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

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

Training computers to understand, model, and synthesize human grasping requires a rich
dataset containing complex 3D object shapes, detailed contact information, hand pose and …

We present a new real-time hand tracking system based on a single depth camera. The
system can accurately reconstruct complex hand poses across a variety of subjects. It also …

We extends the previous 2D cascaded object pose regression work [9] in two aspects so that
it works better for 3D articulated objects. Our first contribution is 3D pose-indexed features …

The rise of deep learning has brought remarkable progress in estimating hand geometry
from images where the hands are part of the scene. This paper focuses on a new problem …

Fully articulated hand tracking promises to enable fundamentally new interactions with
virtual and augmented worlds, but the limited accuracy and efficiency of current systems has …

Signed distance functions (SDFs) is an attractive framework that has recently shown
promising results for 3D shape reconstruction from images. SDFs seamlessly generalize to …

Recent progress in deep learning is essentially based on a “big data for small tasks”
paradigm, under which massive amounts of data are used to train a classifier for a single …

Real-time simultaneous tracking of hands manipulating and interacting with external objects
has many potential applications in augmented reality, tangible computing, and wearable …