  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.
