  Numerous task-specific variants of conditional generative adversarial
networks have been developed for image completion. Yet, a serious limitation
remains that all existing algorithms tend to fail when handling large-scale
missing regions. To overcome this challenge, we propose a generic new approach
that bridges the gap between image-conditional and recent modulated
unconditional generative architectures via co-modulation of both conditional
and stochastic style representations. Also, due to the lack of good
quantitative metrics for image completion, we propose the new Paired/Unpaired
Inception Discriminative Score (P-IDS/U-IDS), which robustly measures the
perceptual fidelity of inpainted images compared to real images via linear
separability in a feature space. Experiments demonstrate superior performance
in terms of both quality and diversity over state-of-the-art methods in
free-form image completion and easy generalization to image-to-image
translation. Code is available at https://github.com/zsyzzsoft/co-mod-gan.

Computer Vision and Pattern Recognition;Graphics;Machine Learning