  In this paper we present an end-to-end deep learning framework to turn images
that show dynamic content, such as vehicles or pedestrians, into realistic
static frames. This objective encounters two main challenges: detecting all the
dynamic objects, and inpainting the static occluded background with plausible
imagery. The second problem is approached with a conditional generative
adversarial model that, taking as input the original dynamic image and its
dynamic/static binary mask, is capable of generating the final static image.
The former challenge is addressed by the use of a convolutional network that
learns a multi-class semantic segmentation of the image.
  These generated images can be used for applications such as augmented reality
or vision-based robot localization purposes. To validate our approach, we show
both qualitative and quantitative comparisons against other state-of-the-art
inpainting methods by removing the dynamic objects and hallucinating the static
structure behind them. Furthermore, to demonstrate the potential of our
results, we carry out pilot experiments that show the benefits of our proposal
for visual place recognition.
