VolumeGAN - 3D-aware Image Synthesis via Learning Structural and Textural Representations

3D-aware Image Synthesis via Learning Structural and Textural Representations
Yinghao Xu, Sida Peng, Ceyuan Yang, Yujun Shen, Bolei Zhou
arXiv preprint arXiv:

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This paper aims at achieving high-fidelity 3D-aware images synthesis. We propose a novel framework, termed as VolumeGAN, for synthesizing images under different camera views, through explicitly learning a structural representation and a textural representation. We first learn a feature volume to represent the underlying structure, which is then converted to a feature field using a NeRF-like model. The feature field is further accumulated into a 2D feature map as the textural representation, followed by a neural renderer for appearance synthesis. Such a design enables independent control of the shape and the appearance. Extensive experiments on a wide range of datasets show that our approach achieves sufficiently higher image quality and better 3D control than the previous methods.

Qualitative Results

Independent control of structure (shape) and texture (appearance).

Comparison to prior work on various datasets.

Code Coming Soon

BibTeX

@article{xu2021volumegan,
  title   = {3D-aware Image Synthesis via Learning Structural and Textural Representations},
  author  = {Xu, Yinghao and Peng, Sida and Yang, Ceyuan and Shen, Yujun and Zhou, Bolei},
  article = {arXiv preprint arXiv:2112.10759},
  year    = {2021}
}