We present a novel method for generating realistic and view-consistent images with fine geometry from 2D image collections. Our method proposes a hybrid explicit-implicit representation called OrthoPlanes, which encodes fine-grained 3D information in feature maps that can be efficiently generated by modifying 2D StyleGANs. Compared to previous representations, our method has better scalability and expressiveness with clear and explicit information. As a result, our method can handle more challenging view-angles and synthesize articulated objects with high spatial degree of freedom. Experiments demonstrate that our method achieves state-of-the-art results on FFHQ and SHHQ datasets, both quantitatively and qualitatively.
EG3D proposes tri-plane representation for 3D-aware image synthesis.
StyleGAN-Human collects and annotates a large-scale human image dataset for StyleGAN-based human generation.
3DHumanGAN proposes a style-based generator architecture to generate 3D-aware human images from 2D image collections.
EVA3D proposes a compositional framework to generate animatable 3D-aware human images from 2D image collections.
AvatarGen proposes a disentangled framework to generate animatable 3D-aware human images from 2D image collections.
@article{he2023orthoplanes,
title={OrthoPlanes: A Novel Representation for Better 3D-Awareness of GANs},
author={Honglin He and Zhuoqian Yang and Shikai Li and Bo Dai and Wayne Wu},
year={2023},
eprint={2309.15830},
archivePrefix={arXiv},
primaryClass={cs.CV}
}