  In contrast to great success of memory-consuming face editing methods at a
low resolution, to manipulate high-resolution (HR) facial images, i.e.,
typically larger than 7682 pixels, with very limited memory is still
challenging. This is due to the reasons of 1) intractable huge demand of
memory; 2) inefficient multi-scale features fusion. To address these issues, we
propose a NOVEL pixel translation framework called Cooperative GAN(CooGAN) for
HR facial image editing. This framework features a local path for fine-grained
local facial patch generation (i.e., patch-level HR, LOW memory) and a global
path for global lowresolution (LR) facial structure monitoring (i.e.,
image-level LR, LOW memory), which largely reduce memory requirements. Both
paths work in a cooperative manner under a local-to-global consistency
objective (i.e., for smooth stitching). In addition, we propose a lighter
selective transfer unit for more efficient multi-scale features fusion,
yielding higher fidelity facial attributes manipulation. Extensive experiments
on CelebAHQ well demonstrate the memory efficiency as well as the high image
generation quality of the proposed framework.
