Estimating 3D humans from images often produces implausible bodies that lean, float, or
penetrate the floor. Such methods ignore the fact that bodies are typically supported by the …

To facilitate the analysis of human actions, interactions and emotions, we compute a 3D
model of human body pose, hand pose, and facial expression from a single monocular …

Abstract Analysis of hand-hand interactions is a crucial step towards better understanding
human behavior. However, most researches in 3D hand pose estimation have focused on …

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 …

The field of vision-based human hand three-dimensional (3D) shape and pose estimation
has attracted significant attention recently owing to its key role in various applications, such …

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

We present a statistical, articulated 3D human shape modeling pipeline, within a fully
trainable, modular, deep learning framework. Given high-resolution complete 3D body …

Estimating 3D hand pose from single RGB images is a highly ambiguous problem that relies
on an unbiased training dataset. In this paper, we analyze cross-dataset generalization …

Estimating hand-object manipulations is essential for in-terpreting and imitating human
actions. Previous work has made significant progress towards reconstruction of hand poses …

We tackle the task of reconstructing hand-object interactions from short video clips. Given an
input video, our approach casts 3D inference as a per-video optimization and recovers a …