  We tackle the problem of target-free text-guided image manipulation, which
requires one to modify the input reference image based on the given text
instruction, while no ground truth target image is observed during training. To
address this challenging task, we propose a Cyclic-Manipulation GAN (cManiGAN)
in this paper, which is able to realize where and how to edit the image regions
of interest. Specifically, the image editor in cManiGAN learns to identify and
complete the input image, while cross-modal interpreter and reasoner are
deployed to verify the semantic correctness of the output image based on the
input instruction. While the former utilizes factual/counterfactual description
learning for authenticating the image semantics, the latter predicts the "undo"
instruction and provides pixel-level supervision for the training of cManiGAN.
With such operational cycle-consistency, our cManiGAN can be trained in the
above weakly supervised setting. We conduct extensive experiments on the
datasets of CLEVR and COCO, and the effectiveness and generalizability of our
proposed method can be successfully verified. Project page:
https://sites.google.com/view/wancyuanfan/projects/cmanigan.
