Trajectory Space Factorization for Deep Video-Based 3D Human Pose Estimation

Understand

Existing deep learning approaches on 3d human pose estimation for videos are either based on Recurrent or Convolutional Neural Networks (RNNs or CNNs).

• However, RNN-based frameworks can only tackle sequences with limited frames because sequential models are sensitive to bad frames and tend to drift over long sequences.
• Although existing CNN-based temporal frameworks attempt to address the sensitivity and drift problems by concurrently processing all input frames in the sequence, the existing state-of-the-art CNN-based framework is limited to 3d pose estimation of a single frame from a sequential input.
• In this paper, we propose a deep learning-based framework that utilizes matrix factorization for sequential 3d human poses estimation.