Deep neural networks (DNNs) have shown remarkable performance improvements on
vision-related tasks such as object detection or image segmentation. Despite their success …

This paper addresses the challenge of reconstructing an animatable human model from a
multi-view video. Some recent works have proposed to decompose a non-rigidly deforming …

This paper addresses the challenge of novel view synthesis for a human performer from a
very sparse set of camera views. Some recent works have shown that learning implicit …

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 …

Human volumetric capture is a long-standing topic in computer vision and computer
graphics. Although high-quality results can be achieved using sophisticated off-line systems …

Recent advances in image-based 3D human shape estimation have been driven by the
significant improvement in representation power afforded by deep neural networks …

We present ARCH++, an image-based method to reconstruct 3D avatars with arbitrary
clothing styles. Our reconstructed avatars are animation-ready and highly realistic, in both …

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 …

Abstract We introduce Pixel-aligned Implicit Function (PIFu), an implicit representation that
locally aligns pixels of 2D images with the global context of their corresponding 3D object …

In this paper, we aim at synthesizing a free-viewpoint video of an arbitrary human
performance using sparse multi-view cameras. Recently, several works have addressed this …