Estimating human pose and shape from monocular images is a long-standing problem in
computer vision. Since the release of statistical body models, 3D human mesh recovery has …

Gras** is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …

We introduce SceneDiffuser, a conditional generative model for 3D scene understanding.
SceneDiffuser provides a unified model for solving scene-conditioned generation …

We show, for the first time, that neural networks trained only on synthetic data achieve state-
of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real …

In this paper, we present Motion-X, a large-scale 3D expressive whole-body motion dataset.
Existing motion datasets predominantly contain body-only poses, lacking facial expressions …

This paper addresses a novel task of anticipating 3D human-object interactions (HOIs). Most
existing research on HOI synthesis lacks comprehensive whole-body interactions with …

Whole-body pose estimation localizes the human body, hand, face, and foot keypoints in an
image. This task is challenging due to multi-scale body parts, fine-grained localization for …

Modelling interactions between humans and objects in natural environments is central to
many applications including gaming, virtual and mixed reality, as well as human behavior …

Inferring human-scene contact (HSC) is the first step toward understanding how humans
interact with their surroundings. While detecting 2D human-object interaction (HOI) and …

We address the problem of generating realistic 3D motions of humans interacting with
objects in a scene. Our key idea is to create a neural interaction field attached to a specific …