Recent skeleton-based action recognition approaches achieve great improvement by using
recurrent neural network (RNN) models. Currently, these approaches build an end-to-end
network from coordinates of joints to class categories and improve accuracy by extending
RNN to spatial domains. First, while such well-designed models and optimization strategies
explore relations between different parts directly from joint coordinates, we provide a simple
universal spatial modeling method perpendicular to the RNN model enhancement …

RNN-based approaches have achieved outstanding performance on action recognition with
skeleton inputs. Currently these methods limit their inputs to coordinates of joints and
improve the accuracy mainly by extending RNN models to spatial domains in various ways.
While such models explore relations between different parts directly from joint coordinates,
we provide a simple universal spatial modeling method perpendicular to the RNN model
enhancement. Specifically, we select a set of simple geometric features, motivated by the …