Sporthesia: Augmenting Sports Videos Using Natural Language

Chen, Zhutian; Yang, Qisen; Xie, Xiao; Beyer, Johanna; Xia, Haijun; Wu, Yingcai; Pfister, Hanspeter

doi:10.1109/tvcg.2022.3209497

Cited by 13 publications

(7 citation statements)

References 64 publications

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“…Here, tiny visualizations, like health bars, are often attached to game characters and move with them. The research work most closely related to ours is embedded basketball visualizations for ingame analysis [7], [20]. In contrast to our work, Lin et al [7] focused on studying how viewers would control the visibility of the visualizations and iBall [20] helped casual fans understand the game.…”

Section: A Sports Visual Analyticsmentioning

confidence: 90%

“…The research work most closely related to ours is embedded basketball visualizations for ingame analysis [7], [20]. In contrast to our work, Lin et al [7] focused on studying how viewers would control the visibility of the visualizations and iBall [20] helped casual fans understand the game. Instead, we focus on how to support the design process of the visualizations and their embeddings.…”

Section: A Sports Visual Analyticsmentioning

confidence: 90%

“…Besides work on GUI-based authoring, researchers have also explored other novel methods. Sporthesia [38] is an example authoring system that takes a language-driven approach in the sports context where visualizations are automatically created based on commentary on the sports activity. Work in Augmented Reality (AR) has looked at how to design visualizations for a dynamic context.…”

Section: B Authoring Tools For Adding Visualizations To Videosmentioning

confidence: 99%

See 2 more Smart Citations

Designing for Visualization in Motion: Embedding Visualizations in Swimming Videos

Yao,

Vuillemot,

Bezerianos

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Embedded representations added to a swimming video of the 2021 French Championship using our technology probe. These show dynamically updating visualizations that move with the swimmers: distance to the leader and predicted winner (left), speed distance to a personal record (top right), and current speed and swimmers' ages (bottom right). The left and bottom right images also show stationary embedded representations of the swimmers' names, nationality, and elapsed time.

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Section: A Sports Visual Analyticsmentioning

confidence: 90%

Section: A Sports Visual Analyticsmentioning

confidence: 90%

Section: B Authoring Tools For Adding Visualizations To Videosmentioning

confidence: 99%

See 1 more Smart Citation

Designing for Visualization in Motion: Embedding Visualizations in Swimming Videos

Yao,

Vuillemot,

Bezerianos

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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“…NLIs are widely studied as a promising means of interaction for visual analytics [1], [11]. NLIs for data visualization generate visual representations in response to NL queries, which help reveal data insights [12], [13], [14], [15], [16], [17], [18], [19]. Cox et al [20] aimed to integrate NLI into existing visualization systems.…”

Section: Related Work 21 Nli For Data Visualizationmentioning

confidence: 99%

XNLI: Explaining and Diagnosing NLI-Based Visual Data Analysis

Feng

Wang

Pan

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Natural language interfaces (NLIs) enable users to flexibly specify analytical intentions in data visualization. However, diagnosing the visualization results without understanding the underlying generation process is challenging. Our research explores how to provide explanations for NLIs to help users locate the problems and further revise the queries. We present XNLI, an explainable NLI system for visual data analysis. The system introduces a Provenance Generator to reveal the detailed process of visual transformations, a suite of interactive widgets to support error adjustments, and a Hint Generator to provide query revision hints based on the analysis of user queries and interactions. Two usage scenarios of XNLI and a user study verify the effectiveness and usability of the system. Results suggest that XNLI can significantly enhance task accuracy without interrupting the NLI-based analysis process.

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“…As described by Perin et al [156], various types of data can be collected, including box-score data, tracking data, and meta-data, each offering new narratives for in-depth exploration, such as dissecting tracking data, showcasing events, trajectories, and player perspectives, and further enriching them with specific information and graphical representations. Notable examples demonstrate the breadth of data collected, including court views, temporal event sequences, player shot patterns, and textual play-by-play analysis [172,173].…”

Section: How DV Can Contribute To the Sports Performance Analysis Tra...mentioning

confidence: 99%

Technological Breakthroughs in Sport: Current Practice and Future Potential of Artificial Intelligence, Virtual Reality, Augmented Reality, and Modern Data Visualization in Performance Analysis

Cossich,

Carlgren,

Holash

et al. 2023

Applied Sciences

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We are currently witnessing an unprecedented era of digital transformation in sports, driven by the revolutions in Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Data Visualization (DV). These technologies hold the promise of redefining sports performance analysis, automating data collection, creating immersive training environments, and enhancing decision-making processes. Traditionally, performance analysis in sports relied on manual data collection, subjective observations, and standard statistical models. These methods, while effective, had limitations in terms of time and subjectivity. However, recent advances in technology have ushered in a new era of objective and real-time performance analysis. AI has revolutionized sports analysis by streamlining data collection, processing vast datasets, and automating information synthesis. VR introduces highly realistic training environments, allowing athletes to train and refine their skills in controlled settings. AR overlays digital information onto the real sports environment, providing real-time feedback and facilitating tactical planning. DV techniques convert complex data into visual representations, improving the understanding of performance metrics. In this paper, we explore the potential of these emerging technologies to transform sports performance analysis, offering valuable resources to coaches and athletes. We aim to enhance athletes’ performance, optimize training strategies, and inform decision-making processes. Additionally, we identify challenges and propose solutions for integrating these technologies into current sports analysis practices. This narrative review provides a comprehensive analysis of the historical context and evolution of performance analysis in sports science, highlighting current methods’ merits and limitations. It delves into the transformative potential of AI, VR, AR, and DV, offering insights into how these tools can be integrated into a theoretical model.

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Sporthesia: Augmenting Sports Videos Using Natural Language

Cited by 13 publications

References 64 publications

Designing for Visualization in Motion: Embedding Visualizations in Swimming Videos

Designing for Visualization in Motion: Embedding Visualizations in Swimming Videos

XNLI: Explaining and Diagnosing NLI-Based Visual Data Analysis

Technological Breakthroughs in Sport: Current Practice and Future Potential of Artificial Intelligence, Virtual Reality, Augmented Reality, and Modern Data Visualization in Performance Analysis

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