Learning Oracle Attention for High-Fidelity Face Completion

By Tong Zhou, Changxing Ding, Shaowen Lin, Xinchao Wang, Dacheng Tao. 2020

46 citations recorded by the source corpus.

High-fidelity face completion is a challenging task due to the rich and subtle facial textures involved. What makes it more complicated is the correlations between different facial components, for example, the symmetry in texture and structure between both eyes. While recent works adopted the attention mechanism to learn the contextual relations among elements of the face, they have largely overlooked the disastrous impacts of inaccurate attention scores; in addition, they fail to pay sufficient attention to key facial components, the completion results of which largely determine the authenticity of a face image. Accordingly, in this paper, we design a comprehensive framework for face completion based on the U-Net structure. Specifically, we propose a dual spatial attention module to efficiently learn the correlations between facial textures at multiple scales; moreover, we provide an oracle supervision signal to the attention module to ensure that the obtained attention scores are reasonable. Furthermore, we take the location of the facial components as prior knowledge and impose a multi-discriminator on these regions, with which the fidelity of facial components is significantly promoted. Extensive experiments on two high-resolution face datasets including CelebA-HQ and Flickr-Faces-HQ demonstrate that the proposed approach outperforms state-of-the-art methods by large margins.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Large Scale Image Completion via Co-Modulated Generative Adversarial Networks (2021, background): “…al., 2018; Yang et al., 2017; 2019a; Yu et al., 2018; Yu et al., 2019; Zeng et al., 2019; Lahiri et al., 2020; Zhao et al., 2020a; Li et al., 2020; Zhou et al., 2020), among which (Liu et al., 2018; Yu et al., 2019) introduce partial convolution and gated convolution to address free-form image…”
Image Inpainting via Conditional Texture and Structure Dual Generation (2021, unknown): “The deep generative methods [35, 36, 8, 34, 38, 40, 37, 26, 12] are currently dominating, which effectively extract meaningful semantics from damaged images and recover reasonable contents with high visual fidelity, owing to their powerful feature learning ability.”