  We present a generalization of the person-image generation task, in which a
human image is generated conditioned on a target pose and a set X of source
appearance images. In this way, we can exploit multiple, possibly complementary
images of the same person which are usually available at training and at
testing time. The solution we propose is mainly based on a local attention
mechanism which selects relevant information from different source image
regions, avoiding the necessity to build specific generators for each specific
cardinality of X. The empirical evaluation of our method shows the practical
interest of addressing the person-image generation problem in a multi-source
setting.
