Human pose estimation aims to locate the human body parts and build human body
representation (eg, body skeleton) from input data such as images and videos. It has drawn …

Recently, generative adversarial networks (GANs) have progressed enormously, which
makes them able to learn complex data distributions in particular faces. More and more …

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 real …

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 …

We introduce an approach that creates animatable human avatars from monocular videos
using 3D Gaussian Splatting (3DGS). Existing methods based on neural radiance fields …

Top-down methods dominate the field of 3D human pose and shape estimation, because
they are decoupled from human detection and allow researchers to focus on the core …

This work addresses the problem of generating 3D holistic body motions from human
speech. Given a speech recording, we synthesize sequences of 3D body poses, hand …

The estimation of 3D human motion from video has progressed rapidly but current methods
still have several key limitations. First most methods estimate the human in camera …

We propose a method to reconstruct global human trajectories from videos in the wild. Our
optimization method decouples the camera and human motion, which allows us to place …

Abstract We present\emph {DreamHuman}, a method to generate realistic animatable 3D
human avatar models entirely from textual descriptions. Recent text-to-3D methods have …