  It is hard to generate an image at target view well for previous cross-view
image translation methods that directly adopt a simple encoder-decoder or U-Net
structure, especially for drastically different views and severe deformation
cases. To ease this problem, we propose a novel two-stage framework with a new
Cascaded Cross MLP-Mixer (CrossMLP) sub-network in the first stage and one
refined pixel-level loss in the second stage. In the first stage, the CrossMLP
sub-network learns the latent transformation cues between image code and
semantic map code via our novel CrossMLP blocks. Then the coarse results are
generated progressively under the guidance of those cues. Moreover, in the
second stage, we design a refined pixel-level loss that eases the noisy
semantic label problem with more reasonable regularization in a more compact
fashion for better optimization. Extensive experimental results on
Dayton~\cite{vo2016localizing} and CVUSA~\cite{workman2015wide} datasets show
that our method can generate significantly better results than state-of-the-art
methods. The source code and trained models are available at
https://github.com/Amazingren/CrossMLP.
