Efficient 3D Articulated Human Generation with Layered Surface Volumes
Yinghao Xu Wang Yifan Alexander W. Bergman Menglei Chai Bolei Zhou Gordon Wetzstein
Access to high-quality and diverse 3D articulated digital human assets is
crucial in various applications, ranging from virtual reality to social
platforms. Generative approaches, such as 3D generative adversarial networks
(GANs), are rapidly replacing laborious manual content creation tools. However,
existing 3D GAN frameworks typically rely on scene representations that
leverage either template meshes, which are fast but offer limited quality, or
volumes, which offer high capacity but are slow to render, thereby limiting the
3D fidelity in GAN settings. In this work, we introduce layered surface volumes
(LSVs) as a new 3D object representation for articulated digital humans. LSVs
represent a human body using multiple textured mesh layers around a
conventional template. These layers are rendered using alpha compositing with
fast differentiable rasterization, and they can be interpreted as a volumetric
representation that allocates its capacity to a manifold of finite thickness
around the template. Unlike conventional single-layer templates that struggle
with representing fine off-surface details like hair or accessories, our
surface volumes naturally capture such details. LSVs can be articulated, and
they exhibit exceptional efficiency in GAN settings, where a 2D generator
learns to synthesize the RGBA textures for the individual layers. Trained on
unstructured, single-view 2D image datasets, our LSV-GAN generates high-quality
and view-consistent 3D articulated digital humans without the need for
view-inconsistent 2D upsampling networks.