Generative AI systems allow visual designers to rapidly produce many image alternatives, but preserving, revisiting, and building on promising partial results across iterations remains challenging. In practice, generated images are rarely valuable as complete wholes; instead, designers often reuse fragments, revisit earlier materials, and combine partial ideas into new directions. We present Trinketry, an element-centered visual exploration system built around trinkets: reusable visual and semantic elements extracted from prior generations. Trinketry allows users to extract image regions, whole images, text labels, and composite elements; recombine them in intention trays to generate new variations; trace the provenance of specific elements across the exploration process; and retrieve semantically related prior outcomes. By foregrounding extraction, recombination, tracing, and retrieval, this demo presents an alternative interaction model for AI-assisted visual exploration—one that supports traceable reuse, intentional iteration, and reflective design exploration.

Current co-op games keep collaboration virtual even when players are physically co-located in the same room, limiting embodied coordination in the shared space. We introduce MoveTogether, a novel physical co-op gameplay in which two players jointly operate a single, tracked prop, adding a shared physical communication channel on top of visual and audio cues. To explore the design space in mixed reality, we conducted a workshop with 10 professional designers, generating a physical co-op design space that encompasses prop and interaction design patterns, and how they relate to affordance and cooperative experience. In a within-subjects study of virtual vs. physical co-op experiences (n=16), we observed finer-grained task coordination, fewer collisions, and more strategy-focused communication. Players reported higher collaboration, sense of achievement, enjoyment, and overall preference for physical co-op. This work opens a new design space for co-located play and offers guidance for designing embodied co-op experiences.

Concept designers in the entertainment industry create highly detailed, often imaginary environments for movies, games, and TV shows. Their early ideation phase requires intensive research, brainstorming, visual exploration, and combination of various design elements to form cohesive designs. However, existing AI tools focus on image generation from user specifications, lacking support for the unique needs and complexity of concept designers' workflows. Through a formative study with 12 professional designers, we captured their workflows and identified key requirements for AI-assisted ideation tools. Leveraging these insights, we developed AIdeation to support early ideation by brainstorming design concepts with flexible searching and recombination of reference images. A user study with 16 professional designers showed that AIdeation significantly enhanced creativity, ideation efficiency, and satisfaction (all p<.01) compared to current tools and workflows. A field study with 4 studios for 1 week provided insights into AIdeation's benefits and limitations in real-world projects. After the completion of the field study, two studios, covering films, television, and games, have continued to use AIdeation in their commercial projects to date, further validating AIdeation's improvement in ideation quality and efficiency.

Environment designers in the entertainment industry create imaginative 2D and 3D scenes for games, films, and television, requiring both fine-grained control of specific details and consistent global coherence. Designers have increasingly integrated generative AI into their workflows, often relying on large language models (LLMs) to expand user prompts for text-to-image generation, then iteratively refining those prompts and applying inpainting. However, our formative study with 10 designers surfaced two key challenges: (1) the lengthy LLM-generated prompts make it difficult to understand and isolate the keywords that must be revised for specific visual elements; and (2) while inpainting supports localized edits, it can struggle with global consistency and correctness. Based on these insights, we present GenTune, an approach that enhances human--AI collaboration by clarifying how AI-generated prompts map to image content. Our GenTune system lets designers select any element in a generated image, trace it back to the corresponding prompt labels, and revise those labels to guide precise yet globally consistent image refinement. In a summative study with 20 designers, GenTune significantly improved prompt--image comprehension, refinement quality, and efficiency, and overall satisfaction (all $p < .01$) compared to current practice. A follow-up field study with two studios further demonstrated its effectiveness in real-world settings.