  Person re-identification (re-id) remains challenging due to significant
intra-class variations across different cameras. Recently, there has been a
growing interest in using generative models to augment training data and
enhance the invariance to input changes. The generative pipelines in existing
methods, however, stay relatively separate from the discriminative re-id
learning stages. Accordingly, re-id models are often trained in a
straightforward manner on the generated data. In this paper, we seek to improve
learned re-id embeddings by better leveraging the generated data. To this end,
we propose a joint learning framework that couples re-id learning and data
generation end-to-end. Our model involves a generative module that separately
encodes each person into an appearance code and a structure code, and a
discriminative module that shares the appearance encoder with the generative
module. By switching the appearance or structure codes, the generative module
is able to generate high-quality cross-id composed images, which are online fed
back to the appearance encoder and used to improve the discriminative module.
The proposed joint learning framework renders significant improvement over the
baseline without using generated data, leading to the state-of-the-art
performance on several benchmark datasets.
