Graph convolutional network based methods that model the body-joints' relations, have
recently shown great promise in 3D skeleton-based human motion prediction. However …

We propose a multiscale spatio-temporal graph neural network (MST-GNN) to predict the
future 3D skeleton-based human poses in an action-category-agnostic manner. The core of …

Light Field (LF) imaging is a promising solution for providing more immersive and closer to
reality multimedia experiences to end-users with unprecedented creative freedom and …

To achieve 3D skeleton-based human motion prediction, many graph-convolution-based
methods are proposed for promising results; however, due to only preserving low-pass …

Light fields capture 3D scene information by recording light rays emitted from a scene at
various orientations. They offer a more immersive perception, compared with classic 2D …

Light field imaging can simultaneously record the position and direction information of light
rays; thus, digital refocusing and full depth-of-field extension—functions that are …

In this paper, we propose a novel light field compression method based on a low rank-
constrained neural scene representation. While most existing methods directly compress the …

Light field imaging, originated from the availability of light field capture technology, offers a
wide range of applications in the field of computational vision. The capability of predicting …

This paper presents a novel technique for the dense reconstruction of light fields (LFs) from
sparse input views. Our approach leverages the Epipolar Focus Spectrum (EFS) …

Plenoptic 2.0 videos that are captured by focused plenoptic cameras outperform the
traditional plenoptic videos with higher spatial resolution of rendered subaperture images …