Vid2Player: Controllable Video Sprites that Behave and Appear like Professional Tennis Players (2008.04524v2)

Abstract: We present a system that converts annotated broadcast video of tennis matches into interactively controllable video sprites that behave and appear like professional tennis players. Our approach is based on controllable video textures, and utilizes domain knowledge of the cyclic structure of tennis rallies to place clip transitions and accept control inputs at key decision-making moments of point play. Most importantly, we use points from the video collection to model a player's court positioning and shot selection decisions during points. We use these behavioral models to select video clips that reflect actions the real-life player is likely to take in a given match play situation, yielding sprites that behave realistically at the macro level of full points, not just individual tennis motions. Our system can generate novel points between professional tennis players that resemble Wimbledon broadcasts, enabling new experiences such as the creation of matchups between players that have not competed in real life, or interactive control of players in the Wimbledon final. According to expert tennis players, the rallies generated using our approach are significantly more realistic in terms of player behavior than video sprite methods that only consider the quality of motion transitions during video synthesis.

• The paper introduces Vid2Player, which converts broadcast tennis footage into interactive video sprites using a shot-cycle state machine and player-specific models.
• The paper demonstrates robust behavior modeling that captures precise court positioning and shot selection to recreate authentic tennis rallies.
• The paper validates its approach with expert assessments, showing that the realistic video sprites enable innovative sports simulation and interactive match experiences.

Overview of Vid2Player: Controllable Video Sprites for Tennis Match Simulation

The paper presents an innovative system called Vid2Player, which transforms annotated broadcast videos of tennis matches into controllable video sprites that visually and behaviorally resemble professional tennis players. This system leverages controllable video textures to achieve interactive control over video sprites. The incorporation of domain-specific knowledge regarding the cyclic nature of tennis rallies further refines this technique by utilizing key decision points in gameplay, allowing transitions between video clips and reactive input during these critical moments.

Vid2Player models a player's court positioning and shot selection through behavioral models derived from real video footage of tennis matches. This modeling approach excels in mimicking authentic tennis strategies at a macro level across entire rallies, not just isolated player movements. Consequently, the system successfully generates novel, plausible rallies that resemble real-life Wimbledon broadcasts. These video sprites facilitate unprecedented experiences, such as constructing hypothetical matchups between players who have never played against each other or enabling users to interactively control players in significant matches like Wimbledon finals. Critically, expert tennis players have assessed the rallies generated by Vid2Player as significantly more authentic in terms of player behavior compared to other video sprite methods focused simply on motion transitions.

Key Contributions and Numerical Results

1. Shot-cycle State Machine: Vid2Player utilizes domain-specific insights into tennis rally structure by controlling video synthesis at the granularity of shot cycles. This high-level control incorporates entire sequences from preparation to shot recovery, enhancing the realism of motion transitions and aligning control inputs with key decision-making periods.
2. Player-specific Behavioral Models: The system constructs models predicting player court positioning and shot choice. These models contribute to the selection of video clips that visually embody both the player's style and strategic behaviors experienced during a match. The discussions outlined in the paper suggest that such modeling avoids the unrealistic outcome of focusing solely on visual transitions.
3. Advanced Video Sprite Rendering: Vid2Player prepares a vast database of broadcast video clips for the generation of video sprites. This preparation involves neural image-to-image transfer to maintain consistent appearance across different lighting conditions and neural network image completion to 'hallucinate' absent visual information due to video cropping.

Implications and Future Directions

Vid2Player projects significant implications for both the practical and theoretical spheres of AI and computer graphics. Practically, the system's ability to create realistic, interactive tennis matches offers new possibilities for sports entertainment, visualization, and training. Future work in this area might involve expanding the database to involve more extensive match coverage, facilitating more sophisticated behavioral models that can more richly replicate professional play strategies. Moreover, applying Vid2Player’s methodologies to other high-end sports contexts with multiple camera setups could even enhance visual fidelity and motion authenticity further.

Theoretically, Vid2Player underscores the importance of domain-specific knowledge in behavior modeling for AI systems aimed at realistic simulation. This reinforces the connection between sports analytics and computer graphics, particularly concerning the modeling of human behavior. As AI continues to advance, integrating sports domain knowledge will likely be pivotal in creating simulations that accurately capture the nuances of player decision-making, potentially leading to interdisciplinary collaborations that improve behavior prediction models.

In conclusion, Vid2Player stands as a formidable advancement in the domain of interactive video sprite technologies, bridging a gap between detailed sports analytics and video rendering to generate realistic and controllable simulations of professional tennis. The potential for future applications in both recreational and professional spaces is vast, positioning Vid2Player as a valuable tool for enhanced sports simulation and modeling.