  Recent image inpainting methods have made great progress but often struggle
to generate plausible image structures when dealing with large holes in complex
images. This is partially due to the lack of effective network structures that
can capture both the long-range dependency and high-level semantics of an
image. We propose cascaded modulation GAN (CM-GAN), a new network design
consisting of an encoder with Fourier convolution blocks that extract
multi-scale feature representations from the input image with holes and a
dual-stream decoder with a novel cascaded global-spatial modulation block at
each scale level. In each decoder block, global modulation is first applied to
perform coarse and semantic-aware structure synthesis, followed by spatial
modulation to further adjust the feature map in a spatially adaptive fashion.
In addition, we design an object-aware training scheme to prevent the network
from hallucinating new objects inside holes, fulfilling the needs of object
removal tasks in real-world scenarios. Extensive experiments are conducted to
show that our method significantly outperforms existing methods in both
quantitative and qualitative evaluation. Please refer to the project page:
\url{https://github.com/htzheng/CM-GAN-Inpainting}.
