This case study investigates if the AI text-to-image generator Midjourney can generate visual game character idea images for Dota 2 that meet the game’s art guidelines. The author defined the term “visual game character ideas” as the idea images at the early stage of visual character design process to help artists get inspiration. To achieve this, an experiment was designed and conducted where three new Dota 2 heroes’ backgrounds were developed by the author, and 32 images per hero were generated with Midjourney bot. These 96 images were evaluated to examine Midjourney’s performance based on seven aspects: accurate content, readability and identifiability, value gradient, value patterning, the number of colors, areas of rest and detail, and directionality. “YES” was given to each criterion if they meet the requirement. The value of this case study is to present the strength and weakness of the text-to-image generators for visual character design ideas, which can potentially show game artists when and how to use them in visual character design process. The result suggested that Midjourney could be used to generate visual character design ideas for Dota 2 unstably, and this instability was mainly caused by its identified flaw: content accuracy. Furthermore, it performed better for non-color-related aspects, while the performance for color-related items was significantly worse than others.

Tools using artificial intelligence are being developed at a rapid pace. One of these tools is the text-to-image generator DALL·E 2 which has been developed by OPEN-AI and is meant to generate images solely through text input. One of the problems with tools like these is that they are not as good at generating material as AI generators for speech or text are. As DALL·E 2 has been made available to be used for commercial purposes there is a need to evaluate whether the tool is good enough to be used for that purpose. The study was conducted through 3 focus groups with a total of 15 participants. The participants were 20 to 25 years old. During the focus groups, already generated images were discussed and they were allowed to test use of the tool themselves. The theoretical framework Technology Acceptance Model 2(TAM-2) was used to analyze the qualitative data according to its already established determining factors. The result of the study presents area of usage as a new extending factor for the perceived usefulness when using TAM-2 in a qualitative study. The conclusion is drawn that visual material created by DALL·E is not seen as accepted for use in a commercial purpose but its application within certain areas of usage could raise the perceived usefulness, and thereby acceptance. / Verktyg som bygger på artificiell intelligens utvecklas i snabb takt. Ett av dessa verktyg är text-till-bild-generatorn DALL·E 2 som utvecklats av OPEN-AI och är menat att generera bilder utifrån text. Ett problem med verktyg som DALL-E 2 är att de inte är lika bra på att skapa visuellt material som generatorer är för ljud eller text. I och med att verktygen börjat göras tillgängliga kommersiellt så finns det ett behov att undersöka huruvida verktygen är tillräckligt bra för att kunna användas till det. Studien genomfördes med tre fokusgrupper med totalt 15 deltagare. Deltagarna hade en ålder mellan 20 och 25 och man diskuterade acceptans till tidigare genererat bildmaterial samt upplevelsen av att använda verktyget själva. Technology Acceptance Model 2 (TAM-2) användes som teoretiskt ramverk för analys av den kvalitativa datan och för påverkande faktorer. Resultatet presenterar sammanhang som en ny påverkande faktor för den uppfattade användbarheten i användningen av TAM-2 i kvalitativa studier. Slutsatsen dras att bildmaterial skapat av DALL·E 2 inte anses accepterat för användning i ett kommersiellt sammanhang men tillämpning i specifika sammanhang skulle kunna höja den uppfattade användbarheten och därmed acceptansen.