ICON-3D Avatar Creator from 2D Pixels
Realistic virtual humans will play a central role in mixed and augmented reality, forming a critical foundation for the Metaverse and supporting remote presence, collaboration, education, and entertainment. To enable this, new tools are needed to easily create large-scale 3D virtual humans that can be readily animated. However, current methods need either posed 3D scans captured by expensive scanning equipment or 2D images with carefully controlled user poses. Both of them can't scale up easily. ICON ("Implicit Clothed humans Obtained from Normals") takes a step towards robust 3D clothed…
In plain words
ICON-3D Avatar Creator reconstructs realistic 3D clothed human avatars from standard 2D images and video, without requiring expensive scanning equipment or carefully posed photographs. The tool creates animatable avatars with natural, pose-dependent cloth deformation. It is designed for developers and creators building applications for augmented reality, virtual reality, remote collaboration, education, and entertainment who need to generate 3D virtual humans at scale.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Realistic virtual humans will play a central role in mixed and augmented reality, forming a critical foundation for the Metaverse and supporting remote presence, collaboration, education, and entertainment. To enable this, new tools are needed to easily create large-scale 3D virtual humans that can be readily animated. However, current methods need either posed 3D scans captured by expensive scanning equipment or 2D images with carefully controlled user poses. Both of them can't scale up easily. ICON ("Implicit Clothed humans Obtained from Normals") takes a step towards robust 3D clothed human reconstruction from in-the-wild images. This also enables creating animatable avatars directly from video with personalized and natural pose-dependent cloth deformation. Homepage: https://icon.is.tue.mpg.de/ Github: https://github.com/YuliangXiu/ICON Google Colab: https://colab.research.google.com/drive/1-AWeWhPvCTBX0KfMtgt... Video: https://www.youtube.com/watch?v=ZufrPvooR2Q Paper: https://arxiv.org/abs/2112.09127
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