Existing approaches for color-concept association typically rely on query-based image referencing, and color extraction from image references. However, these approaches are effective only for common concepts, and are vulnerable to unstable image referencing and varying image conditions. Our formative study with designers underscores the need for primary-accent color compositions and context-dependent colors (e.g., 'clear' vs. 'polluted' sky) in design. In response, we introduce a generative approach for mining semantically resonant colors leveraging images generated by text-to-image models. Our insight is that contemporary text-to-image models can resemble visual patterns from large-scale real-world data. The framework comprises three stages: concept instancing produces generative samples using diffusion models, text-guided image segmentation identifies concept-relevant regions within the image, and color association extracts primarily accompanied by accent colors. Quantitative comparisons with expert designs validate our approach's effectiveness, and we demonstrate the applicability through cases in various design scenarios and a gallery.

Graphical overlays that layer visual elements onto charts, are effective to convey insights and context in financial narrative visualizations. However, automating graphical overlays is challenging due to complex narrative structures and limited understanding of effective overlays. To address the challenge, we first summarize the commonly used graphical overlays and narrative structures, and the proper correspondence between them in financial narrative visualizations, elected by a survey of 1752 layered charts with corresponding narratives. We then design FinFlier, a two-stage innovative system leveraging a knowledge-grounding large language model to automate graphical overlays for financial visualizations. The text-data binding module enhances the connection between financial vocabulary and tabular data through advanced prompt engineering, and the graphics overlaying module generates effective overlays with narrative sequencing. We demonstrate the feasibility and expressiveness of FinFlier through a gallery of graphical overlays covering diverse financial narrative visualizations. Performance evaluations and user studies further confirm system's effectiveness and the quality of generated layered charts.

Dashboards serve as effective visualization tools for conveying complex information. However, there exists a knowledge gap regarding how dashboard designs impact user engagement, necessitating designers to rely on their design expertise. Saliency has been used to comprehend viewing behaviors and assess visualizations, yet existing saliency models are primarily designed for single-view visualizations. To address this, we conduct an eye-tracking study to quantify participants' viewing patterns on dashboards. We collect eye-movement data from 60 participants, each viewing 36 dashboards (16 representative dashboards shared across all and 20 unique to each participant), totaling 1,216 dashboards and 2,160 eye-movement data instances. Analysis of the data from 16 dashboards viewed by all participants provides insights into how dashboard objects and layout designs influence viewing behaviors. Our analysis confirms known viewing patterns and reveals new patterns related to dashboard layout designs. Using the eye-movement data and identified patterns, we develop a saliency model to predict viewing behaviors with dashboards. Compared to state-of-the-art models for single-view visualizations, our model demonstrates overall improvement in prediction performance for dashboards. Finally, we propose potential dashboard design guidelines, illustrate an application case, and discuss general scanning strategies along with limitations and future work.

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.