arXiv subjects

Lin-Ping Yuan

Publications and source records attributed to Lin-Ping Yuan.

13 recordsLinked to original sources

arXiv2026

CRAFT: Exploring Wearable Creative AI on Smart Glasses for Fiction Writing in Real-World Contexts

Creative writing increasingly integrates AI assistance, yet current tools miss in-situ moments when writers draw inspiration from real-world experiences. We envision Context-aware Reality-Fiction Transformation (CRAFT), an approach for AI glasses that translates daily experiences into fiction narratives. We explored its desirability, feasibility, and potential viability through three studies. Interviews with nine writers yielded desires and three design goals: 1) augmenting in-situ perception to bridge reality-fiction gaps, 2) promoting authenticity grounded in real-world experiences while maintaining fictionalization, and 3) preserving creative agency, enjoyment, and life-art boundaries. Co-design workshops with 16 writers and researchers operationalized these goals into concrete interaction mechanisms using a technology probe. We then conducted supported field trials with eight writers across 24 sessions using a refined probe, revealing writer-perceived benefits (e.g., enriched fictional ideas from serendipitous encounters), emergent practices (e.g., micro-creation), and design considerations for future sustained use. We contribute design explorations for the CRAFT approach, offering design implications and empirical insights on ubiquitous human-AI creative collaboration in everyday life.

cs.HC↗

arXiv2026

Spatula: Exploring On-Demand In-Situ Interfaces and Interaction for Attribute Control

Controlling attributes is a critical step toward achieving the final creative outcome, yet current approaches fall short in supporting users in the iterative refinement of generative content. We propose Spatula, a proof-of-concept system that generates on-demand, in-situ attribute control interfaces and interactions for creating motion graphics. Building on a technical probe that automatically analyzes animation context and generates corresponding attributes and UI, we frame attribute control as an explorable landscape and explore the attribute control space along four key dimensions: Discoverability, Resolution, Scope, and Expandability. Findings from a user study (N=12) show that our system provides intuitive and convenient interactions while supporting diverse needs for fine-grained parameter control. Furthermore, our applications demonstrate that the plug-and-play design generalizes to other domains, such as web design and 3D modeling.

cs.HC↗

arXiv2026

EmoSpace: Immersive Affective Image Generation Guided by Fine-Grained Emotion Prototypes

Immersive affective content generation aims to create visually compelling VR imagery with controllable emotional nuance, yet existing methods typically rely on coarse labels or prompt-only control. Although modern diffusion transformers (DiTs) such as FLUX improve visual fidelity, they are not designed to incorporate structured affective representations. We present EmoSpace, an immersive affective content generation framework guided by fine-grained emotion prototypes, transforming free-form text and emotion descriptions into fine-grained affective imagery through three coordinated components. First, to represent sub-emotion variation beyond conventional categorical models, EmoSpace learns a hierarchical bank of 256 prototypes with input-conditioned adaptation through vision-language alignment. Second, Prototype-Conditioned Steering converts these prototypes into DiT-compatible generation signals through multi-pathway injection and temporal blending, while Iterative Prompt Refinement enriches prompts with prototype-aligned sub-emotion descriptors. Third, Affect-Grounded Modulation coordinates emotion conditioning with controllable LoRAs for panoramic, stylized, and multi-conditional generation. Through quantitative and qualitative evaluations, EmoSpace improves fine-grained emotional alignment while maintaining high aesthetic quality. Our user study shows that EmoSpace outputs are perceived as more emotionally aligned than baseline results and more suitable for immersive scene design. Additionally, we find that immersive presentation alters emotional perception and increases emotional engagement. Together, these findings inform the design of emotion-aware generative systems for immersive media, with potential applications including education, immersive storytelling, and artistic creation. We will release our code and models to facilitate future research along this line.

cs.CV↗

arXiv2026

SketchDynamics: Exploring Free-Form Sketches for Dynamic Intent Expression in Animation Generation

Sketching provides an intuitive way to convey dynamic intent in animation authoring (i.e., how elements change over time and space), making it a natural medium for automatic content creation. Yet existing approaches often constrain sketches to fixed command tokens or predefined visual forms, overlooking their freeform nature and the central role of humans in shaping intention. To address this, we introduce an interaction paradigm where users convey dynamic intent to a vision-language model via free-form sketching, instantiated here in a sketch storyboard to motion graphics workflow. We implement an interface and improve it through a three-stage study with 24 participants. The study shows how sketches convey motion with minimal input, how their inherent ambiguity requires users to be involved for clarification, and how sketches can visually guide video refinement. Our findings reveal the potential of sketch and AI interaction to bridge the gap between intention and outcome, and demonstrate its applicability to 3D animation and video generation.

cs.HC↗

arXiv2025

NoteEx: Interactive Visual Context Manipulation for LLM-Assisted Exploratory Data Analysis in Computational Notebooks

Computational notebooks have become popular for Exploratory Data Analysis (EDA), augmented by LLM-based code generation and result interpretation. Effective LLM assistance hinges on selecting informative context -- the minimal set of cells whose code, data, or outputs suffice to answer a prompt. As notebooks grow long and messy, users can lose track of the mental model of their analysis. They thus fail to curate appropriate contexts for LLM tasks, causing frustration and tedious prompt engineering. We conducted a formative study (n=6) that surfaced challenges in LLM context selection and mental model maintenance. Therefore, we introduce NoteEx, a JupyterLab extension that provides a semantic visualization of the EDA workflow, allowing analysts to externalize their mental model, specify analysis dependencies, and enable interactive selection of task-relevant contexts for LLMs. A user study (n=12) against a baseline shows that NoteEx improved mental model retention and context selection, leading to more accurate and relevant LLM responses.

cs.HC↗

arXiv2025

Pre/Absence: Prompting Cultural Awareness and Understanding for Lost Architectural Heritage in Virtual Reality

Lost architectural heritage presents interpretive challenges due to vanished structures and fragmented historical records. Using Hanyuan Hall of the Tang dynasty's Daming Palace as a case study, we conducted a formative investigation with archaeologists, heritage administrators, and visitors to identify key issues in current interpretation practices. We found that these practices often compress complex cultural layers into factual summaries and rely on linear narratives that overlook the continuing reinterpretations following a site's disappearance. In response, we designed Pre/Absence, a virtual reality experience grounded in the presence-absence dialectic to interweave tangible and vanished aspects of heritage within a spatiotemporal narrative. A mixed-method study with 28 participants compared Pre/Absence to a paper-based experience. Both improved users' factual understanding, but the VR experience more strongly enhanced cultural awareness, evoked emotional engagement with loss, and encouraged critical reflection on the evolving social and political meanings of heritage. The findings suggest that VR can move beyond static reconstruction to engage users as co-constructors of cultural meaning, providing a nuanced framework for critical heritage narrative design in human-computer interaction.

cs.HC↗

arXiv2025

KinemaFX: A Kinematic-Driven Interactive System for Particle Effect Exploration and Customization

Particle effects are widely used in games and animation to simulate natural phenomena or stylized visual effects. However, creating effect artworks is challenging for non-expert users due to their lack of specialized skills, particularly in finding particle effects with kinematic behaviors that match their intent. To address these issues, we present KinemaFX, a kinematic-driven interactive system, to assist non-expert users in constructing customized particle effect artworks. We propose a conceptual model of particle effects that captures both semantic features and kinematic behaviors. Based on the model, KinemaFX adopts a workflow powered by Large Language Models (LLMs) that supports intent expression through combined semantic and kinematic inputs, while enabling implicit preference-guided exploration and subsequent creation of customized particle effect artworks based on exploration results. Additionally, we developed a kinematic-driven method to facilitate efficient interactive particle effect search within KinemaFX via structured representation and measurement of particle effects. To evaluate KinemaFX, we illustrate usage scenarios and conduct a user study employing an ablation approach. Evaluation results demonstrate that KinemaFX effectively supports users in efficiently and customarily creating particle effect artworks.

cs.HC↗

arXiv2025

Virtual Reality in Manufacturing Education: A Scoping Review Indicating State-of-the-Art, Benefits, and Challenges Across Domains, Levels, and Entities

To address the shortage of a skilled workforce in the U.S. manufacturing industry, immersive Virtual Reality (VR)-based training solutions hold promising potential. To effectively utilize VR to meet workforce demands, it is important to understand the role of VR in manufacturing education. Therefore, we conduct a scoping review in the field. As a first step, we used a 5W1H (What, Where, Who, When, Why, How) formula as a problem-solving approach to define a comprehensive taxonomy that can consider the role of VR from all relevant possibilities. Our taxonomy categorizes VR applications across three key aspects: (1) Domains, (2) Levels, and (3) Entities. Using a systematic literature search and analysis, we reviewed 108 research articles to find the current state, benefits, challenges, and future opportunities of VR in the field. It was found that VR has been explored in a variety of areas and provides numerous benefits to learners. Despite these benefits, its adoption in manufacturing education is limited. This review discusses the identified barriers and provides actionable insights to address them. These insights can enable the widespread usage of immersive technology to nurture and develop a workforce equipped with the skills required to excel in the evolving landscape of manufacturing.

cs.HC↗

arXiv2025

Intuit: Explain Quantum Computing Concepts via AR-based Analogy

Quantum computing has shown great potential to revolutionize traditional computing and can provide an exponential speedup for a wide range of possible applications, attracting various stakeholders. However, understanding fundamental quantum computing concepts remains a significant challenge for novices because of their abstract and counterintuitive nature. Thus, we propose an analogy-based characterization framework to construct the mental mapping between quantum computing concepts and daily objects, informed by in-depth expert interviews and a literature review, covering key quantum concepts and characteristics like number of qubits, output state duality, quantum concept type, and probability quantification. Then, we developed an AR-based prototype system, Intuit, using situated analytics to explain quantum concepts through daily objects and phenomena (e.g., rotating coins, paper cutters). We thoroughly evaluated our approach through in-depth user and expert interviews. The Results demonstrate the effectiveness and usability of Intuit in helping learners understand abstract concepts in an intuitive and engaging manner.

cs.HC↗

arXiv2025

"You'll Be Alice Adventuring in Wonderland!" Processes, Challenges, and Opportunities of Creating Animated Virtual Reality Stories

Animated virtual reality (VR) stories, combining the presence of VR and the artistry of computer animation, offer a compelling way to deliver messages and evoke emotions. Motivated by the growing demand for immersive narrative experiences, more creators are creating animated VR stories. However, a holistic understanding of their creation processes and challenges involved in crafting these stories is still limited. Based on semi-structured interviews with 21 animated VR story creators, we identify ten common stages in their end-to-end creation processes, ranging from idea generation to evaluation, which form diverse workflows that are story-driven or visual-driven. Additionally, we highlight nine unique issues that arise during the creation process, such as a lack of reference material for multi-element plots, the absence of specific functionalities for story integration, and inadequate support for audience evaluation. We compare the creation of animated VR stories to general XR applications and distill several future research opportunities.

cs.HC↗

arXiv2024

VirtuWander: Enhancing Multi-modal Interaction for Virtual Tour Guidance through Large Language Models

Tour guidance in virtual museums encourages multi-modal interactions to boost user experiences, concerning engagement, immersion, and spatial awareness. Nevertheless, achieving the goal is challenging due to the complexity of comprehending diverse user needs and accommodating personalized user preferences. Informed by a formative study that characterizes guidance-seeking contexts, we establish a multi-modal interaction design framework for virtual tour guidance. We then design VirtuWander, a two-stage innovative system using domain-oriented large language models to transform user inquiries into diverse guidance-seeking contexts and facilitate multi-modal interactions. The feasibility and versatility of VirtuWander are demonstrated with virtual guiding examples that encompass various touring scenarios and cater to personalized preferences. We further evaluate VirtuWander through a user study within an immersive simulated museum. The results suggest that our system enhances engaging virtual tour experiences through personalized communication and knowledgeable assistance, indicating its potential for expanding into real-world scenarios.

cs.HC↗

arXiv2021

Deep Colormap Extraction from Visualizations

This work presents a new approach based on deep learning to automatically extract colormaps from visualizations. After summarizing colors in an input visualization image as a Lab color histogram, we pass the histogram to a pre-trained deep neural network, which learns to predict the colormap that produces the visualization. To train the network, we create a new dataset of 64K visualizations that cover a wide variety of data distributions, chart types, and colormaps. The network adopts an atrous spatial pyramid pooling module to capture color features at multiple scales in the input color histograms. We then classify the predicted colormap as discrete or continuous and refine the predicted colormap based on its color histogram. Quantitative comparisons to existing methods show the superior performance of our approach on both synthetic and real-world visualizations. We further demonstrate the utility of our method with two use cases,i.e., color transfer and color remapping.

cs.HC↗

arXiv2021

InfoColorizer: Interactive Recommendation of Color Palettes for Infographics

When designing infographics, general users usually struggle with getting desired color palettes using existing infographic authoring tools, which sometimes sacrifice customizability, require design expertise, or neglect the influence of elements' spatial arrangement. We propose a data-driven method that provides flexibility by considering users' preferences, lowers the expertise barrier via automation, and tailors suggested palettes to the spatial layout of elements. We build a recommendation engine by utilizing deep learning techniques to characterize good color design practices from data, and further develop InfoColorizer, a tool that allows users to obtain color palettes for their infographics in an interactive and dynamic manner. To validate our method, we conducted a comprehensive four-part evaluation, including case studies, a controlled user study, a survey study, and an interview study. The results indicate that InfoColorizer can provide compelling palette recommendations with adequate flexibility, allowing users to effectively obtain high-quality color design for input infographics with low effort.

cs.HC↗