arXiv subjects

Jun-Hsiang Yao

Publications and source records attributed to Jun-Hsiang Yao.

4 recordsLinked to original sources

arXiv2026

InfoAlign: A Human-AI Co-Creation System for Storytelling with Infographics

Storytelling infographics are a powerful medium for communicating data-driven stories through visual presentation. However, existing authoring tools lack support for maintaining story consistency and aligning with users' story goals throughout the design process. To address this gap, we conducted formative interviews and a quantitative analysis to identify design needs and common story-informed layout patterns in infographics. Based on these insights, we propose a narrative-centric workflow for infographic creation consisting of three phases: story construction, visual encoding, and spatial composition. Building on this workflow, we developed InfoAlign, a human-AI co-creation system that transforms long or unstructured text into stories, recommends semantically aligned visual designs, and generates layout blueprints. Users can intervene and refine the design at any stage, ensuring their intent is preserved and the infographic creation process remains transparent. Evaluations show that InfoAlign preserves story coherence across authoring stages and effectively supports human-AI co-creation for storytelling infographic design.

cs.HC↗

arXiv2025

VidAnimator: User-Guided Stylized 3D Character Animation from Human Videos

With captivating visual effects, stylized 3D character animation has gained widespread use in cinematic production, advertising, social media, and the potential development of virtual reality (VR) non-player characters (NPCs). However, animating stylized 3D characters often requires significant time and effort from animators. We propose a mixed-initiative framework and interactive system to enable stylized 3D characters to mimic motion in human videos. The framework takes a single-view human video and a stylized 3D character (the target character) as input, captures the motion of the video, and then transfers the motion to the target character. In addition, it involves two interaction modules for customizing the result. Accordingly, the system incorporates two authoring tools that empower users with intuitive modification. A questionnaire study offers tangible evidence of the framework's capability of generating natural stylized 3D character animations similar to the motion in the video. Additionally, three case studies demonstrate the utility of our approach in creating diverse results.

cs.HC↗

arXiv2025

Beyond the Broadcast: Enhancing VR Tennis Broadcasting through Embedded Visualizations and Camera Techniques

Virtual Reality (VR) broadcasting has emerged as a promising medium for providing immersive viewing experiences of major sports events such as tennis. However, current VR broadcast systems often lack an effective camera language and do not adequately incorporate dynamic, in-game visualizations, limiting viewer engagement and narrative clarity. To address these limitations, we analyze 400 out-of-play segments from eight major tennis broadcasts to develop a tennis-specific design framework that effectively combines cinematic camera movements with embedded visualizations. We further refine our framework by examining 25 cinematic VR animations, comparing their camera techniques with traditional tennis broadcasts to identify key differences and inform adaptations for VR. Based on data extracted from the broadcast videos, we reconstruct a simulated game that captures the players' and ball's motion and trajectories. Leveraging this design framework and processing pipeline, we develope Beyond the Broadcast, a VR tennis viewing system that integrates embedded visualizations with adaptive camera motions to construct a comprehensive and engaging narrative. Our system dynamically overlays tactical information and key match events onto the simulated environment, enhancing viewer comprehension and narrative engagement while ensuring perceptual immersion and viewing comfort. A user study involving tennis viewers demonstrate that our approach outperforms traditional VR broadcasting methods in delivering an immersive, informative viewing experience.

cs.HC↗

arXiv2025

DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research

The Digital Twin Brain (DTB) is an advanced artificial intelligence framework that integrates spiking neurons to simulate complex cognitive functions and collaborative behaviors. For domain experts, visualizing the DTB's simulation outcomes is essential to understanding complex cognitive activities. However, this task poses significant challenges due to DTB data's inherent characteristics, including its high-dimensionality, temporal dynamics, and spatial complexity. To address these challenges, we developed DTBIA, an Immersive Visual Analytics System for Brain-Inspired Research. In collaboration with domain experts, we identified key requirements for effectively visualizing spatiotemporal and topological patterns at multiple levels of detail. DTBIA incorporates a hierarchical workflow - ranging from brain regions to voxels and slice sections - along with immersive navigation and a 3D edge bundling algorithm to enhance clarity and provide deeper insights into both functional (BOLD) and structural (DTI) brain data. The utility and effectiveness of DTBIA are validated through two case studies involving with brain research experts. The results underscore the system's role in enhancing the comprehension of complex neural behaviors and interactions.

cs.HC↗