GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information

Autor:	Dang, Hai, Mecke, Lukas, Buschek, Daniel
Rok vydání:	2022
Předmět:	Computer Science - Human-Computer Interaction Computer Science - Computer Vision and Pattern Recognition Computer Science - Graphics H.5.2
Druh dokumentu:	Working Paper
DOI:	10.1145/3491102.3502141
Popis:	We investigate how multiple sliders with and without feedforward visualizations influence users' control of generative models. In an online study (N=138), we collected a dataset of people interacting with a generative adversarial network (StyleGAN2) in an image reconstruction task. We found that more control dimensions (sliders) significantly increase task difficulty and user actions. Visual feedforward partly mitigates this by enabling more goal-directed interaction. However, we found no evidence of faster or more accurate task performance. This indicates a tradeoff between feedforward detail and implied cognitive costs, such as attention. Moreover, we found that visualizations alone are not always sufficient for users to understand individual control dimensions. Our study quantifies fundamental UI design factors and resulting interaction behavior in this context, revealing opportunities for improvement in the UI design for interactive applications of generative models. We close by discussing design directions and further aspects. Comment: 15 pages, 10 figures, ACM CHI 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.00965 Zobrazit plný text záznamu View this record from Arxiv