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

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

20 Dec 2021CVPR 2022 1arXiv:2112.10759archive 2025-07-28

Yinghao Xu, Sida Peng, Ceyuan Yang, Yujun Shen, Bolei Zhou

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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create_samples genforce/volumegan/render.py official repository ran · our draft was wrong licence not identified · pointer only · d979382ab407b5b0 · report
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3D-Aware Image SynthesisImage GenerationNeRF

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