Generative image modeling techniques such as GAN demonstrate highly convincing image generation result. However, user interaction is often necessary to obtain the desired results. Existing attempts add interactivity but require either tailored architectures or extra data. We present a human-in-the-optimization method that allows users to directly explore and search the latent vector space of generative image modeling. Our system provides multiple candidates by sampling the latent vector space, and the user selects the best blending weights within the subspace using multiple sliders. In addition, the user can express their intention through image editing tools. The system samples latent vectors based on inputs and presents new candidates to the user iteratively. An advantage of our formulation is that one can apply our method to arbitrary pre-trained model without developing specialized architecture or data. We demonstrate our method with various generative image modeling applications, and show superior performance in a comparative user study with prior art iGAN.

中文翻译：

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使用顺序子空间搜索和基于内容的指导的生成图像建模的交互优化

GAN 等生成图像建模技术展示了极具说服力的图像生成结果。然而，用户交互通常是获得所需结果所必需的。现有的尝试增加了交互性，但需要定制的架构或额外的数据。我们提出了一种人工优化方法，允许用户直接探索和搜索生成图像建模的潜在向量空间。我们的系统通过对潜在向量空间进行采样来提供多个候选，并且用户使用多个滑块在子空间内选择最佳混合权重。此外，用户可以通过图像编辑工具表达他们的意图。系统根据输入对潜在向量进行采样，并以迭代方式向用户呈现新的候选对象。我们的公式的一个优点是可以将我们的方法应用于任意的预训练模型，而无需开发专门的架构或数据。我们用各种生成图像建模应用程序展示了我们的方法，并在与现有技术 iGAN 的比较用户研究中展示了卓越的性能。