TY  - JOUR
TI  - Phase-Functioned Neural Networks for Character Control (PFNN)
AU  - Holden, D.
AU  - Komura, T.
AU  - Saito, J.
PY  - 2017
DA  - 2017/07//
JO  - ACM Transactions on Graphics (SIGGRAPH 2017)
AB  - Holden, Komura and Saito (2017) introduced Phase-Functioned Neural Networks, in which the weights of a small network are computed as a function of a cyclic motion phase variable by a second "gating" network. This allows the controller to smoothly interpolate movement styles across the locomotion cycle and terrain types, producing responsive real-time character locomotion with terrain adaptation. Published at SIGGRAPH 2017, PFNN is the named origin of the phase-based neural locomotion family and anchors the AI4Animation framework widely used in academic and industry teaching. It is the upstream paper to the held DeepPhase entry and distinct from Holden's 2016 motion-synthesis paper.
KW  - motion-synthesis
KW  - character-animation
UR  - https://dl.acm.org/doi/10.1145/3072959.3073663
LA  - en
ER  -