We present ViSTAR, a Virtual Skill Training system in AR that supports self-guided basketball skill practice, with feedback on balance, posture, and timing. From a formative study with basketball players and coaches, the system addresses three challenges: understanding skills, identifying errors, and correcting mistakes. ViSTAR follows the Behavioral Skills Training (BST) framework-instruction, modeling, rehearsal, and feedback. It provides feedback through visual overlays, rhythm and timing cues, and an AI-powered coaching agent using 3D motion reconstruction. We generate verbal feedback by analyzing spatio-temporal joint data and mapping features to natural-language coaching cues via a Large Language Model (LLM). A key novelty is this feedback generation: motion features become concise coaching insights. In two studies (N=16), participants generally preferred our AI-generated feedback to coach feedback and reported that ViSTAR helped them notice posture and balance issues and refine movements beyond self-observation.

Training resources for parasports are limited, reducing opportunities for athletes and coaches to engage with sport-specific movements and tactical coordination. To address this gap, we developed BRIDGE, a system that integrates a reconstruction pipeline, which detects and tracks players from broadcast video to generate 3D play sequences, with an embodiment-aware visualization framework that decomposes head, trunk, and wheelchair base orientations to represent attention, intent, and mobility. We evaluated BRIDGE in two controlled studies with 20 participants (10 national wheelchair basketball team players and 10 amateur players). The results showed that BRIDGE significantly enhanced the perceived naturalness of player postures and made tactical intentions easier to understand. In addition, it supported functional classification by realistically conveying players’ capabilities, which in turn improved participants’ sense of self-efficacy. This work advances inclusive sports learning and accessible coaching practices, contributing to more equitable access to tactical resources in parasports.

As basketball's popularity surges, fans often find themselves confused and overwhelmed by the rapid game pace and complexity. Basketball tactics, involving a complex series of actions, require substantial knowledge to be fully understood. This complexity leads to a need for additional information and explanation, which can distract fans from the game. To tackle these challenges, we present Sportify, a Visual Question Answering system that integrates narratives and embedded visualization for demystifying basketball tactical questions, aiding fans in understanding various game aspects. We propose three novel action visualizations (i.e., Pass, Cut, and Screen) to demonstrate critical action sequences. To explain the reasoning and logic behind players' actions, we leverage a large-language model (LLM) to generate narratives. We adopt a storytelling approach for complex scenarios from both first and third-person perspectives, integrating action visualizations. We evaluated Sportify with basketball fans to investigate its impact on understanding of tactics, and how different personal perspectives of narratives impact the understanding of complex tactic with action visualizations. Our evaluation with basketball fans demonstrates Sportify's capability to deepen tactical insights and amplify the viewing experience. Furthermore, third-person narration assists people in getting in-depth game explanations while first-person narration enhances fans' game engagement.

Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain. However, expressing unique characteristics of an artwork (e.g. brushwork, colortone, or composition) with text prompts alone may encounter limitations due to the inherent constraints of verbal description. To this end, we introduce DreamStyle, a novel framework designed for artistic image synthesis, proficient in both text-to-image synthesis and style transfer. DreamStyle optimizes a multi-stage textual embedding with a context-aware text prompt, resulting in prominent image quality. In addition, with content and style guidance, DreamStyle exhibits flexibility to accommodate a range of style references. Experimental results demonstrate its superior performance across multiple scenarios, suggesting its promising potential in artistic product creation. Project page: https://nmhkahn.github.io/dreamstyler/

The process of drawing digital comics and animations is a complex process that involves multiple stages. Flat-coloring, the task of filling segmented regions in a line art image with uniform tone and hue, is a particularly time-consuming and labor-intensive task. We have identified that artists suffer from not only adjusting colors in overflowing regions due to line discontinuity but also finding to replace misaligned pixels near the line due to region-bleeding problems (aliasing issues). To address these issues, we propose a holistic data generation pipeline (FlatGAN-DG) that awares the region of line discontinuity and augments the input sketch image to build robust models for noise. In addition, we propose a real-time post-processing method (FlatGAN-PP) that automatically finds and replaces miscolored pixels to alleviate the region-bleeding problems (aliasing issues). To enhance inference speed, we build FlatGAN, which shares the parameters of a generator to predict the foreground, background, and trimap at once to learn in a multi-task manner. Our experimental results show that our method outperforms other rule-and learning-based methods on three different datasets with different painting styles. To evaluate the segmented regions, we collect datasets with the annotation of split-score, merge-hard-score, and merge-easy-score. We also introduce a new evaluation metric (Region Score) on these datasets, validating the efficacy of our methods through a user study. Code is available at https://github.com/hanish3464/FlatGAN.

Annotating tens or hundreds of tiny objects in a given image is laborious yet crucial for a multitude of Computer Vision tasks. Such imagery typically contains objects from various categories, yet the multi-class interactive annotation setting for the detection task has thus far been unex-plored. To address these needs, we propose a novel interactive annotation method for multiple instances of tiny objects from multiple classes, based on a few point-based user in-puts. Our approach, C3Det, relates the full image context with annotator inputs in a local and global manner via late-fusion andfeature-correlation, respectively. We perform ex-periments on the Tiny-DOTA. and LCell datasets using both two-stage and one-stage object detection architectures to verify the efficacy of our approach. Our approach outper-forms existing approaches in interactive annotation, achieving higher mAP with fewer clicks. Furthermore, we validate the annotation efficiency of our approach in a user study where it is shown to be 2.85x faster and yield only 0.36x task load (NASA-TLX, lower is better) compared to manual annotation. The code is available at https://github.com/ChungYi347/Interactive-Multi-Class-Tiny-Object-Detection.

Users may face challenges while designing graphical user interfaces, due to a lack of relevant experience and guidance. This paper aims to investigate the issues users face during the design process, and how to resolve them. To this end, we conducted semi-structured interviews, based on which we built a GUI prototyping assistance tool called GUIComp. This tool can be connected to GUI design software as an extension, and it provides real-time, multi-faceted feedback on a user's current design. Additionally, we conducted two user studies, in which we asked participants to create mobile GUIs with or without GUIComp, and requested online workers to assess the created GUIs. The experimental results show that GUIComp facilitated iterative designs and the participants with GUIComp had better a user experience and produced more acceptable designs than those who did not use it.

Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences. This paper proposes a novel spatio-temporal graph attention (ST-GRAT) that effectively captures the spatio-temporal dynamics in road networks. The novel aspects of our approach mainly include spatial attention, temporal attention, and spatial sentinel vectors. The spatial attention takes the graph structure information (e.g., distance between roads) and dynamically adjusts spatial correlation based on road states. The temporal attention is responsible for capturing traffic speed changes, and the sentinel vectors allow the model to retrieve new features from spatially correlated nodes or preserve existing features. The experimental results show that ST-GRAT outperforms existing models, especially in difficult conditions where traffic speeds rapidly change (e.g., rush hours). We additionally provide a qualitative study to analyze when and where ST-GRAT tended to make accurate predictions during rush-hour times.

We present an interactive visual analytics system that enables traffic congestion exploration, surveillance, and forecasting based on vehicle detector data. Through domain expert collaboration, we have extracted task requirements, incorporated the Long Short-Term Memory (LSTM) model for congestion forecasting, and designed a weighting method for detecting the causes of congestion and congestion propagation directions. Our visual analytics system is designed to enable users to explore congestion causes, directions, and severity. Congestion conditions of a city are visualized using a Volume-Speed Rivers (VSRivers) visualization that simultaneously presents traffic volumes and speeds. To evaluate our system, we report performance comparison results, wherein our model is more accurate than other forecasting algorithms. We demonstrate the usefulness of our system in the traffic management and congestion broadcasting domains through three case studies and domain expert feedback.

In spite of the evolution of Augmented Reality~(AR) technology, it is not wide spread in everyday life. There may be many reasons, but one of the reasons is that it has been developed for very specific users, such as researchers and professionals. To overcome this problem, Grubert et al. proposed the pervasive AR. It is not limited to a specific situation, but is usable in various instances and providing continuous and flexible AR experience. The AR browser is the example of utilizing pervasive AR. The AR browser understands the context of the user and provides corresponding information. However, if the corresponding information to the context of user cannot reach the user in time due to massive data transmission, unexpected network congestion, poor service quality or signal strength, it cannot be guaranteed to be continuous. This leads to a degradation of the user experience, and it cannot support pervasive AR. This paper presents the Progressive Augmented Reality, the way which quickly send incomplete, yet informative, response about the user's current context rather than wait for sending complete information to the user. The concept of Progressive Augmented Reality comes from Progressive Data Science. Our system is aware of network quality by collecting various network health parameters. According to the network status quality, it divides the chunk information to the optimal number and transmits the one that have the highest priority among the divided information. By conducting the above process iteratively, all the divided information is updated. Our client-side system utilizes Android ARCore and has been tested on Google Pixel 2XL.

Addiction is a brain disease manifested by the loss of control over drugs or behaviors, despite negative consequences. Although addiction research has been conducted for decades in psychiatry and neuroscience, a comprehensive understanding of the mechanisms underlying addiction has not yet been achieved. Recent studies in neuroscience [1] have sought to bring light upon this issue by measuring exploration/exploitation decisions in sequential choice tasks, requiring balancing the need to exploit known options and to explore new ones. These studies show a relationship between addiction and exploration/exploitation decisions. For example, people addicted to substances (e.g. alcohol or methamphetamine) or behaviors (e.g. gambling) have tendencies to explore less, which implies they have difficulties 'seeing the big picture".