Abstract
Generating novel, yet realistic, images of persons is a
challenging task due to the complex interplay between the
different image factors, such as the foreground, background
and pose information. In this work, we aim at generating such images based on a novel, two-stage reconstruction pipeline that learns a disentangled representation of
the aforementioned image factors and generates novel person images at the same time. First, a multi-branched reconstruction network is proposed to disentangle and encode the
three factors into embedding features, which are then combined to re-compose the input image itself. Second, three
corresponding mapping functions are learned in an adversarial manner in order to map Gaussian noise to the learned
embedding feature space, for each factor, respectively. Using the proposed framework, we can manipulate the foreground, background and pose of the input image, and also
sample new embedding features to generate such targeted
manipulations, that provide more control over the generation process. Experiments on the Market-1501 and Deepfashion datasets show that our model does not only generate
realistic person images with new foregrounds, backgrounds
and poses, but also manipulates the generated factors and
interpolates the in-between states. Another set of experiments on Market-1501 shows that our model can also be
beneficial for the person re-identification task