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Learning Human Action Sequence Style from Video | Technoculture, Art and Games

Technoculture, Art and Games (TAG) is an interdisciplinary centre for research/ creation in game studies and design, digital culture and interactive art

Research & Creation



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Learning Human Action Sequence Style from Video

Kaustubha Mendhurwar, Sudhir Mudur

The current project we are working on is focused on enhancing the standard motion graph algorithms. We are interested in utilizing transition frames presented in video data to inform the transition generation process in order to achieve more realistic transitions between actions. Video data is readily available, less intrusive and easy to produce compared to motion capture. Our algorithm is to reduce human interference during motion creation and improve the realism of the animation.


Project Type

eplay Cultures / Player Experience

Credits

XiaoLong Chen