3D avatar creation plays a crucial role in the digital age. However, the whole production
process is prohibitively time-consuming and labor-intensive. To democratize this technology …

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 …

We show, for the first time, that neural networks trained only on synthetic data achieve state-
of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real …

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 propose SelfRecon, a clothed human body reconstruction method that combines implicit
and explicit representations to recover space-time coherent geometries from a monocular …

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 …

We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a
clothed human and turns them into an animatable avatar. These avatars are driven by pose …

In this paper we introduce SMPLicit, a novel generative model to jointly represent body
pose, shape and clothing geometry. In contrast to existing learning-based approaches that …

Abstract We present Neural Generalized Implicit Functions (Neural-GIF), to animate people
in clothing as a function of the body pose. Given a sequence of scans of a subject in various …

This paper presents a framework for efficient 3D clothed avatar reconstruction. By combining
the advantages of the high accuracy of optimization-based methods and the efficiency of …