38 research outputs found
From motions to emotions: Classification of Affect from Dance Movements using Deep Learning
This work investigates classification of emotions from MoCap full-body data by using Convolutional Neural Networks (CNN). Rather than addressing regular day to day activities, we focus on a more complex type of full-body movement - dance. For this purpose, a new dataset was created which contains short excerpts of the performances of professional dancers who interpreted four emotional states: anger, happiness, sadness, and insecurity. Fourteen minutes of motion capture data are used to explore different CNN architectures and data representations. The results of the four-class classification task are up to 0.79 (F1 score) on test data of other performances by the same dancers. Hence, through deep learning, this paper proposes a novel and effective method of emotion classification which can be exploited in affective interfaces
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks
Recently, skeleton based action recognition gains more popularity due to
cost-effective depth sensors coupled with real-time skeleton estimation
algorithms. Traditional approaches based on handcrafted features are limited to
represent the complexity of motion patterns. Recent methods that use Recurrent
Neural Networks (RNN) to handle raw skeletons only focus on the contextual
dependency in the temporal domain and neglect the spatial configurations of
articulated skeletons. In this paper, we propose a novel two-stream RNN
architecture to model both temporal dynamics and spatial configurations for
skeleton based action recognition. We explore two different structures for the
temporal stream: stacked RNN and hierarchical RNN. Hierarchical RNN is designed
according to human body kinematics. We also propose two effective methods to
model the spatial structure by converting the spatial graph into a sequence of
joints. To improve generalization of our model, we further exploit 3D
transformation based data augmentation techniques including rotation and
scaling transformation to transform the 3D coordinates of skeletons during
training. Experiments on 3D action recognition benchmark datasets show that our
method brings a considerable improvement for a variety of actions, i.e.,
generic actions, interaction activities and gestures.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201