Skip to yearly menu bar Skip to main content


AttriHuman-3D: Editable 3D Human Avatar Generation with Attribute Decomposition and Indexing

Fan Yang · Tianyi Chen · XIAOSHENG HE · Zhongang Cai · Lei Yang · Si Wu · Guosheng Lin

Arch 4A-E Poster #91
[ ]
Thu 20 Jun 10:30 a.m. PDT — noon PDT


Editable 3D-aware generation, which supports user-interacted editing, has witnessed rapid development recently. However, existing editable 3D GANs either fail to achieve high-accuracy local editing or suffer from huge computational costs. We propose AttriHuman-3D, an editable 3D human generation model, which address the aforementioned problems with attribute decomposition and indexing. The core idea of the proposed model is to generate all attributes (e.g. human body, hair, clothes and so on) in an overall attribute space with six feature planes, which are then decomposed and manipulated with different attribute indexes. To precisely extract features of different attributes from the generated feature planes, we propose a novel attribute indexing method as well as an orthogonal projection regularization to enhance the disentanglement. We also introduce a hyper-latent training strategy and an attribute-specific sampling strategy to avoid style entanglement and misleading punishment from the discriminator. Our method allows users to interactively edit selected attributes in the generated 3D human avatars while keeping others fixed. Both qualitative and quantitative experiments demonstrate that our model provides a strong disentanglement between different attributes, allows fine-grained image editing and generates high-quality 3D human avatars.

Live content is unavailable. Log in and register to view live content