Abstract
Most image completion methods produce only one result
for each masked input, although there may be many reasonable possibilities. In this paper, we present an approach for pluralistic image completion – the task of generating multiple and diverse plausible solutions for image completion.
A major challenge faced by learning-based approaches is that usually only one ground truth training instance per label. As such, sampling from conditional VAEs still leads
to minimal diversity. To overcome this, we propose a novel and probabilistically principled framework with two parallel paths. One is a reconstructive path that utilizes the only
one given ground truth to get prior distribution of missing parts and rebuild the original image from this distribution. The other is a generative path for which the conditional
prior is coupled to the distribution obtained in the reconstructive path. Both are supported by GANs. We also introduce a new short+long term attention layer that exploits
distant relations among decoder and encoder features, improving appearance consistency. When tested on datasets with buildings (Paris), faces (CelebA-HQ), and natural images (ImageNet), our method not only generated higherquality completion results, but also with multiple and diverse plausible outputs.
2022-05-09 16:57:33
2.93MB
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人工智能-机器学习-课堂教学评价实证研究从多元智能实验看课堂教学评价.pdf
2022-05-09 09:07:48
3.72MB