  State-of-the-art pedestrian detection models have achieved great success in
many benchmarks. However, these models require lots of annotation information
and the labeling process usually takes much time and efforts. In this paper, we
propose a method to generate labeled pedestrian data and adapt them to support
the training of pedestrian detectors. The proposed framework is built on the
Generative Adversarial Network (GAN) with multiple discriminators, trying to
synthesize realistic pedestrians and learn the background context
simultaneously. To handle the pedestrians of different sizes, we adopt the
Spatial Pyramid Pooling (SPP) layer in the discriminator. We conduct
experiments on two benchmarks. The results show that our framework can smoothly
synthesize pedestrians on background images of variations and different levels
of details. To quantitatively evaluate our approach, we add the generated
samples into training data of the baseline pedestrian detectors and show the
synthetic images are able to improve the detectors' performance.
