The recent amalgamation of transformer and convolutional designs has led to steady
improvements in accuracy and efficiency of the models. In this work, we introduce FastViT, a …

We present an approach that can reconstruct hands in 3D from monocular input. Our
approach for Hand Mesh Recovery HaMeR follows a fully transformer-based architecture …

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

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 …

Reconstructing two hands from monocular RGB images is challenging due to frequent
occlusion and mutual confusion. Existing methods mainly learn an entangled representation …

This paper proposes a do-it-all neural model of human hands, named LISA. The model can
capture accurate hand shape and appearance, generalize to arbitrary hand subjects …

Estimating the articulated 3D hand-object pose from a single RGB image is a highly
ambiguous and challenging problem, requiring large-scale datasets that contain diverse …

Real-time 3D hand mesh reconstruction is challenging, especially when the hand is holding
some object. Beyond the previous methods, we design H2ONet to fully exploit non-occluded …

Reconstructing interacting hands from monocular images is indispensable in AR/VR
applications. Most existing solutions rely on the accurate localization of each skeleton joint …

Hand gesture is one of the most efficient and natural interfaces in current human–computer
interaction (HCI) systems. Despite the great progress achieved in hand gesture-based HCI …