VIRD: Immersive Match Video Analysis for High-Performance Badminton Coaching.

Badminton is a fast-paced sport that requires a strategic combination of spatial, temporal, and technical tactics. To gain a competitive edge at high-level competitions, badminton professionals frequently analyze match videos to gain insights and develop game strategies. However, the current process for analyzing matches is time-consuming and relies heavily on manual note-taking, due to the lack of automatic data collection and appropriate visualization tools. As a result, there is a gap in effectively analyzing matches and communicating insights among badminton coaches and players. This work proposes an end-to-end immersive match analysis pipeline designed in close collaboration with badminton professionals, including Olympic and national coaches and players. We present VIRD, a VR Bird (i.e., shuttle) immersive analysis tool, that supports interactive badminton game analysis in an immersive environment based on 3D reconstructed game views of the match video. We propose a top-down analytic workflow that allows users to seamlessly move from a high-level match overview to a detailed game view of individual rallies and shots, using situated 3D visualizations and video. We collect 3D spatial and dynamic shot data and player poses with computer vision models and visualize them in VR. Through immersive visualizations, coaches can interactively analyze situated spatial data (player positions, poses, and shot trajectories) with flexible viewpoints while navigating between shots and rallies effectively with embodied interaction. We evaluated the usefulness of VIRD with Olympic and national-level coaches and players in real matches. Results show that immersive analytics supports effective badminton match analysis with reduced context-switching costs and enhances spatial understanding with a high sense of presence.

VoxAR: Adaptive Visualization of Volume Rendered Objects in Optical See-Through Augmented Reality.

We present VoxAR, a method to facilitate an effective visualization of volume-rendered objects in optical see-through head-mounted displays (OST-HMDs). The potential of augmented reality (AR) to integrate digital information into the physical world provides new opportunities for visualizing and interpreting scientific data. However, a limitation of OST-HMD technology is that rendered pixels of a virtual object can interfere with the colors of the real-world, making it challenging to perceive the augmented virtual information accurately. We address this challenge in a two-step approach. First, VoxAR determines an appropriate placement of the volume-rendered object in the real-world scene by evaluating a set of spatial and environmental objectives, managed as user-selected preferences and pre-defined constraints. We achieve a real-time solution by implementing the objectives using a GPU shader language. Next, VoxAR adjusts the colors of the input transfer function (TF) based on the real-world placement region. Specifically, we introduce a novel optimization method that adjusts the TF colors such that the resulting volume-rendered pixels are discernible against the background and the TF maintains the perceptual mapping between the colors and data intensity values. Finally, we present an assessment of our approach through objective evaluations and subjective user studies.

RL-LABEL: A Deep Reinforcement Learning Approach Intended for AR Label Placement in Dynamic Scenarios.

Labels are widely used in augmented reality (AR) to display digital information. Ensuring the readability of AR labels requires placing them in an occlusion-free manner while keeping visual links legible, especially when multiple labels exist in the scene. Although existing optimization-based methods, such as force-based methods, are effective in managing AR labels in static scenarios, they often struggle in dynamic scenarios with constantly moving objects. This is due to their focus on generating layouts optimal for the current moment, neglecting future moments and leading to sub-optimal or unstable layouts over time. In this work, we present RL-LABEL, a deep reinforcement learning-based method intended for managing the placement of AR labels in scenarios involving moving objects. RL-LABEL considers both the current and predicted future states of objects and labels, such as positions and velocities, as well as the user's viewpoint, to make informed decisions about label placement. It balances the trade-offs between immediate and long-term objectives. We tested RL-LABEL in simulated AR scenarios on two real-world datasets, showing that it effectively learns the decision-making process for long-term optimization, outperforming two baselines (i.e., no view management and a force-based method) by minimizing label occlusions, line intersections, and label movement distance. Additionally, a user study involving 18 participants indicates that, within our simulated environment, RL-LABEL excels over the baselines in aiding users to identify, compare, and summarize data on labels in dynamic scenes.

The Ball is in Our Court: Conducting Visualization Research With Sports Experts.

Most sports visualizations rely on a combination of spatial, highly temporal, and user-centric data, making sports a challenging target for visualization. Emerging technologies, such as augmented and mixed reality (AR/XR), have brought exciting opportunities along with new challenges for sports visualization. We share our experience working with sports domain experts and present lessons learned from conducting visualization research in SportsXR. In our previous work, we have targeted different types of users in sports, including athletes, game analysts, and fans. Each user group has unique design constraints and requirements, such as obtaining real-time visual feedback in training, automating the low-level video analysis workflow, or personalizing embedded visualizations for live game data analysis. In this article, we synthesize our best practices and pitfalls we identified while working on SportsXR. We highlight lessons learned in working with sports domain experts in designing and evaluating sports visualizations and in working with emerging AR/XR technologies. We envision that sports visualization research will benefit the larger visualization community through its unique challenges and opportunities for immersive and situated analytics.

PuzzleFixer: A Visual Reassembly System for Immersive Fragments Restoration.

We present PuzzleFixer, an immersive interactive system for experts to rectify defective reassembled 3D objects. Reassembling the fragments of a broken object to restore its original state is the prerequisite of many analytical tasks such as cultural relics analysis and forensics reasoning. While existing computer-aided methods can automatically reassemble fragments, they often derive incorrect objects due to the complex and ambiguous fragment shapes. Thus, experts usually need to refine the object manually. Prior advances in immersive technologies provide benefits for realistic perception and direct interactions to visualize and interact with 3D fragments. However, few studies have investigated the reassembled object refinement. The specific challenges include: 1) the fragment combination set is too large to determine the correct matches, and 2) the geometry of the fragments is too complex to align them properly. To tackle the first challenge, PuzzleFixer leverages dimensionality reduction and clustering techniques, allowing users to review possible match categories, select the matches with reasonable shapes, and drill down to shapes to correct the corresponding faces. For the second challenge, PuzzleFixer embeds the object with node-link networks to augment the perception of match relations. Specifically, it instantly visualizes matches with graph edges and provides force feedback to facilitate the efficiency of alignment interactions. To demonstrate the effectiveness of PuzzleFixer, we conducted an expert evaluation based on two cases on real-world artifacts and collected feedback through post-study interviews. The results suggest that our system is suitable and efficient for experts to refine incorrect reassembled objects.

Sporthesia: Augmenting Sports Videos Using Natural Language.

Augmented sports videos, which combine visualizations and video effects to present data in actual scenes, can communicate insights engagingly and thus have been increasingly popular for sports enthusiasts around the world. Yet, creating augmented sports videos remains a challenging task, requiring considerable time and video editing skills. On the other hand, sports insights are often communicated using natural language, such as in commentaries, oral presentations, and articles, but usually lack visual cues. Thus, this work aims to facilitate the creation of augmented sports videos by enabling analysts to directly create visualizations embedded in videos using insights expressed in natural language. To achieve this goal, we propose a three-step approach - 1) detecting visualizable entities in the text, 2) mapping these entities into visualizations, and 3) scheduling these visualizations to play with the video - and analyzed 155 sports video clips and the accompanying commentaries for accomplishing these steps. Informed by our analysis, we have designed and implemented Sporthesia, a proof-of-concept system that takes racket-based sports videos and textual commentaries as the input and outputs augmented videos. We demonstrate Sporthesia's applicability in two exemplar scenarios, i.e., authoring augmented sports videos using text and augmenting historical sports videos based on auditory comments. A technical evaluation shows that Sporthesia achieves high accuracy (F1-score of 0.9) in detecting visualizable entities in the text. An expert evaluation with eight sports analysts suggests high utility, effectiveness, and satisfaction with our language-driven authoring method and provides insights for future improvement and opportunities.

Exploring Interactions with Printed Data Visualizations in Augmented Reality.

This paper presents a design space of interaction techniques to engage with visualizations that are printed on paper and augmented through Augmented Reality. Paper sheets are widely used to deploy visualizations and provide a rich set of tangible affordances for interactions, such as touch, folding, tilting, or stacking. At the same time, augmented reality can dynamically update visualization content to provide commands such as pan, zoom, filter, or detail on demand. This paper is the first to provide a structured approach to mapping possible actions with the paper to interaction commands. This design space and the findings of a controlled user study have implications for future designs of augmented reality systems involving paper sheets and visualizations. Through workshops ( N=20) and ideation, we identified 81 interactions that we classify in three dimensions: 1) commands that can be supported by an interaction, 2) the specific parameters provided by an (inter)action with paper, and 3) the number of paper sheets involved in an interaction. We tested user preference and viability of 11 of these interactions with a prototype implementation in a controlled study ( N=12, HoloLens 2) and found that most of the interactions are intuitive and engaging to use. We summarized interactions (e.g., tilt to pan) that have strong affordance to complement "point" for data exploration, physical limitations and properties of paper as a medium, cases requiring redundancy and shortcuts, and other implications for design.

The Quest for Omnioculars: Embedded Visualization for Augmenting Basketball Game Viewing Experiences.

Sports game data is becoming increasingly complex, often consisting of multivariate data such as player performance stats, historical team records, and athletes' positional tracking information. While numerous visual analytics systems have been developed for sports analysts to derive insights, few tools target fans to improve their understanding and engagement of sports data during live games. By presenting extra data in the actual game views, embedded visualization has the potential to enhance fans' game-viewing experience. However, little is known about how to design such kinds of visualizations embedded into live games. In this work, we present a user-centered design study of developing interactive embedded visualizations for basketball fans to improve their live game-watching experiences. We first conducted a formative study to characterize basketball fans' in-game analysis behaviors and tasks. Based on our findings, we propose a design framework to inform the design of embedded visualizations based on specific data-seeking contexts. Following the design framework, we present five novel embedded visualization designs targeting five representative contexts identified by the fans, including shooting, offense, defense, player evaluation, and team comparison. We then developed Omnioculars, an interactive basketball game-viewing prototype that features the proposed embedded visualizations for fans' in-game data analysis. We evaluated Omnioculars in a simulated basketball game with basketball fans. The study results suggest that our design supports personalized in-game data analysis and enhances game understanding and engagement.

PoseCoach: A Customizable Analysis and Visualization System for Video-based Running Coaching.

Videos are an accessible form of media for analyzing sports postures and providing feedback to athletes. Existing sport-specific systems embed bespoke human pose attributes and thus can be hard to scale for new attributes, especially for users without programming experiences. Some systems retain scalability by directly showing the differences between two poses, but they might not clearly visualize the key differences that viewers would like to pursue. Besides, video-based coaching systems often present feedback on the correctness of poses by augmenting videos with visual markers or reference poses. However, previewing and augmenting videos limit the analysis and visualization of human poses due to the fixed viewpoints in videos, which confine the observation of captured human movements and cause ambiguity in the augmented feedback. To address these issues, we study customizable human pose data analysis and visualization in the context of running pose attributes, such as joint angles and step distances. Based on existing literature and a formative study, we have designed and implemented a system, PoseCoach, to provide feedback on running poses for amateurs by comparing the running poses between a novice and an expert. PoseCoach adopts a customizable data analysis model to allow users' controllability in defining pose attributes of their interests through our interface. To avoid the influence of viewpoint differences and provide intuitive feedback, PoseCoach visualizes the pose differences as part-based 3D animations on a human model to imitate the demonstration of a human coach. We conduct a user study to verify our design components and conduct expert interviews to evaluate the usefulness of the system.

TIVEE: Visual Exploration and Explanation of Badminton Tactics in Immersive Visualizations.

Tactic analysis is a major issue in badminton as the effective usage of tactics is the key to win. The tactic in badminton is defined as a sequence of consecutive strokes. Most existing methods use statistical models to find sequential patterns of strokes and apply 2D visualizations such as glyphs and statistical charts to explore and analyze the discovered patterns. However, in badminton, spatial information like the shuttle trajectory, which is inherently 3D, is the core of a tactic. The lack of sufficient spatial awareness in 2D visualizations largely limited the tactic analysis of badminton. In this work, we collaborate with domain experts to study the tactic analysis of badminton in a 3D environment and propose an immersive visual analytics system, TIVEE, to assist users in exploring and explaining badminton tactics from multi-levels. Users can first explore various tactics from the third-person perspective using an unfolded visual presentation of stroke sequences. By selecting a tactic of interest, users can turn to the first-person perspective to perceive the detailed kinematic characteristics and explain its effects on the game result. The effectiveness and usefulness of TIVEE are demonstrated by case studies and an expert interview.

Augmenting Sports Videos with VisCommentator.

Visualizing data in sports videos is gaining traction in sports analytics, given its ability to communicate insights and explicate player strategies engagingly. However, augmenting sports videos with such data visualizations is challenging, especially for sports analysts, as it requires considerable expertise in video editing. To ease the creation process, we present a design space that characterizes augmented sports videos at an element-level (what the constituents are) and clip-level (how those constituents are organized). We do so by systematically reviewing 233 examples of augmented sports videos collected from TV channels, teams, and leagues. The design space guides selection of data insights and visualizations for various purposes. Informed by the design space and close collaboration with domain experts, we design VisCommentator, a fast prototyping tool, to eases the creation of augmented table tennis videos by leveraging machine learning-based data extractors and design space-based visualization recommendations. With VisCommentator, sports analysts can create an augmented video by selecting the data to visualize instead of manually drawing the graphical marks. Our system can be generalized to other racket sports (e.g., tennis, badminton) once the underlying datasets and models are available. A user study with seven domain experts shows high satisfaction with our system, confirms that the participants can reproduce augmented sports videos in a short period, and provides insightful implications into future improvements and opportunities.

A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization.

Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VIS is needed. In this article, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML?" and "how ML techniques can be used to solve visualization problems? "This survey reveals seven main processes where the employment of ML techniques can benefit visualizations: Data Processing4VIS, Data-VIS Mapping, Insight Communication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling. The seven processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations. Meanwhile, the seven processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this article can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.io.

ShuttleSpace: Exploring and Analyzing Movement Trajectory in Immersive Visualization.

We present ShuttleSpace, an immersive analytics system to assist experts in analyzing trajectory data in badminton. Trajectories in sports, such as the movement of players and balls, contain rich information on player behavior and thus have been widely analyzed by coaches and analysts to improve the players' performance. However, existing visual analytics systems often present the trajectories in court diagrams that are abstractions of reality, thereby causing difficulty for the experts to imagine the situation on the court and understand why the player acted in a certain way. With recent developments in immersive technologies, such as virtual reality (VR), experts gradually have the opportunity to see, feel, explore, and understand these 3D trajectories from the player's perspective. Yet, few research has studied how to support immersive analysis of sports data from such a perspective. Specific challenges are rooted in data presentation (e.g., how to seamlessly combine 2D and 3D visualizations) and interaction (e.g., how to naturally interact with data without keyboard and mouse) in VR. To address these challenges, we have worked closely with domain experts who have worked for a top national badminton team to design ShuttleSpace. Our system leverages 1) the peripheral vision to combine the 2D and 3D visualizations and 2) the VR controller to support natural interactions via a stroke metaphor. We demonstrate the effectiveness of ShuttleSpace through three case studies conducted by the experts with useful insights. We further conduct interviews with the experts whose feedback confirms that our first-person immersive analytics system is suitable and useful for analyzing badminton data.

Visual Analysis of Discrimination in Machine Learning.

The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set visualization to facilitate the exploration and interpretation of discriminatory itemsets. A user study shows that users can interpret the visually encoded information in DiscriLens quickly and accurately. Use cases demonstrate that DiscriLens provides informative guidance in understanding and reducing algorithmic discrimination.

MARVisT: Authoring Glyph-Based Visualization in Mobile Augmented Reality.

Recent advances in mobile augmented reality (AR) techniques have shed new light on personal visualization for their advantages of fitting visualization within personal routines, situating visualization in a real-world context, and arousing users' interests. However, enabling non-experts to create data visualization in mobile AR environments is challenging given the lack of tools that allow in-situ design while supporting the binding of data to AR content. Most existing AR authoring tools require working on personal computers or manually creating each virtual object and modifying its visual attributes. We systematically study this issue by identifying the specificity of AR glyph-based visualization authoring tool and distill four design considerations. Following these design considerations, we design and implement MARVisT, a mobile authoring tool that leverages information from reality to assist non-experts in addressing relationships between data and virtual glyphs, real objects and virtual glyphs, and real objects and data. With MARVisT, users without visualization expertise can bind data to real-world objects to create expressive AR glyph-based visualizations rapidly and effortlessly, reshaping the representation of the real world with data. We use several examples to demonstrate the expressiveness of MARVisT. A user study with non-experts is also conducted to evaluate the authoring experience of MARVisT.

Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline.

Designers need to consider not only perceptual effectiveness but also visual styles when creating an infographic. This process can be difficult and time consuming for professional designers, not to mention non-expert users, leading to the demand for automated infographics design. As a first step, we focus on timeline infographics, which have been widely used for centuries. We contribute an end-to-end approach that automatically extracts an extensible timeline template from a bitmap image. Our approach adopts a deconstruction and reconstruction paradigm. At the deconstruction stage, we propose a multi-task deep neural network that simultaneously parses two kinds of information from a bitmap timeline: 1) the global information, i.e., the representation, scale, layout, and orientation of the timeline, and 2) the local information, i.e., the location, category, and pixels of each visual element on the timeline. At the reconstruction stage, we propose a pipeline with three techniques, i.e., Non-Maximum Merging, Redundancy Recover, and DL GrabCut, to extract an extensible template from the infographic, by utilizing the deconstruction results. To evaluate the effectiveness of our approach, we synthesize a timeline dataset (4296 images) and collect a real-world timeline dataset (393 images) from the Internet. We first report quantitative evaluation results of our approach over the two datasets. Then, we present examples of automatically extracted templates and timelines automatically generated based on these templates to qualitatively demonstrate the performance. The results confirm that our approach can effectively extract extensible templates from real-world timeline infographics.

LassoNet: Deep Lasso-Selection of 3D Point Clouds.

Selection is a fundamental task in exploratory analysis and visualization of 3D point clouds. Prior researches on selection methods were developed mainly based on heuristics such as local point density, thus limiting their applicability in general data. Specific challenges root in the great variabilities implied by point clouds (e.g., dense vs. sparse), viewpoint (e.g., occluded vs. non-occluded), and lasso (e.g., small vs. large). In this work, we introduce LassoNet, a new deep neural network for lasso selection of 3D point clouds, attempting to learn a latent mapping from viewpoint and lasso to point cloud regions. To achieve this, we couple user-target points with viewpoint and lasso information through 3D coordinate transform and naive selection, and improve the method scalability via an intention filtering and farthest point sampling. A hierarchical network is trained using a dataset with over 30K lasso-selection records on two different point cloud data. We conduct a formal user study to compare LassoNet with two state-of-the-art lasso-selection methods. The evaluations confirm that our approach improves the selection effectiveness and efficiency across different combinations of 3D point clouds, viewpoints, and lasso selections. Project Website: https://lassonet.github.io.

StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams.

Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.