Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold

TL;DR

This paper introduces an unsupervised framework for modeling human actions as trajectories in a learned pose embedding space, enabling effective action recognition and pose reconstruction.

Contribution

It proposes a novel unsupervised manifold learning method called EnGAN and integrates it with PoseRNN for improved action modeling and recognition.

Findings

Achieves state-of-the-art transferability in action recognition

EnGAN effectively models continuous pose embedding manifold

Framework enables high-quality pose reconstruction and interpolation

Abstract

An unsupervised human action modeling framework can provide useful pose-sequence representation, which can be utilized in a variety of pose analysis applications. In this work we propose a novel temporal pose-sequence modeling framework, which can embed the dynamics of 3D human-skeleton joints to a continuous latent space in an efficient manner. In contrast to end-to-end framework explored by previous works, we disentangle the task of individual pose representation learning from the task of learning actions as a trajectory in pose embedding space. In order to realize a continuous pose embedding manifold with improved reconstructions, we propose an unsupervised, manifold learning procedure named Encoder GAN, (or EnGAN). Further, we use the pose embeddings generated by EnGAN to model human actions using a bidirectional RNN auto-encoder architecture, PoseRNN. We introduce first-order gradient loss to explicitly enforce temporal regularity in the predicted motion sequence. A hierarchical feature fusion technique is also investigated for simultaneous modeling of local skeleton joints along with global pose variations. We demonstrate state-of-the-art transfer-ability of the learned representation against other supervisedly and unsupervisedly learned motion embeddings for the task of fine-grained action recognition on SBU interaction dataset. Further, we show the qualitative strengths of the proposed framework by visualizing skeleton pose reconstructions and interpolations in pose-embedding space, and low dimensional principal component projections of the reconstructed pose trajectories.