Generative image modeling techniques such as GAN demonstrate highly convincing image generation result. However, user interaction is often necessary to obtain 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.

This work was supported by JST CREST JPMJCR17A1.We would like to thank Makoto Nakajima, Bing-Yu Chen, and anonymous reviewers for insightful suggestions and discussions. During this work, I-Chao Shen was also supported by the MediaTek Fellowship.

@article{ chong2020ganui,	      
author    = {Toby Chong Long Hin and I-Chao Shen and Issei Sato and Takeo Igarashi},
title     = {Interactive Optimization of Generative Image Modeling using Sequential Subspace Search and Content-based Guidance},
journal   = {Computer Graphics Forum},
year      = {2021},
doi       = {10.1111/cgf.14188},
publisher = {Wiley Online Library}
}