The outpainting results produced by existing approaches are often too random to meet users' requirement. In this work, we take the image outpainting one step forward by allowing users to harvest personal custom outpainting results using sketches as the guidance. To this end, we propose an encoder-decoder based network to conduct sketch-guided outpainting, where two alignment modules are adopted to impose the generated content to be realistic and consistent with the provided sketches. First, we apply a holistic alignment module to make the synthesized part be similar to the real one from the global view. Second, we reversely produce the sketches from the synthesized part and encourage them be consistent with the ground-truth ones using a sketch alignment module. In this way, the learned generator will be imposed to pay more attention to fine details and be sensitive to the guiding sketches. To our knowledge, this work is the first attempt to explore the challenging yet meaningful conditional scenery image outpainting. We conduct extensive experiments on two collected benchmarks to qualitatively and quantitatively validate the effectiveness of our approach compared with the other state-of-the-art generative models.

中文翻译：

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草图引导的风景图像外画

现有方法产生的修补结果往往过于随机，无法满足用户的要求。在这项工作中，我们通过允许用户以草图为指导收获个人定制的修复结果，使图像修复向前迈进了一步。为此，我们提出了一个基于编码器-解码器的网络来进行草图引导的外画，其中采用两个对齐模块来使生成的内容与提供的草图保持真实并一致。首先，我们应用了一个整体对齐模块，使合成部分从全局来看与真实部分相似。其次，我们从合成部分反向生成草图，并使用草图对齐模块鼓励它们与真实情况一致。这样，学习到的生成器将被强加给更多关注细节并对指导草图敏感。据我们所知，这项工作是第一次尝试探索具有挑战性但有意义的条件风景图像外画。我们对两个收集的基准进行了广泛的实验，以定性和定量地验证我们的方法与其他最先进的生成模型相比的有效性。