DeepPhase: Periodic Autoencoders for Learning Motion Phases Manifolds
Professor Komura’s research focuses on creating diverse human movements for computer animation, games, and virtual environments. His research team designed a new type of neural network called the Periodic Autoencoder that can identify repeating patterns in large sets of motion data without any additional information. This allows us to create unique movements in various styles, such as dance motions synchronized to music or dribbling movements in soccer. Additionally, the system can help find similar motions in large database, produce natural movements between a small number of key frame poses and estimate human movements even when the body is partially obscured in videos.