  In this paper, we propose a novel approach to solve the pose guided person
image generation task. We assume that the relation between pose and appearance
information can be described by a simple matrix operation in hidden space.
Based on this assumption, our method estimates a pose-invariant feature matrix
for each identity, and uses it to predict the target appearance conditioned on
the target pose. The estimation process is formulated as a p-norm regression
problem in hidden space. By utilizing the differentiation of the solution of
this regression problem, the parameters of the whole framework can be trained
in an end-to-end manner. While most previous works are only applicable to the
supervised training and single-shot generation scenario, our method can be
easily adapted to unsupervised training and multi-shot generation. Extensive
experiments on the challenging Market-1501 dataset show that our method yields
competitive performance in all the aforementioned variant scenarios.
