Skeleton: Visual Authoring of Non-visual Data Experiences

• URL: http://arxiv.org/abs/2607.14579v1
• Date: Thu, 16 Jul 2026 05:16:17 GMT
• Title: Skeleton: Visual Authoring of Non-visual Data Experiences
• Abstract summary: When sighted practitioners author accessible data visualizations, they build navigation structures entirely in code.<n>Without a representation to react to, practitioners cannot develop judgment about what makes navigation good or bad.<n>We introduce an Inspector that renders navigation graphs as interactive node-link diagrams, and a Dimensions API that expresses navigation in terms of data dimensions.<n>We present Skeleton, a direct-manipulation authoring environment in which the properties of an accessible navigation structure are translated into visual representations.
• Score: 18.880086057363766
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: When sighted practitioners author accessible data visualizations, they build navigation structures (the nodes, edges, and input bindings that govern how assistive technologies traverse an interface) entirely in code, with no visual representation. Without a representation to react to, practitioners cannot develop judgment about what makes navigation good or bad, and the quality ceiling of non-visual experiences is set by the absence of a feedback loop. We address this problem through longitudinal co-design with practitioners across cartography, design systems, and open-source visualization, and make three contributions. First, we introduce an Inspector that renders navigation graphs as interactive node-link diagrams, and a Dimensions API that expresses navigation in terms of data dimensions rather than explicit graph construction. Second we present Skeleton, a direct-manipulation authoring environment in which the properties of an accessible navigation structure are translated into visual representations authors can observe and manipulate. Key techniques include a dual-view editor that simultaneously shows the system's navigation model and the end user's spatial experience, a scaffolding engine that automates spatial node placement by repurposing a visualization rendering pipeline, a live label-template editor with real-time screen-reader-output preview, and a testing mode that makes traversal sequence visually trackable. Third, we evaluate Skeleton through an in-situ study with 8 practitioners across visualization design, engineering, and research. Making navigation structure visible changed how practitioners engaged with accessible design: they reconsidered the architecture of their own visualizations, attended to a broader range of input modalities, and shifted from treating accessibility as a compliance task to treating it as a design problem. (abstract shortened for arxiv)

Related papers

• Navigating User Behavior toward Personalized Multimodal Generation [65.58245647055583]
  We study personalized content generation, which turns a user's interaction history into an executable instruction for downstream synthesis.<n>We propose NaviGen, which represents each item with a dual legible coupling a collaborative code and a textual code.<n>Experiments across product, game, and short-video domains show that NaviGen improves personalized image and video generation, strengthens next-item prediction, and yields more specific, relevant, and visually generatable instructions.
  arXiv  Detail & Related papers  (2026-06-23T06:31:21Z)
• In-Video Instructions: Visual Signals as Generative Control [79.44662698914401]
  We investigate whether capabilities can be harnessed for controllable image-to-video generation by interpreting visual signals embedded within the frames as instructions.<n>In-Video Instruction encodes user guidance directly into the visual domain through elements such as overlaid text, arrows, or trajectories.<n>Experiments on three state-of-the-art generators, including Veo 3.1, Kling 2.5, and Wan 2.2, show that video models can reliably interpret and execute such visually embedded instructions.
  arXiv  Detail & Related papers  (2025-11-24T18:38:45Z)
• Aligning Knowledge Graph with Visual Perception for Object-goal Navigation [16.32780793344835]
  We propose the Aligning Knowledge Graph with Visual Perception (AKGVP) method for object-goal navigation. Our approach introduces continuous modeling of the hierarchical scene architecture and leverages visual-language pre-training to align natural language description with visual perception. The integration of a continuous knowledge graph architecture and multimodal feature alignment empowers the navigator with a remarkable zero-shot navigation capability.
  arXiv  Detail & Related papers  (2024-02-29T06:31:18Z)
• NavHint: Vision and Language Navigation Agent with a Hint Generator [31.322331792911598]
  We provide indirect supervision to the navigation agent through a hint generator that provides detailed visual descriptions. The hint generator assists the navigation agent in developing a global understanding of the visual environment. We evaluate our method on the R2R and R4R datasets and achieve state-of-the-art on several metrics.
  arXiv  Detail & Related papers  (2024-02-04T16:23:16Z)
• Learning Navigational Visual Representations with Semantic Map Supervision [85.91625020847358]
  We propose a navigational-specific visual representation learning method by contrasting the agent's egocentric views and semantic maps. Ego$2$-Map learning transfers the compact and rich information from a map, such as objects, structure and transition, to the agent's egocentric representations for navigation.
  arXiv  Detail & Related papers  (2023-07-23T14:01:05Z)
• Augmented reality navigation system for visual prosthesis [67.09251544230744]
  We propose an augmented reality navigation system for visual prosthesis that incorporates a software of reactive navigation and path planning. It consists on four steps: locating the subject on a map, planning the subject trajectory, showing it to the subject and re-planning without obstacles. Results show how our augmented navigation system help navigation performance by reducing the time and distance to reach the goals, even significantly reducing the number of obstacles collisions.
  arXiv  Detail & Related papers  (2021-09-30T09:41:40Z)
• Semantic Snapping for Guided Multi-View Visualization Design [6.8323414329956265]
  We present semantic snapping, an approach to help non-expert users design effective multi-view visualizations. Our method uses an on-the-fly procedure to detect and suggest resolutions for conflicting, misleading, or ambiguous designs.
  arXiv  Detail & Related papers  (2021-09-17T07:40:56Z)
• Deep Learning for Embodied Vision Navigation: A Survey [108.13766213265069]
  "Embodied visual navigation" problem requires an agent to navigate in a 3D environment mainly rely on its first-person observation. This paper attempts to establish an outline of the current works in the field of embodied visual navigation by providing a comprehensive literature survey.
  arXiv  Detail & Related papers  (2021-07-07T12:09:04Z)
• Pathdreamer: A World Model for Indoor Navigation [62.78410447776939]
  We introduce Pathdreamer, a visual world model for agents navigating in novel indoor environments. Given one or more previous visual observations, Pathdreamer generates plausible high-resolution 360 visual observations. In regions of high uncertainty, Pathdreamer can predict diverse scenes, allowing an agent to sample multiple realistic outcomes.
  arXiv  Detail & Related papers  (2021-05-18T18:13:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.