We present HandGAN (H-GAN), a cycle-consistent adversarial learning approach
implementing multi-scale perceptual discriminators. It is designed to translate
synthetic images of hands to the real domain. Synthetic hands provide complete
ground-truth annotations, yet they are not representative of the target
distribution of real-world data. We strive to provide the perfect blend of a
realistic hand appearance with synthetic annotations. Relying on image-to-image
translation, we improve the appearance of synthetic hands to approximate the
statistical distribution underlying a collection of real images of hands. H-GAN
tackles not only the cross-domain tone mapping but also structural differences
in localized areas such as shading discontinuities. Results are evaluated on a
qualitative and quantitative basis improving previous works. Furthermore, we
relied on the hand classification task to claim our generated hands are
statistically similar to the real domain of hands.

本论文提出 HandGAN，一种采用多尺度感知判别器的循环一致性对抗性学习方法，旨在将合成的手部图像转换为真实领域中的图像，从而提高人工合成手部图像的逼真程度。