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 …

Abstract We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and
novel view synthesis approach for general non-rigid dynamic scenes. Our approach takes …

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 propose a novel approach for 3D video synthesis that is able to represent multi-view
video recordings of a dynamic real-world scene in a compact, yet expressive representation …

Modelling interactions between humans and objects in natural environments is central to
many applications including gaming, virtual and mixed reality, as well as human behavior …

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 …

Learning-based 3D reconstruction methods have shown impressive results. However, most
methods require 3D supervision which is often hard to obtain for real-world datasets …

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 …

Modeling 3D humans accurately and robustly from a single image is very challenging, and
the key for such an ill-posed problem is the 3D representation of the human models. To …

We advocate the use of implicit fields for learning generative models of shapes and
introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving …