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 …

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

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

We present a system for the creation of realistic one-shot mesh-based (ROME) human head
avatars. From a single photograph, our system estimates the head mesh (with person …

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 …

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 …

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

Creating animatable avatars from static scans requires the modeling of clothing
deformations in different poses. Existing learning-based methods typically add pose …

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 …