Effective visualization is essential for cultural heritage interpretation. However, existing visualization systems remain constrained by fragmented data integration and limited exploration capabilities for multimodal heritage data. This paper presents HeritageExplorer, an interactive system that synergizes large language models (LLMs) with dynamic visualizations to enable progressive heritage exploration. Our approach constructs a comprehensive knowledge graph integrating 831 historic buildings in Guangzhou, which unifies their architectural, spatial, temporal, and contextual attributes. The system’s novel integration of KG-enhanced contextual understanding with LLMs supports: natural language query understanding and seamless coupling with interactive visualizations. Quantitative evaluation demonstrates consistent improvements in factual accuracy across heritage tasks, while case studies illustrate its successful application in diverse exploration scenarios.

Scene creation in video games is an iterative process that transitions from initial ideas to 2D visuals and finally to 3D scenes, requiring collaborative teamwork among gameplay and environment designers, 3D modelers, and technical artists. While advances in generative methods have enabled the automation of scene creation, these end-to-end approaches often do not align with the typical workflow. This paper presents SceneWeaver, a 3D scene creation system that utilizes a multi-agent collaborative framework with large language models (LLMs) assigned to manage text parsing, floorplan design, object selection, and scene composition. An interactive visual interface with step-by-step previsualization to enhance human-AI communication and collaboration throughout the scene creation process. SceneWeaver effectively enhances user engagement and improves process controllability in 3D scene creation, as confirmed by performance evaluations and user studies.

Landscape renderings are realistic images of landscape sites, allowing stakeholders to perceive better and evaluate design ideas. While recent advances in Generative Artificial Intelligence (GAI (generative artificial intelligence)) enable automated generation of landscape renderings, the End to End (endtoend) methods are not compatible with common design processes, leading to insufficient alignment with design idealizations and limited cohesion of iterative landscape design. Informed by a formative study for comprehending design requirements, we present PlantoGraphy, an iterative design system that allows for interactive configuration of generative artificial intelligence models to accommodate human-centered design practice. A two-stage pipeline is incorporated: first, the concretization module transforms conceptual ideas into concrete scene layouts with a domain-oriented large language model; and second, the illustration module converts scene layouts into realistic landscape renderings with a layout-guided diffusion model Fine-tune (finetune)ed through Low-Rank Adaptation (LoRA) (lora). PlantoGraphy has undergone a series of performance evaluations and user studies, demonstrating its effectiveness in landscape rendering generation and the high recognition of its interactive functionality.

High-quality visualization collections are beneficial for a variety of applications including visualization reference and data-driven visualization design. The visualization community has created many visualization collections, and developed interactive exploration systems for the collections. However, the systems are mainly based on extrinsic attributes like authors and publication years, whilst neglect intrinsic property (i.e., visual appearance) of visualizations, hindering visual comparison and query of visualization designs. This paper presents VISAtlas, an image-based approach empowered by neural image embedding, to facilitate exploration and query for visualization collections. To improve embedding accuracy, we create a comprehensive collection of synthetic and real-world visualizations, and use it to train a convolutional neural network (CNN) model with a triplet loss for taxonomical classification of visualizations. Next, we design a coordinated multiple view (CMV) system that enables multi-perspective exploration and design retrieval based on visualization embeddings. Specifically, we design a novel embedding overview that leverages contextual layout framework to preserve the context of the embedding vectors with the associated visualization taxonomies, and density plot and sampling techniques to address the overdrawing problem. We demonstrate in three case studies and one user study the effectiveness of VISAtlas in supporting comparative analysis of visualization collections, exploration of composite visualizations, and image-based retrieval of visualization designs. The studies reveal that real-world visualization collections (e.g., Beagle and VIS30K) better accord with the richness and diversity of visualization designs than synthetic collections (e.g., Data2Vis), inspiring composite visualizations are identified in real-world collections, and distinct design patterns exist in visualizations from different sources.

Data videos are becoming increasingly popular in society and academia. Yet little is known about how to create endings that strengthen a lasting impression and persuasion. To fulfill the gap, this work aims to develop guidelines for data video endings by drawing inspiration from cinematic arts. To contextualize cinematic endings in data videos, 111 film endings and 105 data video endings are first analyzed to identify four common styles using the framework of ending punctuation marks. We conducted expert interviews (N=11) and formulated 20 guidelines for creating cinematic endings in data videos. To validate our guidelines, we conducted a user study where 24 participants were invited to design endings with and without our guidelines, which are evaluated by experts and the general public. The participants praise the clarity and usability of the guidelines, and results show that the endings with guidelines are perceived to be more understandable, impressive, and reflective.