Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from
coordinates (eg, xyz) to signals (eg, signed distances), have shown remarkable promise as …

Abstract Dynamic Neural Radiance Field (NeRF) is a powerful algorithm capable of
rendering photo-realistic novel view images from a monocular RGB video of a dynamic …

Creating photorealistic avatars of existing people currently requires extensive person-
specific data capture, which is usually only accessible to the VFX industry and not the …

Recently, neural networks have been used as implicit representations for surface
reconstruction, modelling, learning, and generation. So far, training neural networks to be …

Computer graphics, 3D computer vision and robotics communities have produced multiple
approaches to representing 3D geometry for rendering and reconstruction. These provide …

Given a monocular video, segmenting and decoupling dynamic objects while recovering the
static environment is a widely studied problem in machine intelligence. Existing solutions …

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a
novel algorithm that is able to predict elaborate riggable 3D face models from a single image …

We present FaceVerse, a fine-grained 3D Neural Face Model, which is built from hybrid East
Asian face datasets containing 60K fused RGB-D images and 2K high-fidelity 3D head scan …

We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of
a category and generating dense correspondences among shapes. With DIF, a 3D shape is …

Modern autonomous driving systems rely heavily on deep learning models to process point
cloud sensory data; meanwhile, deep models have been shown to be susceptible to …