DeepGamingAI's video: AI Converts Real-World Tennis Match Into A Playable Video Game Game Futurology 28

This is episode of the video series "Game Futurology" covering the paper "Vid2Player: Controllable Video Sprites that Behave and Appear like Professional Tennis Players" by Haotian Zhang, Cristobal Sciutto, Maneesh Agrawala and Kayvon Fatahalian. PDF: https://arxiv.org/pdf/2008.04524.pdf Authors' Video: https://www.youtube.com/watch?v=GnZUIuOzgQc Game Futurology: This is a video series consisting of short 2-3 minute overview of research papers in the field of AI and Game Development. This series aims to ponder over what the future games might look like based on the latest academic research going on in the field today. Subscribe for more weekly videos! 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. Music Credits: https://www.fesliyanstudios.com/ ---------------------------------------------------------------- • YouTube - https://www.youtube.com/c/DeepGamingA... • Twitter - https://twitter.com/deepgamingai • Medium - https://medium.com/@chintan.t93 • GitHub - https://github.com/ChintanTrivedi --------------------------------------------------------------------