  We demonstrate the benefit of using an ultimate skip (US) connection for
facial expression synthesis using generative adversarial networks (GAN). A
direct connection transfers identity, facial, and color details from input to
output while suppressing artifacts. The intermediate layers can therefore focus
on expression generation only. This leads to a light-weight US-GAN model
comprised of encoding layers, a single residual block, decoding layers, and an
ultimate skip connection from input to output. US-GAN has $3\times$ fewer
parameters than state-of-the-art models and is trained on $2$ orders of
magnitude smaller dataset. It yields $7\%$ increase in face verification score
(FVS) and $27\%$ decrease in average content distance (ACD). Based on a
randomized user-study, US-GAN outperforms the state of the art by $25\%$ in
face realism, $43\%$ in expression quality, and $58\%$ in identity
preservation.
