广东省基础与应用基础基金区域联合基金(2020B1515120089); 广东省高校人工智能重点领域专项基金(2019KZDZX1033); 广东省佛山市技术创新基金(2016AG100472)
现有的妆容迁移算法效果优越, 功能丰富, 但是较少考虑到输入图像为低分辨率的场景. 当高分辨率图像难以获得时, 现有的妆容迁移算法将难以适用, 妆容无法完全迁移. 为此本文提出了一种适用于低分辨率图像的妆容迁移算法, 将包含妆容信息的特征矩阵作为先验信息, 将超分辨率网络与妆容迁移网络结合在一起产生协同效应, 即使输入的图像为低分辨率图像, 也能输出高分辨率的妆容迁移结果, 并且充分保留妆容细节的同时提升姿势和表情的鲁棒性. 由于使用端到端的模型实现妆容迁移与超分辨率, 因此设计了一组联合损失函数, 包括生成对抗损失、感知损失、循环一致性损失、妆容损失和均方误差损失函数. 所提出的模型在妆容迁移与超分辨率的定性实验和定量实验中均达到了先进水平.
The existing makeup transfer algorithms are highly effective with rich features, but they seldom take into account the scenarios of the low-resolution input images. When high-resolution images are difficult to obtain, it will be difficult for the existing makeup transfer algorithms to apply and the makeup cannot be fully transferred. In this study, a makeup transfer algorithm applied to low-resolution images is proposed, which uses the feature matrix containing makeup information as prior information and combines the super-resolution network with the makeup transfer network to produce the synergistic effect. The high-resolution makeup transfer results can be delivered even if the input image is a low-resolution one, and the robustness of postures and expressions is improved while the makeup details are fully retained. Since an end-to-end model is adopted to achieve the makeup transfer and super-resolution, a set of joint loss functions are designed, including generative adversarial loss, perceptual loss, cycle consistency loss, makeup loss, and mean square error loss functions. The proposed model attains an advanced level in both qualitative and quantitative experiments on makeup transfer and super-resolution.