6th March 2026 @WACV2026

Accepted papers and tentative program here

Winter sports like skiing, bobsleigh, ice skating, and ice hockey and mountain sports such as climbing, mountaineering, hiking and downhill biking enjoy widespread popularity as year-round pastimes. In efforts to enhance human well-being and foster social connections, various sports federations seek to promote these activities by encouraging greater participation. A primary strategy involves the broadcast of professional competitions, which serves to engage audiences and to attract new enthusiasts. Notably, televised events, particularly in alpine and nordic skiing, collectively draw an impressive viewership of approximately 6 million, thereby reaching a substantial audience of potential participants. Similarly, mountain sports, like climbing have seen a consistent growth in the past years, with the inclusion in the Summer Olympic Games in Tokyo 2020 and Oscar-winning documentary ``Free Solo'' contributing to attract new practitioners and viewers to the events.

In order to increase the interest in winter and mountain sports disciplines, the enhancement of the spectator experience is a key element, enriching the broadcasted content with additional content, leveraging the most recent advances in video technologies. While current methods for analyzing athlete performance often rely on sensor-based solutions like IMUs or GNSS, the study, development, and utilization of image and video analytics remains a relatively underexplored area in the winter sports domain. Despite the abundance of visual data available from broadcast videos, user-generated, or collected using ad-hoc cameras, a number of challenges arise, because of the dynamic, like athletes' high speed or unseen body poses, as well as the challenging conditions, such as harsh snowy and icy environments or vertical walls.

These challenges must be carefully addressed, especially considering the real-time processing demands of broadcasting applications and the decision-making processes involved in training scenarios. Tackling these issues presents unique and stimulating challenges, offering opportunities for significant contributions to the field of computer vision.

The workshop invites paper submissions focusing on the analysis and interpretation of images and videos captured during winter sports and related summer activities such as mountain sports (e.g., mountaineering, downhill biking, climbing). Topics of interest for the workshop include machine learning solutions for video understanding, pose estimation of athletes, evaluation of athlete performance, forecasting performance, injury detection/prevention, crowd and spectator monitoring, augmented/virtual reality for fan engagement, and applications of computer vision/AI to various winter and mountain sports disciplines. Additionally, submissions can address challenges such as understanding images/videos in harsh weather conditions, camera pose estimation in broadcast videos, trajectory reconstruction, winter scene reconstruction, snow/ice measurements, real-time processing algorithms, fusion of image/video data with other sensor data, and the creation of datasets and benchmarks.

Invited Speakers:

Dr. Yuki Inaba

Japan High Performance Sport Center

Dr. Gedas Bertasius

University of North Carolina, Chapel Hill

Dr. Yuki Inaba

Expanding the Possibilities of Integrated Performance Support for Snow Sports

— Anchored to Final Performance, with Examples from Snowboard Slopestyle & Big Air and Para-Alpine Skiing —

An athlete’s final competition performance is shaped by many factors that interact with one another. While multidisciplinary expertise enables rich forms of athlete support, it is essential that everyone involved—including support staff and coaches—shares a clear understanding of how each intervention can influence final performance. To that end, we conduct performance structure analysis and emphasize the importance of integrating scientific knowledge into practice based on a shared model of what drives performance in each discipline.

At the same time, substantial parts of the performance structure remain uncertain in real-world settings. We therefore view it as critical to continuously refine our understanding through iterative discussion grounded in experience and theory, as well as through ongoing measurement and analysis. Most performance-related data in winter sports come from competition and training contexts—primarily video and sensor-derived signals. Recent technological advances have made it possible to capture and analyze these data in greater detail and, importantly, in ways that are more feasible in the field. This represents a major step forward for performance support that meaningfully contributes to athletes’ competition outcomes.

In this talk, I will share how we have supported winter sports to date and discuss what future, technology-enabled support could look like. As concrete examples, I will introduce our work in Snowboard Slopestyle & Big Air (SS/BA) where we combine video and sensor information to support performance, and in Para-Alpine skiing, where we have implemented VR-based support to address sport-specific demands.

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