G-NeRF: Geometry-enhanced Novel View Synthesis from Single-View Images

• URL: http://arxiv.org/abs/2404.07474v1
• Date: Thu, 11 Apr 2024 04:58:18 GMT
• Title: G-NeRF: Geometry-enhanced Novel View Synthesis from Single-View Images
• Authors: Zixiong Huang, Qi Chen, Libo Sun, Yifan Yang, Naizhou Wang, Mingkui Tan, Qi Wu,
• Abstract summary: We propose a Geometry-enhanced NeRF (G-NeRF), which seeks to enhance the geometry priors by a geometry-guided multi-view synthesis approach. To tackle the absence of multi-view supervision for single-view images, we design the depth-aware training approach.
• Score: 45.66479596827045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Novel view synthesis aims to generate new view images of a given view image collection. Recent attempts address this problem relying on 3D geometry priors (e.g., shapes, sizes, and positions) learned from multi-view images. However, such methods encounter the following limitations: 1) they require a set of multi-view images as training data for a specific scene (e.g., face, car or chair), which is often unavailable in many real-world scenarios; 2) they fail to extract the geometry priors from single-view images due to the lack of multi-view supervision. In this paper, we propose a Geometry-enhanced NeRF (G-NeRF), which seeks to enhance the geometry priors by a geometry-guided multi-view synthesis approach, followed by a depth-aware training. In the synthesis process, inspired that existing 3D GAN models can unconditionally synthesize high-fidelity multi-view images, we seek to adopt off-the-shelf 3D GAN models, such as EG3D, as a free source to provide geometry priors through synthesizing multi-view data. Simultaneously, to further improve the geometry quality of the synthetic data, we introduce a truncation method to effectively sample latent codes within 3D GAN models. To tackle the absence of multi-view supervision for single-view images, we design the depth-aware training approach, incorporating a depth-aware discriminator to guide geometry priors through depth maps. Experiments demonstrate the effectiveness of our method in terms of both qualitative and quantitative results.

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