Many tasks in computer vision and graphics fall within the framework of conditional image synthesis. In recent years, generative adversarial nets have delivered impressive advances in quality of synthesized images. However, it remains a challenge to generate both diverse and plausible images for the same input, due to the problem of mode collapse. In this paper, we develop a new generic multimodal conditional image synthesis method based on implicit maximum likelihood estimation and demonstrate improved multimodal image synthesis performance on two tasks, single image super-resolution and image synthesis from scene layouts. We make our implementation publicly available.

中文翻译：

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具有条件隐式最大似然估计的多模态图像合成

计算机视觉和图形中的许多任务都属于条件图像合成的框架。近年来，生成对抗网络在合成图像的质量方面取得了令人瞩目的进步。然而，由于模式崩溃的问题，为相同的输入生成多样化和合理的图像仍然是一个挑战。在本文中，我们开发了一种新的基于隐式最大似然估计的通用多模态条件图像合成方法，并在两个任务上展示了改进的多模态图像合成性能，单图像超分辨率和场景布局的图像合成。我们公开我们的实施。