Non‐rigid registration computes an alignment between a source surface with a target
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …

The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

It is extremely challenging to create an animatable clothed human avatar from RGB videos,
especially for loose clothes due to the difficulties in motion modeling. To address this …

We present PHORHUM, a novel, end-to-end trainable, deep neural network methodology for
photorealistic 3D human reconstruction given just a monocular RGB image. Our pixel …

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 …

We present neural radiance fields for rendering and temporal (4D) reconstruction of humans
in motion (H-NeRF), as captured by a sparse set of cameras or even from a monocular …

Learning a model of facial shape and expression from 4D scans Page 1 Learning a model
of facial shape and expression from 4D scans TIANYE LI∗†, University of Southern …

We present a simple yet effective method to infer detailed full human body shape from only a
single photograph. Our model can infer full-body shape including face, hair, and clothing …

This paper describes how to obtain accurate 3D body models and texture of arbitrary people
from a single, monocular video in which a person is moving. Based on a parametric body …

Human performance capture is a highly important computer vision problem with many
applications in movie production and virtual/augmented reality. Many previous performance …