3 citations · 6 across the 8 of their papers we have counts for
12 papers
Perceive, Predict, and Plan: a Unified World Action Model for Egocentric Dexterous Manipulation
Zirui Wu, Chen Guo, Tianjian Jiang +11
We introduce P-WAM, a unified world action model for egocentric dexterous manipulation that learns 3D perception, action-conditioned prediction, and goal-directed planning th…
Gaussian Wardrobe: Compositional 3D Gaussian Avatars for Free-Form Virtual Try-On
Zhiyi Chen, Hsuan-I Ho, Tianjian Jiang +3
We introduce Gaussian Wardrobe, a novel framework to digitalize compositional 3D neural avatars from multi-view videos. Existing methods for 3D neural avatars typically treat the h…
ReLoo: Reconstructing Humans Dressed in Loose Garments from Monocular Video in the Wild
Chen Guo, Tianjian Jiang, Manuel Kaufmann +4
While previous years have seen great progress in the 3D reconstruction of humans from monocular videos, few of the state-of-the-art methods are able to handle loose garments that e…
MultiPly: Reconstruction of Multiple People from Monocular Video in the Wild
Zeren Jiang, Chen Guo, Manuel Kaufmann +4
We present MultiPly, a novel framework to reconstruct multiple people in 3D from monocular in-the-wild videos. Reconstructing multiple individuals moving and interacting naturally…
4D-DRESS: A 4D Dataset of Real-world Human Clothing with Semantic Annotations
Wenbo Wang, Hsuan-I Ho, Chen Guo +5
The studies of human clothing for digital avatars have predominantly relied on synthetic datasets. While easy to collect, synthetic data often fall short in realism and fail to cap…
EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild
Manuel Kaufmann, Jie Song, Chen Guo +5
We present EMDB, the Electromagnetic Database of Global 3D Human Pose and Shape in the Wild. EMDB is a novel dataset that contains high-quality 3D SMPL pose and shape parameters wi…
Hi4D: 4D Instance Segmentation of Close Human Interaction
Yifei Yin, Chen Guo, Manuel Kaufmann +3
We propose Hi4D, a method and dataset for the automatic analysis of physically close human-human interaction under prolonged contact. Robustly disentangling several in-contact subj…
X-Avatar: Expressive Human Avatars
Kaiyue Shen, Chen Guo, Manuel Kaufmann +4
We present X-Avatar, a novel avatar model that captures the full expressiveness of digital humans to bring about life-like experiences in telepresence, AR/VR and beyond. Our method…
Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition
Chen Guo, Tianjian Jiang, Xu Chen +2
We present Vid2Avatar, a method to learn human avatars from monocular in-the-wild videos. Reconstructing humans that move naturally from monocular in-the-wild videos is difficult.…
PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence
Zijian Dong, Chen Guo, Jie Song +3
We present a novel method to learn Personalized Implicit Neural Avatars (PINA) from a short RGB-D sequence. This allows non-expert users to create a detailed and personalized virtu…
Human Performance Capture from Monocular Video in the Wild
Chen Guo, Xu Chen, Jie Song +1
Capturing the dynamically deforming 3D shape of clothed human is essential for numerous applications, including VR/AR, autonomous driving, and human-computer interaction. Existing…
Shape-aware Multi-Person Pose Estimation from Multi-View Images
Zijian Dong, Jie Song, Xu Chen +2
In this paper we contribute a simple yet effective approach for estimating 3D poses of multiple people from multi-view images. Our proposed coarse-to-fine pipeline first aggregates…