The rapid advancement of AI-generated content (AIGC) promises to transform various aspects of human life significantly. This work particularly focuses on the potential of AIGC to revolutionize image creation, such as photography and self-expression. We introduce ContextCam, a novel human-AI image co-creation system that integrates context awareness with mainstream AIGC technologies like Stable Diffusion. ContextCam provides user's image creation process with inspiration by extracting relevant contextual data, and leverages Large Language Model-based (LLM) multi-agents to co-create images with the user. A study with 16 participants and 136 scenarios revealed that ContextCam was well-received, showcasing personalized and diverse outputs as well as interesting user behavior patterns. Participants provided positive feedback on their engagement and enjoyment when using ContextCam, and acknowledged its ability to inspire creativity.
doi.org/10.1145/3613904.3642129
InkBrush is a new sketch-based 3D drawing tool for creating 3D ink paintings using free-form 3D ink strokes. It offers a digital calligraphy brush and various editing tools to generate realistic ink-like brush strokes with attributes like hairy edges, ink drips, and scattered dots. Users can adjust parameters such as moisture, color, darkness, dryness, and stroke style to customize the appearance of the brush strokes. The development of InkBrush was guided by a design study involving artists and designers. It was developed as a plugin for Blender, a popular 3D modeling tool, and its effectiveness and usability were evaluated through a user study involving 75 participants. Preliminary feedback from the participants was overwhelmingly positive, indicating that InkBrush was intuitive and easy to use. Following this, we also sought in-depth assessments from experts in ink painting and 3D design. Their evaluations further demonstrated the effectiveness of InkBrush.
doi.org/10.1145/3613904.3642128
Mixed-media tutorials, which integrate videos, images, text, and diagrams to teach procedural skills, offer more browsable alternatives than timeline-based videos. However, manually creating such tutorials is tedious, and existing automated solutions are often restricted to a particular domain. While AI models hold promise, it is unclear how to effectively harness their powers, given the multi-modal data involved and the vast landscape of models. We present TutoAI, a cross-domain framework for AI-assisted mixed-media tutorial creation on physical tasks. First, we distill common tutorial components by surveying existing work; then, we present an approach to identify, assemble, and evaluate AI models for component extraction; finally, we propose guidelines for designing user interfaces (UI) that support tutorial creation based on AI-generated components. We show that TutoAI has achieved higher or similar quality compared to a baseline model in preliminary user studies.
doi.org/10.1145/3613904.3642443
Computational notebooks are widely utilized for exploration and analysis. However, creating slides to communicate analysis results from these notebooks is quite tedious and time-consuming. Researchers have proposed automatic systems for generating slides from notebooks, which, however, often do not consider the process of users conceiving and organizing their messages from massive code cells. Those systems ask users to go directly into the slide creation process, which causes potentially ill-structured slides and burdens in further refinement. Inspired by the common and widely recommended slide creation practice: drafting outlines first and then adding concrete content, we introduce OutlineSpark, an AI-powered slide creation tool that generates slides from a slide outline written by the user. The tool automatically retrieves relevant notebook cells based on the outlines and converts them into slide content. We evaluated OutlineSpark with 12 users. Both the quantitative and qualitative feedback from the participants verify its effectiveness and usability.
doi.org/10.1145/3613904.3642865
Design space exploration (DSE) for Text-to-Image (TTI) models entails navigating a vast, opaque space of possible image outputs, through a commensurately vast input space of hyperparameters and prompt text. Perceptually small movements in prompt-space can surface unexpectedly disparate images. How can interfaces support end-users in reliably steering prompt-space explorations towards interesting results? Our design probe, DreamSheets, supports user-composed exploration strategies with LLM-assisted prompt construction and large-scale simultaneous display of generated results, hosted in a spreadsheet interface. Two studies, a preliminary lab study and an extended two-week study where five expert artists developed custom TTI sheet-systems, reveal various strategies for targeted TTI design space exploration---such as using templated text generation to define and layer semantic ``axes'' for exploration. We identified patterns in exploratory structures across our participants' sheet-systems: configurable exploration ``units'' that we distill into a UI mockup, and generalizable UI components to guide future interfaces.
doi.org/10.1145/3613904.3642858