFRR, Mixture of Diffusion Models for FRR. Inspired by DeepSeek's expert isolation strategy, the MoFRR uses sparse activation of specialized experts handling distinct retouching types and the engagement of a shared expert dealing with universal retouching traces. Each specialized expert follows a dual-branch structure with a DDIM-based low-frequency branch guided by an Iterative Distortion Evaluation Module (IDEM) and a Cross-Attention-based High-Frequency branch (HFCAM) for detail refinement. Extensive experiments on a newly constructed face retouching dataset, RetouchingFFHQ++, demonstrate the effectiveness of MoFRR for FRR.

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