Person Re-identification (re-id) faces two major challenges: the lack of
cross-view paired training data and learning discriminative identity-sensitive
and view-invariant features in the presence of large pose variations. In this
work, we address both problems by proposing a novel deep person image
generation model for synthesizing realistic person images conditional on the
pose. The model is based on a generative adversarial network (GAN) designed
specifically for pose normalization in re-id, thus termed pose-normalization
GAN (PN-GAN). With the synthesized images, we can learn a new type of deep
re-id feature free of the influence of pose variations. We show that this
feature is strong on its own and complementary to features learned with the
original images. Importantly, under the transfer learning setting, we show that
our model generalizes well to any new re-id dataset without the need for
collecting any training data for model fine-tuning. The model thus has the
potential to make re-id model truly scalable.

本文提出了一种新颖的深度人图像生成模型，利用生成对抗网络，实现了消除视角变化的标准化，生成与姿势相关的真实人物图像，得到新型的去除姿势影响的深度重新识别（re-id）特征，并证明这种特征可以广泛应用于许多不同的 re-id 数据集中。