Recent advances in neural rendering have improved both training and rendering times by
orders of magnitude. While these methods demonstrate state-of-the-art quality and speed …

Abstract 3D human pose estimation and mesh recovery have attracted widespread research
interest in many areas, such as computer vision, autonomous driving, and robotics. Deep …

Human hands possess the dexterity to interact with diverse objects such as gras** specific
parts of the objects and/or approaching them from desired directions. More importantly …

We interact with the world with our hands and see it through our own (egocentric)
perspective. A holistic 3D understanding of such interactions from egocentric views is …

We present a method for controlling a simulated humanoid to grasp an object and move it to
follow an object trajectory. Due to the challenges in controlling a humanoid with dexterous …

We introduce DiffH2O, a new diffusion-based framework for synthesizing realistic, dexterous
hand-object interactions from natural language. Our model employs a temporal two-stage …

Tracking human object interaction from videos is important to understand human behavior
from the rapidly growing stream of video data. Previous video-based methods require …

In this paper, we tackle the problem of detailed hand-object 3D reconstruction from
monocular video with unknown objects, for applications where the required accuracy and …

Reconstructing 3D hand-face interactions with deformations from a single image is a
challenging yet crucial task with broad applications in AR, VR, and gaming. The challenges …

We propose the task of Hand-Object Stable Grasp Reconstruction (HO-SGR), the
reconstruction of frames during which the hand is stably holding the object. We first develop …