Human poses that are rare or unseen in a training set are challenging for a network to
predict. Similar to the long-tailed distribution problem in visual recognition, the small number …

End-to-end deep representation learning has achieved remarkable accuracy for monocular
3D human pose estimation, yet these models may fail for unseen poses with limited and …

Recovering human pose from RGB images and videos has drawn increasing attention in
recent years owing to minimum sensor requirements and applicability in diverse fields such …

Heatmap representations have formed the basis of human pose estimation systems for
many years, and their extension to 3D has been a fruitful line of recent research. This …

We present a method to capture temporally coherent dynamic clothing deformation from a
monocular RGB video input. In contrast to the existing literature, our method does not …

Reconstructing 3D human shape and pose from monocular images is challenging despite
the promising results achieved by the most recent learning-based methods. The commonly …

We present an approach for 3D human pose estimation from monocular images. The
approach consists of two steps: it first estimates a 2D pose from an image and then …

Recovering 3D human pose from 2D joints is a highly unconstrained problem. We propose a
novel neural network framework, PoseNet3D, that takes 2D joints as input and outputs 3D …

In this paper we propose to embed SMPL within a deep-based model to accurately estimate
3D pose and shape from a still RGB image. We use CNN-based 3D joint predictions as an …

We use our hands every day: to grasp a cup of coffee, write text on a keyboard, or signal that
we are about to say something important. We use our hands to interact with our environment …