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

• URL: http://arxiv.org/abs/2112.10759v1
• Date: Mon, 20 Dec 2021 18:59:40 GMT
• Title: 3D-aware Image Synthesis via Learning Structural and Textural Representations
• Authors: Yinghao Xu, Sida Peng, Ceyuan Yang, Yujun Shen, Bolei Zhou
• Abstract summary: We propose VolumeGAN, for high-fidelity 3D-aware image synthesis, through explicitly learning a structural representation and a textural representation. Our approach achieves sufficiently higher image quality and better 3D control than the previous methods.
• Score: 39.681030539374994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Making generative models 3D-aware bridges the 2D image space and the 3D physical world yet remains challenging. Recent attempts equip a Generative Adversarial Network (GAN) with a Neural Radiance Field (NeRF), which maps 3D coordinates to pixel values, as a 3D prior. However, the implicit function in NeRF has a very local receptive field, making the generator hard to become aware of the global structure. Meanwhile, NeRF is built on volume rendering which can be too costly to produce high-resolution results, increasing the optimization difficulty. To alleviate these two problems, we propose a novel framework, termed as VolumeGAN, for high-fidelity 3D-aware image synthesis, 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.

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