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 …

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 …

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 …

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

Humans commonly work with multiple objects in daily life and can intuitively transfer
manipulation skills to novel objects by understanding object functional regularities. However …

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

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 …

Recent work achieved impressive progress towards joint reconstruction of hands and
manipulated objects from monocular color images. Existing methods focus on two …

Abstract 3D hand-mesh reconstruction from RGB images facilitates many applications,
including augmented reality (AR). However, this requires not only real-time speed and …