  With the development of neural radiance fields and generative models,
numerous methods have been proposed for learning 3D human generation from 2D
images. These methods allow control over the pose of the generated 3D human and
enable rendering from different viewpoints. However, none of these methods
explore semantic disentanglement in human image synthesis, i.e., they can not
disentangle the generation of different semantic parts, such as the body, tops,
and bottoms. Furthermore, existing methods are limited to synthesize images at
$512^2$ resolution due to the high computational cost of neural radiance
fields. To address these limitations, we introduce SemanticHuman-HD, the first
method to achieve semantic disentangled human image synthesis. Notably,
SemanticHuman-HD is also the first method to achieve 3D-aware image synthesis
at $1024^2$ resolution, benefiting from our proposed 3D-aware super-resolution
module. By leveraging the depth maps and semantic masks as guidance for the
3D-aware super-resolution, we significantly reduce the number of sampling
points during volume rendering, thereby reducing the computational cost. Our
comparative experiments demonstrate the superiority of our method. The
effectiveness of each proposed component is also verified through ablation
studies. Moreover, our method opens up exciting possibilities for various
applications, including 3D garment generation, semantic-aware image synthesis,
controllable image synthesis, and out-of-domain image synthesis.
