论文标题
革兰氏-HD：高分辨率的3D一致图像生成具有生成辐射歧管
GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds
论文作者
论文摘要
最近的作品表明，经过非结构化单图像收集训练的3D感知剂可以生成新颖实例的多视图像。实现此目标的关键基础是3D辐射场发生器和卷渲染过程。但是，由于神经量渲染的高计算成本，现有方法无法生成高分辨率图像（例如，最高256x256），或者依靠2D CNN来进行图像空间上采样，从而危及跨不同视图的3D一致性。本文提出了一种新颖的3D感知gan，可以产生高分辨率图像（最高1024x1024），同时保持严格的3D一致性，如音量渲染。我们的动机是直接在3D空间中实现超分辨率，以保持3D一致性。我们通过在最近的生成辐射歧管（GRAM）方法中定义的一组2D辐射歧管上应用2D卷积来避免原本高昂的计算成本，并应用专门的损失函数以高分辨率以有效的GAN训练。 FFHQ和AFHQV2数据集的实验表明，我们的方法可以产生高质量的3D一致性结果，从而极大地表现了现有方法。它朝着缩小传统2D图像生成与3D一致的自由视图生成之间的差距迈出了重要一步。
Recent works have shown that 3D-aware GANs trained on unstructured single image collections can generate multiview images of novel instances. The key underpinnings to achieve this are a 3D radiance field generator and a volume rendering process. However, existing methods either cannot generate high-resolution images (e.g., up to 256X256) due to the high computation cost of neural volume rendering, or rely on 2D CNNs for image-space upsampling which jeopardizes the 3D consistency across different views. This paper proposes a novel 3D-aware GAN that can generate high resolution images (up to 1024X1024) while keeping strict 3D consistency as in volume rendering. Our motivation is to achieve super-resolution directly in the 3D space to preserve 3D consistency. We avoid the otherwise prohibitively-expensive computation cost by applying 2D convolutions on a set of 2D radiance manifolds defined in the recent generative radiance manifold (GRAM) approach, and apply dedicated loss functions for effective GAN training at high resolution. Experiments on FFHQ and AFHQv2 datasets show that our method can produce high-quality 3D-consistent results that significantly outperform existing methods. It makes a significant step towards closing the gap between traditional 2D image generation and 3D-consistent free-view generation.
