Design spaces serve as a conceptual framework that enables designers to explore feasible solutions, yet their lack of computational formalization limits integration with AI-driven design automation. To address this, we introduce a structured design space model that formalizes design spaces with orthogonal dimensions and discrete elements, making them machine-interpretable and executable. Building on this model, we present IDEA, a fully automated design exploration framework to generate effective outcomes based on user requirements and design spaces. Specifically, IDEA leverages large language models (LLMs) for constraint generation, incorporates a constraint-guided Monte Carlo Tree Search (MCTS) algorithm to explore the space, and instantiates abstract decisions into domain-specific implementations. We evaluate IDEA in two design scenarios: data-driven article and standard visualization, supported by comparativeratings, expert interviews, and quantitative experiments. Results demonstrate the IDEA's adaptability across domains and its capability to produce high-quality designs.

Embedded data visualizations have emerged as a powerful narrative medium for conveying complex information within video footage. However, creating such content remains labor-intensive, as existing workflows rely on manual frame-by-frame adjustments to ensure spatial and temporal consistency. To address these challenges, we present ChartBlender, an interactive authoring system designed to streamline the creation, embedding, and automatic synchronization of data visualizations within video scenes. We develop a tracking pipeline that supports both object and camera tracking, ensuring robust alignment of visualizations with dynamic video content. To maintain visual clarity and aesthetic coherence, we also explore the design space of video-suited visualizations and develop a library of customizable templates optimized for video embedding. We evaluated ChartBlender through two controlled experiments and expert interviews with five domain experts. Results show that our system enables accurate synchronization and accelerates the production of data-driven videos.

Music videos, as a prevalent form of multimedia entertainment, deliver engaging audio-visual experiences to audiences and have gained immense popularity among singers and fans. Creators can express their interpretations of music naturally through visual elements. However, the creation process of music video demands proficiency in script design, video shooting, and music-video synchronization, posing significant challenges for non-professionals. Previous work has designed automated music video generation frameworks. However, they suffer from complexity in input and poor output quality. In response, we present MV-Crafter, a system capable of producing high-quality music videos with synchronized music-video rhythm and style. Our approach involves three technical modules that simulate the human creation process: the script generation module, video generation module, and music-video synchronization module. MV-Crafter leverages a large language model to generate scripts considering the musical semantics. To address the challenge of synchronizing short video clips with music of varying lengths, we propose a dynamic beat-matching algorithm and visual envelope-induced warping method to ensure precise, monotonic music-video synchronization. Besides, we design a user-friendly interface to simplify the creation process with intuitive editing features. Extensive experiments have demonstrated that MV-Crafter provides an effective solution for improving the quality of generated music videos.