We present a method, Neural Radiance Flow (NeRFlow), to learn a 4D spatial-temporal
representation of a dynamic scene from a set of RGB images. Key to our approach is the use …

Deep learning based 3D reconstruction techniques have recently achieved impressive
results. However, while state-of-the-art methods are able to output complex 3D geometry, it …

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

We present the first marker-less approach for temporally coherent 3D performance capture
of a human with general clothing from monocular video. Our approach reconstructs …

We propose DoubleFusion, a new real-time system that combines volumetric dynamic
reconstruction with data-driven template fitting to simultaneously reconstruct detailed …

Over the last few years there has been an increasing research effort directed towards the
automatic verification of infinite state systems. This paper is concerned with identifying …

To address the ill-posed problem caused by partial observations in monocular human
volumetric capture, we present AvatarCap, a novel framework that introduces animatable …

Background: Recent developments in capturing devices like kinect, Intel real sense camera
etc., has impelled research in 3D reconstruction especially in the dynamic scene and the …

We present a deep learning-based volumetric capture approach for performance capture
using a passive and highly sparse multi-view capture system. We focus on a template-free …

We have recently seen great progress in building photorealistic animatable full-body codec
avatars, but generating high-fidelity animation of clothing is still difficult. To address these …