HumanCoser: Layered 3D human generation via semantic-aware diffusion model

Wang, Yi, Ma, Jian, Shao, Ruizhi, Feng, Qiao, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 and Li, Kun 2024. HumanCoser: Layered 3D human generation via semantic-aware diffusion model. Presented at: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Seattle, USA, 21-25 October 2024.

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Abstract

This paper aims to generate physically-layered 3D humans from text prompts. Existing methods either generate 3D clothed humans as a whole or support only tight and simple clothing generation, which limits their applications to virtual try-on and partlevel editing. To achieve physically-layered 3D human generation with reusable and complex clothing, we propose a novel layer-wise dressed human representation based on a physically-decoupled diffusion model. Specifically, to achieve layer-wise clothing generation, we propose a dual-representation decoupling framework for generating clothing decoupled from the human body, in conjunction with an innovative multi-layer fusion volume rendering method. To match the clothing with different body shapes, we propose an SMPL-driven implicit field deformation network that enables the free transfer and reuse of clothing. Extensive experiments demonstrate that our approach not only achieves state-ofthe- art layered 3D human generation with complex clothing but also supports virtual try-on and layered human animation. More results and the code can be found on our project page at https: //cic.tju.edu.cn/faculty/likun/projects/HumanCoser.

Item Type:	Conference or Workshop Item (Paper)
Status:	In Press
Schools:	Computer Science & Informatics
Date of First Compliant Deposit:	7 September 2024
Date of Acceptance:	31 July 2024
Last Modified:	03 Nov 2024 02:30
URI:	https://orca.cardiff.ac.uk/id/eprint/171912

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