@article{deepphasemotionmanifolds2022,
  title = {DeepPhase: Periodic Autoencoders for Learning Motion Phase Manifolds},
  author = {Starke, S. and Mason, I. and Komura, T.},
  year = {2022},
  journal = {ACM Transactions on Graphics (SIGGRAPH 2022)},
  url = {https://dl.acm.org/doi/10.1145/3528223.3530178},
  abstract = {Starke, Mason, and Komura (2022) introduce a periodic autoencoder that discovers the phase structure of motion data automatically, learning a continuous manifold that supports smooth transitions between locomotion states without requiring manually annotated phase signals. The method received the SIGGRAPH 2022 Best Paper award and is animation-native, having been designed and evaluated entirely within the character animation domain. It is the current reference for neural phase-based character control and has demonstrably shaped the direction of interactive character locomotion research.},
  keywords = {character-animation, motion-synthesis, generative-ai},
  note = {AI \& Animation Education Knowledge Base}
}