  For semantic-guided cross-view image translation, it is crucial to learn
where to sample pixels from the source view image and where to reallocate them
guided by the target view semantic map, especially when there is little overlap
or drastic view difference between the source and target images. Hence, one not
only needs to encode the long-range dependencies among pixels in both the
source view image and target view semantic map but also needs to translate
these learned dependencies. To this end, we propose a novel generative
adversarial network, PI-Trans, which mainly consists of a novel
Parallel-ConvMLP module and an Implicit Transformation module at multiple
semantic levels. Extensive experimental results show that PI-Trans achieves the
best qualitative and quantitative performance by a large margin compared to the
state-of-the-art methods on two challenging datasets. The source code is
available at https://github.com/Amazingren/PI-Trans.
