We propose a physics-based method for synthesizing dexterous hand-object...
We present ArtiGrasp, a novel method to synthesize bi-manual hand-object...
We present EMDB, the Electromagnetic Database of Global 3D Human Pose an...
While progress in 2D generative models of human appearance has been rapi...
In this paper, we propose a novel hybrid representation and end-to-end
t...
We propose the first framework to learn control policies for vision-base...
We propose a method to estimate 3D human poses from substantially blurre...
We propose Hi4D, a method and dataset for the automatic analysis of
phys...
Real-world robotic manipulation tasks remain an elusive challenge, since...
We present X-Avatar, a novel avatar model that captures the full
express...
We present Vid2Avatar, a method to learn human avatars from monocular
in...
In this paper, we take a significant step towards real-world applicabili...
We present HARP (HAnd Reconstruction and Personalization), a personalize...
The ability to create realistic, animatable and relightable head avatars...
We propose a method that leverages graph neural networks, multi-level me...
We present Depth-aware Image-based NEural Radiance fields (DINER). Given...
Neural fields have revolutionized the area of 3D reconstruction and nove...
Traditional tracking of magnetic markers leads to high computational cos...
Garments with the ability to provide kinesthetic force-feedback on-deman...
The goal of Adaptive UIs is to automatically change an interface so that...
We present a method for inferring diverse 3D models of human-object
inte...
We introduce TempCLR, a new time-coherent contrastive learning approach ...
A unique challenge in creating high-quality animatable and relightable 3...
Efficient exploration is a crucial challenge in deep reinforcement learn...
We use our hands to interact with and to manipulate objects. Articulated...
Kinesthetic garments provide physical feedback on body posture and motio...
Neural face avatars that are trained from multi-view data captured in ca...
We present a novel method to learn Personalized Implicit Neural Avatars
...
Hugs are complex affective interactions that often include gestures like...
To make 3D human avatars widely available, we must be able to generate a...
Human grasping synthesis has numerous applications including AR/VR, vide...
Traditional morphable face models provide fine-grained control over
expr...
We introduce the dynamic grasp synthesis task: given an object with a kn...
Capturing the dynamically deforming 3D shape of clothed human is essenti...
Upsampling videos of human activity is an interesting yet challenging ta...
In this paper we contribute a simple yet effective approach for estimati...
Due to the lack of camera parameter information for in-the-wild images,
...
We present Hand ArticuLated Occupancy (HALO), a novel representation of
...
In natural conversation and interaction, our hands often overlap or are ...
Hand pose estimation is difficult due to different environmental conditi...
Acquiring accurate 3D annotated data for hand pose estimation is a
notor...
Despite significant progress, we show that state of the art 3D human pos...
Deep generative models can synthesize photorealistic images of human fac...
Neural implicit surface representations have emerged as a promising para...
In this paper we address the challenge of exploration in deep reinforcem...
Receiving a hug is one of the best ways to feel socially supported, and ...
The task of estimating the 6D pose of an object from RGB images can be b...
Many computer vision tasks rely on labeled data. Rapid progress in gener...
In this paper we propose a convolutional autoencoder to address the prob...
We propose a novel algorithm for the fitting of 3D human shape to images...