59. Generative AI and Creative Workflows
発表担当
目次
説明
Sketch-to-image systems let users transform simple line drawings into realistic images, but current workflows force users into tedious redraw-regenerate cycles that slow creative exploration. We introduce two complementary interaction techniques that reduce iteration friction: AutoSketch, which extends partial sketches through AI-driven completions (pre-generation support), and BackSketch, which transforms generated images back into editable sketches at multiple abstraction levels (post-generation support). In a study with 30 participants, the results indicate that both techniques can improve exploration and expressiveness compared to a baseline sketch-to-image system, while AutoSketch also can increase users’ sense of agency and co-creation with the AI. We contribute new evidence that shifting support before or after generation opens distinct pathways for balancing user control and system initiative. Together, our results establish pre- and post-generation assistance as a design space for co-creative sketch-to-image systems.
日本語まとめ
説明
Well-designed indoor scenes should prioritize how people can act within a space rather than merely what objects to place. However, existing 3D scene generation methods emphasize visual and semantic plausibility, while insufficiently addressing whether people can comfortably walk, sit, or manipulate objects. To bridge this gap, we present a Behavior-Aware Anthropometric Scene Generation framework. Our approach leverages vision–language models (VLMs) to analyze object–behavior relationships, translating spatial requirements into parametric layout constraints adapted to user-specific anthropometric data. We conducted comparative studies with state-of-the-art models using geometric metrics and a user perception study (N=16). We further conducted in-depth human-scale studies (individuals, N=20; groups, N=18). The results showed improvements in task completion time, trajectory efficiency, and human-object manipulation space. This study contributes a framework that bridges VLM-based interaction reasoning with anthropometric constraints, validated through both technical metrics and real-scale human usability studies.
日本語まとめ
説明
Digital collage is an artistic practice that combines image cutouts to tell stories. However, preparing cutouts from a set of photos remains a tedious and time-consuming task. A formative study identified three main challenges: 1) inefficient search for relevant photos, 2) manual image cutout, and 3) difficulty in organizing large sets of cutouts. To meet these challenges and facilitate asset preparation for collage, we propose Collaposer, a tool that transforms a collection of photos into organized, ready-to-use visual cutouts based on user-provided story descriptions. Collaposer tags, detects, and segments photos, and then uses an LLM to select central and related labels based on the user-provided story description. Collaposer presents the resulting visuals in varying sizes, clustered according to semantic hierarchy. Our evaluation shows that Collaposer effectively automates the preparation process to produce diverse sets of visual cutouts adhering to the storyline, allowing users to focus on collaging these assets for storytelling.
日本語まとめ
説明
Creative tasks require creators to iteratively produce, select, and discard potentially useful ideas. Now, creativity tools include generative AI features (e.g., Photoshop Generative Fill) that increase the number of alternatives creators consider through rapid experiments with prompts and random generations. Creators use tedious manual systems for organizing their prior ideas by saving file versions or hiding layers, but they lack the support they want for reusing prior alternatives in personal work or in communication with others. We present HistoryPalette, a system that supports exploration and reuse of prior designs in generative image creation and editing. Using HistoryPalette, creators and their collaborators explore a "palette" of prior design alternatives organized by spatial position, topic category, and creation time. HistoryPalette enables creators to quickly preview and reuse their prior work. In creative professional and client collaborator user studies, participants generated and edited images by exploring and reusing past design alternatives with HistoryPalette.
日本語まとめ
説明
While large language models (LLMs) are increasingly applied in creative domains, their role in supporting interactive storytelling tailored to creators’ needs remains underexplored. This thesis adopts a creator-centered perspective to examine how LLMs can assist in building interactive narratives, focusing on multi-layered structure editing, automated analysis, target user feedback, and the preservation of authorial control. A multi-stage design was employed: interviews with sixteen creators identified five key design goals, which informed the development of \textit{CoNoder}, a prototype integrating node-graph editing, dual interaction modes, and generation styles, ripple-effect analysis, and simulated feedback. Evaluation results show that \textit{CoNoder} improves creative efficiency, supports morally complex storytelling, and provides structured narrative feedback, though onboarding, expert guidance, and finer control remain areas for improvement. Overall, this research contributes a creator-focused framework and a practical system design approach, highlighting the need for future tools that balance expressive freedom with creative sovereignty.
日本語まとめ
説明
A new generation of GenAI tools fueled by vibe coding practices promises to democratize software development, explicitly targeting users without programming backgrounds. Yet, we lack understanding of how technical and non-technical users actually engage with these tools across the product development lifecycle. We conducted a mixed-methods study combining an online survey (N=85) with interviews of hackathon participants (N=31) and practitioners (N=8), examining how different user groups employ chatbots, local development assistants, and cloud development environments from ideation through deployment. Our findings reveal that cloud development environments accelerate prototyping, enabling non-technical users to generate high-fidelity "throw-away" prototypes valuable for experiential exploration. However, deployment and long-term maintainability remain dependent on technical expertise, with non-technical users consistently encountering barriers when transitioning beyond prototyping. We contribute a comparative analysis of how technical and non-technical users appropriate GenAI tools across the full product development cycle in contexts approximating real-world product building, highlighting implications for tool design and educational practices.