13 citations · 53 across the 11 of their papers we have counts for
14 papers
MHR: Momentum Human Rig
Aaron Ferguson, Ahmed A. A. Osman, Berta Bescos +44
We present MHR, a parametric human body model that combines the decoupled skeleton/shape paradigm of ATLAS with a flexible, modern rig and pose corrective system inspired by the Mo…
Learning Disentangled Avatars with Hybrid 3D Representations
Yao Feng, Weiyang Liu, Timo Bolkart +3
Tremendous efforts have been made to learn animatable and photorealistic human avatars. Towards this end, both explicit and implicit 3D representations are heavily studied for a ho…
SCULPT: Shape-Conditioned Unpaired Learning of Pose-dependent Clothed and Textured Human Meshes
Soubhik Sanyal, Partha Ghosh, Jinlong Yang +3
We present SCULPT, a novel 3D generative model for clothed and textured 3D meshes of humans. Specifically, we devise a deep neural network that learns to represent the geometry and…
BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion
Michael J. Black, Priyanka Patel, Joachim Tesch +1
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 r…
AG3D: Learning to Generate 3D Avatars from 2D Image Collections
Zijian Dong, Xu Chen, Jinlong Yang +3
While progress in 2D generative models of human appearance has been rapid, many applications require 3D avatars that can be animated and rendered. Unfortunately, most existing meth…
ECON: Explicit Clothed humans Optimized via Normal integration
Yuliang Xiu, Jinlong Yang, Xu Cao +2
The combination of deep learning, artist-curated scans, and Implicit Functions (IF), is enabling the creation of detailed, clothed, 3D humans from images. However, existing methods…
Capturing and Animation of Body and Clothing from Monocular Video
Yao Feng, Jinlong Yang, Marc Pollefeys +2
While recent work has shown progress on extracting clothed 3D human avatars from a single image, video, or a set of 3D scans, several limitations remain. Most methods use a holisti…
Neural Point-based Shape Modeling of Humans in Challenging Clothing
Qianli Ma, Jinlong Yang, Michael J. Black +1
Parametric 3D body models like SMPL only represent minimally-clothed people and are hard to extend to clothing because they have a fixed mesh topology and resolution. To address th…
gDNA: Towards Generative Detailed Neural Avatars
Xu Chen, Tianjian Jiang, Jie Song +4
To make 3D human avatars widely available, we must be able to generate a variety of 3D virtual humans with varied identities and shapes in arbitrary poses. This task is challenging…
ICON: Implicit Clothed humans Obtained from Normals
Yuliang Xiu, Jinlong Yang, Dimitrios Tzionas +1
Current methods for learning realistic and animatable 3D clothed avatars need either posed 3D scans or 2D images with carefully controlled user poses. In contrast, our goal is to l…
The Power of Points for Modeling Humans in Clothing
Qianli Ma, Jinlong Yang, Siyu Tang +1
Currently it requires an artist to create 3D human avatars with realistic clothing that can move naturally. Despite progress on 3D scanning and modeling of human bodies, there is s…
SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements
Qianli Ma, Shunsuke Saito, Jinlong Yang +2
Learning to model and reconstruct humans in clothing is challenging due to articulation, non-rigid deformation, and varying clothing types and topologies. To enable learning, the c…
SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks
Shunsuke Saito, Jinlong Yang, Qianli Ma +1
We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a clothed human and turns them into an animatable avatar. These avatars are driven by pose parame…
Grasping Field: Learning Implicit Representations for Human Grasps
Korrawe Karunratanakul, Jinlong Yang, Yan Zhang +3
Robotic grasping of house-hold objects has made remarkable progress in recent years. Yet, human grasps are still difficult to synthesize realistically. There are several key reason…