Communication Dans Un Congrès Année : 2022

Non-Deterministic Face Mask Removal Based on 3d Priors

Résumé

This paper presents a novel image inpainting framework for face mask removal. Although current methods have demonstrated their impressive ability in recovering damaged face images, they suffer from two main problems: the dependence on manually labeled missing regions and the deterministic result corresponding to each input. The proposed approach tackles these problems by integrating a multi-task 3D face reconstruction module with a face inpainting module. Given a masked face image, the former predicts a 3DMM-based reconstructed face together with a binary occlusion map, providing dense geometrical and textural priors that greatly facilitate the inpainting task of the latter. By gradually controlling the 3D shape parameters, our method generates high-quality dynamic in-painting results with different expressions and mouth movements. Qualitative and quantitative experiments verify the effectiveness of the proposed method.

Dates et versions

hal-04910830 , version 1 (24-01-2025)

Identifiants

Citer

Xiangnan Yin, Di Huang, Liming Chen. Non-Deterministic Face Mask Removal Based on 3d Priors. 2022 IEEE International Conference on Image Processing (ICIP), Oct 2022, Bordeaux, France. pp.2137-2141, ⟨10.1109/ICIP46576.2022.9897343⟩. ⟨hal-04910830⟩
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