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 …

In this work, we tackle the problem of learning universal robotic dexterous grasping from a
point cloud observation under a table-top setting. The goal is to grasp and lift up objects in …

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 …

Recent successes in image synthesis are powered by large-scale diffusion models.
However, most methods are currently limited to either text-or image-conditioned generation …

We introduce DexYCB, a new dataset for capturing hand grasping of objects. We first
compare DexYCB with a related one through cross-dataset evaluation. We then present a …

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 …

In this paper we introduce SMPLicit, a novel generative model to jointly represent body
pose, shape and clothing geometry. In contrast to existing learning-based approaches that …

Generating digital humans that move realistically has many applications and is widely
studied, but existing methods focus on the major limbs of the body, ignoring the hands and …

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 …

Robotic manipulation refers to how robots intelligently interact with the objects in their
surroundings, such as grasping and carrying an object from one place to another. Dexterous …