  Recent years have witnessed substantial progress in semantic image synthesis,
it is still challenging in synthesizing photo-realistic images with rich
details. Most previous methods focus on exploiting the given semantic map,
which just captures an object-level layout for an image. Obviously, a
fine-grained part-level semantic layout will benefit object details generation,
and it can be roughly inferred from an object's shape. In order to exploit the
part-level layouts, we propose a Shape-aware Position Descriptor (SPD) to
describe each pixel's positional feature, where object shape is explicitly
encoded into the SPD feature. Furthermore, a Semantic-shape Adaptive Feature
Modulation (SAFM) block is proposed to combine the given semantic map and our
positional features to produce adaptively modulated features. Extensive
experiments demonstrate that the proposed SPD and SAFM significantly improve
the generation of objects with rich details. Moreover, our method performs
favorably against the SOTA methods in terms of quantitative and qualitative
evaluation. The source code and model are available at
https://github.com/cszy98/SAFM.
