DECON: Reconstruction of Clothed-Geometric Multiple Humans from a Single Image via Geometry-Guided Decoupling
Yiming Jiang, Wenfeng Song, Shuai Li, Aimin Hao
Abstract
3D multi-human reconstruction from single images holds significant potential for advancing AR/VR applications. While remarkable progress has been made in single-human reconstruction, existing methods face challenges when reconstructing multiple humans. These challenges include: (1) severe inter-occlusion that disrupts individual body structures, and (2) the absence of physically plausible relative positioning among subjects. We present DECON, a novel DEcouple-and-reCONstruct framework that systematically addresses these limitations through two technical innovations: (1) a decouple-and-reconstruct framework with multi-view synthesis. It separates individuals and reconstructs detailed 3D bodies from a single image. (2) a Perspective-Aware Position Optimization (PAPO) approach. It ensures realistic positioning by fixing overlaps and gaps between subjects. Extensive experiments demonstrate our method
BibTeX
@inproceedings{aaai2026_deconreconstruct,
title = {DECON: Reconstruction of Clothed-Geometric Multiple Humans from a Single Image via Geometry-Guided Decoupling},
author = {Yiming Jiang and Wenfeng Song and Shuai Li and Aimin Hao},
booktitle = {AAAI 2026},
year = {2026}
}