Reflecting on Design Paradigms of Animated Data Video Tools

TL;DR

The paper tackles the challenge of creating data videos by introducing a two-dimensional framework that separates what data-video components to create and coordinate from how to support their production. It analyzes 46 data-video tools across three expressivity levels—Animation Unit, Animated Narrative, and Audio-Enriched Data Videos—and four transformation modes—Original, Human-Led, Mixed-Initiative, and AI-Led—to reveal design paradigms for visual, motion, narrative, and audio components. The authors provide detailed reflections on gaps, including component expansion, representation languages, user-intent modeling, reliability of AI, and evaluation frameworks, and discuss future directions to bridge these gaps. The work offers concrete guidance for researchers and tool builders to develop more capable, expressive, and trustworthy data-video authoring tools, with implications for both research and domain applications across 2D data storytelling. Overall, the framework and findings aim to catalyze more systematic, computable, and user-centered designs for next-generation data-video tools.

Abstract

Animated data videos have gained significant popularity in recent years. However, authoring data videos remains challenging due to the complexity of creating and coordinating diverse components (e.g., visualization, animation, audio, etc.). Although numerous tools have been developed to streamline the process, there is a lack of comprehensive understanding and reflection of their design paradigms to inform future development. To address this gap, we propose a framework for understanding data video creation tools along two dimensions: what data video components to create and coordinate, including visual, motion, narrative, and audio components, and how to support the creation and coordination. By applying the framework to analyze 46 existing tools, we summarized key design paradigms of creating and coordinating each component based on the varying work distribution for humans and AI in these tools. Finally, we share our detailed reflections, highlight gaps from a holistic view, and discuss future directions to address them.

Figures (7)

• Figure 1: Overview of papers about data video creation tools across publication years and venues.
• Figure 2: Illustrations of data video components along a timeline from left to right, each serving a distinct role. They are divided into four categories: visual components, motion components, narrative, and audio components.
• Figure 3: Data video with three levels of expressivity. Greater diversity in data video components enhances expressive potential but also increases authoring complexity, due to involving more components and introducing new coordination relationships.
• Figure 4: The roles of humans and AI in transforming diverse user inputs into coordinated data video components.
• Figure 5: Animation authoring paradigms: (a) keyframe animations Thompson2021; (b) animation preset wonderflow; and (c) procedural animations Lu2020a.