Abstract
In previous sporting scene synthesis and 3D reconstruction pipelines, gathering an extensive database of sprite animations has been problematic and often the cause of unrealistic renders. We present a video processing framework for collecting and rendering sprite animations - applied on the football pitch. The main idea is to maximize the available information in a football scene by transferring the motion from all the players across the pitch to an individual. All the players on a football pitch provide a wide variety of poses and animation key-frames. In our experiments, even if we successfully capture clean video samples of a target player for 10 s, we can render an order of magnitude more animation key-frames - worth of minutes. The framework is also extensible for other sports, but football is particularly relevant for this task, highlighting the team’s impact on the pitch.
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Ionascu, A., Stefaniga, S., Gaianu, M. (2023). Synthetic Football Sprite Animations Learned Across the Pitch. In: Nguyen, N.T., et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_48
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DOI: https://doi.org/10.1007/978-3-031-41774-0_48
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