Local Facial Makeup Transfer via Disentangled Representation

Abstract: Facial makeup transfer aims to render a non-makeup face image in an arbitrary given makeup one while preserving face identity. The most advanced method separates makeup style information from face images to realize makeup transfer. However, makeup style includes several semantic clear local styles which are still entangled together. In this paper, we propose a novel unified adversarial disentangling network to further decompose face images into four independent components, i.e., personal identity, lips makeup style, eyes makeup style and face makeup style. Owing to the disentangled makeup representation, our method can not only flexible control the degree of local makeup styles, but also can transfer local makeup styles from different images into the final result, which any other approaches fail to handle. For makeup removal, different from other methods which regard makeup removal as the reverse process of makeup transfer, we integrate the makeup transfer with the makeup removal into one uniform framework and obtain multiple makeup removal results. Extensive experiments have demonstrated that our approach can produce visually pleasant and accurate makeup transfer results compared to the state-of-the-art methods.

14/06/2020

PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer

Wentao Jiang, Si Liu, Chen Gao and
Jie Cao, Ran He, Jiashi Feng, Shuicheng Yan

Zhiliang Xu, Xiyu Yu, Zhibin Hong and
Zhen Zhu, Junyu Han, Jingtuo Liu, Errui Ding, Xiang Bai

radial distortion correction, face recognition, spatial transformer network, cascaded network, fisheye camera, wide-angle camera

1:00

14/06/2020

3d face reconstruction, soft rasterization, differentiable rendering, free-form deformation, 3d morphable model, face parsing

5:01

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scene image, scene text editor, font adaptive, font generation, font color transfer, single observation, computer vision, deep learning

1:00

22/11/2021

Yuhan Wang, Xu Chen, Junwei Zhu and
Wenqing Chu, Ying Tai, Chengjie Wang, Jilin Li, Yongjian Wu, Feiyue Huang, Rongrong Ji

image inpainting, diverse image inpainting, image completion, unsupervised cross-space translation, diverse image generation, deep-learning based inpainting, deep learning, multiple-solution inpainting

1:01

06/12/2021

superimposed image separation, adversarial training, separation-critic, image deraining, photo reflection removal, image shadow removal

0:59

14/06/2020

Ziyu Wan, Bo Zhang, Dongdong Chen and
Pan Zhang, Dong Chen, Jing Liao, Fang Wen

deepfake detection, image forensics, face forgery detection, face manipulation detection, image presentation, responsible ai

4:59

19/08/2021