Extracting behavioral measurements non-invasively from video is stymied by the fact that it is
a hard computational problem. Recent advances in deep learning have tremendously …

Human motion generation aims to generate natural human pose sequences and shows
immense potential for real-world applications. Substantial progress has been made recently …

Most existing monocular 3D pose estimation approaches only focus on a single body part,
neglecting the fact that the essential nuance of human motion is conveyed through a concert …

Humans intuitively understand that inanimate objects do not move by themselves, but that
state changes are typically caused by human manipulation (eg, the opening of a book). This …

Graph convolutional network (GCN) has achieved great success in single hand
reconstruction task, while interacting two-hand reconstruction by GCN remains unexplored …

The metaverse aims to build an immersive virtual reality world to support the daily life, work,
and recreation of people. In this survey, the status quo of the metaverse is investigated, and …

Estimating 3D hand and object pose from a single image is an extremely challenging
problem: hands and objects are often self-occluded during interactions, and the 3D …

Our work aims to reconstruct hand-held objects given a single RGB image. In contrast to
prior works that typically assume known 3D templates and reduce the problem to 3D pose …

We study the problem of understanding hand-object interactions from 2D images in the wild.
This requires reconstructing both the hand and the object in 3D, which is challenging …

In this work, we propose a framework for single-view hand mesh reconstruction, which can
simultaneously achieve high reconstruction accuracy, fast inference speed, and temporal …