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 …

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 …

Despite recent research advancements in reconstructing clothed humans from a single
image, accurately restoring the “unseen regions” with high-level details remains an …

To make 3D human avatars widely available, we must be able to generate a variety of 3D
virtual humans with varied identities and shapes in arbitrary poses. This task is challenging …

We propose a method that leverages graph neural networks, multi-level message passing,
and unsupervised training to enable real-time prediction of realistic clothing dynamics …

In this paper, we aim to create generalizable and controllable neural signed distance fields
(SDFs) that represent clothed humans from monocular depth observations. Recent …

We present CaPhy, a novel method for reconstructing animatable human avatars with
realistic dynamic properties for clothing. Specifically, we aim for capturing the geometric and …

Creating high-quality 3D models of clothed humans from single images for real-world
applications is crucial. Despite recent advancements accurately reconstructing humans in …

Recent years have witnessed significant progress in the field of neural surface
reconstruction. While extensive focus was put on volumetric and implicit approaches, a …

High-fidelity human 3D models can now be learned directly from videos, typically by
combining a template-based surface model with neural representations. However, obtaining …