The combination of deep learning, artist-curated scans, and Implicit Functions (IF), is
enabling the creation of detailed, clothed, 3D humans from images. However, existing …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Current methods for learning realistic and animatable 3D clothed avatars need either posed
3D scans or 2D images with carefully controlled user poses. In contrast, our goal is to learn …

We introduce a method to render Neural Radiance Fields (NeRFs) in real time using
PlenOctrees, an octree-based 3D representation which supports view-dependent effects …

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 …

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 …

Abstract Existing Human NeRF methods for reconstructing 3D humans typically rely on
multiple 2D images from multi-view cameras or monocular videos captured from fixed …

We introduce TADA, a simple-yet-effective approach that takes textual descriptions and
produces expressive 3D avatars with high-quality geometry and lifelike textures, that can be …

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 …