The aesthetic design of 3D scenes in game content enhances players' experience by inducing desired emotions. Creating emotionally engaging scenes involves designing low-level features, such as color distribution, contrast, and brightness. This study presents LumiMood, an AI-driven creativity support tool (CST) that automatically adjusts lighting and post-processing to create moods for 3D scenes. LumiMood supports designers by synthesizing reference images, creating mood templates, and providing intermediate design steps. Our formative study with 10 designers identified distinct challenges in mood design based on the participants' experience levels. A user study involving 40 designers revealed that using LumiMood benefits the designers by streamlining workflow, improving precision, and increasing mood intention accuracy. Results indicate that LumiMood supports clarifying mood concepts and improves interpretation of lighting and post-processing, thus resolving the challenges. We observe the effect of template based designing and discuss considerable factors for AI-driven CSTs for users with varying levels of experiences.
doi.org/10.1145/3613904.3642440
Interior color design is a creative process that endeavors to allocate colors to furniture and other elements within an interior space. While much research focuses on generating realistic interior designs, these automated approaches often misalign with user intention and disregard design rationales. Informed by a need-finding preliminary study, we develop C2Ideas, an innovative system for designers to creatively ideate color schemes enabled by an intent-aligned and domain-oriented large language model. C2Ideas integrates a three-stage process: Idea Prompting stage distills user intentions into color linguistic prompts; Word-Color Association stage transforms the prompts into semantically and stylistically coherent color schemes; and Interior Coloring stage assigns colors to interior elements complying with design principles. We also develop an interactive interface that enables flexible user refinement and interpretable reasoning. C2Ideas has undergone a series of indoor cases and user studies, demonstrating its effectiveness and high recognition of interactive functionality by designers.
doi.org/10.1145/3613904.3642224
Semantic typographic logos harmoniously blend typeface and imagery to represent semantic concepts while maintaining legibility. Conventional methods using spatial composition and shape substitution are hindered by the conflicting requirement for achieving seamless spatial fusion between geometrically dissimilar typefaces and semantics. While recent advances made AI generation of semantic typography possible, the end-to-end approaches exclude designer involvement and disregard personalized design. This paper presents TypeDance, an AI-assisted tool incorporating design rationales with the generative model for personalized semantic typographic logo design. It leverages combinable design priors extracted from uploaded image exemplars and supports type-imagery mapping at various structural granularity, achieving diverse aesthetic designs with flexible control. Additionally, we instantiate a comprehensive design workflow in TypeDance, including ideation, selection, generation, evaluation, and iteration. A two-task user evaluation, including imitation and creation, confirmed the usability of TypeDance in design across different usage scenarios.
doi.org/10.1145/3613904.3642185
Studies of Generative AI (GenAI)-assisted creative workflows have focused on individuals overcoming challenges of prompting to produce what they envisioned. When designers work in teams, how do collaboration and prompting influence each other, and how do users perceive generative AI and their collaborators during the co-prompting process? We engaged students with design or performance backgrounds, and little exposure to GenAI, to work in pairs with GenAI to create stage designs based on a creative theme. We found two patterns of collaborative prompting focused on generating story descriptions first, or visual imagery first. GenAI tools helped participants build consensus in the task, and allowed for discussion of the prompting strategies. Participants perceived GenAI as efficient tools rather than true collaborators, suggesting that human partners reduced the reliance on their use. This work highlights the importance of human-human collaboration when working with GenAI tools, suggesting systems that take advantage of shared human expertise in the prompting process.
doi.org/10.1145/3613904.3642133
This paper presents a study that examines developer perceptions and usage of generative AI (GAI) in a summer professional development program for game development interns focused on mobile game design. GAI applications are in common usage worldwide, yet the impacts of this technology in game development remain relatively underexplored. Through a qualitative study using ethnographic interviews and participatory observation, this paper explores how GAI impacted the workflows, creative processes, and professional identities of early career game developers. We present a case of GAI integration that was not a straightforward adoption. Focusing on the interns' resistance, negotiation, and reimagining, we show that the interns were actively developing a new professional culture both with and against generative AI. For the interns, their ethical commitments to fellow game developers and the future of their profession were as important as their practical concerns about usability, utility, and efficacy of GAI tools.
doi.org/10.1145/3613904.3641889