GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation

• URL: http://arxiv.org/abs/2112.08867v2
• Date: Fri, 17 Dec 2021 07:50:09 GMT
• Title: GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation
• Authors: Yu Deng, Jiaolong Yang, Jianfeng Xiang, Xin Tong
• Abstract summary: 3D-aware image generative modeling aims to generate 3D-consistent images with explicitly controllable camera poses. Recent works have shown promising results by training neural radiance field (NeRF) generators on unstructured 2D images.
• Score: 25.20217335614512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D-aware image generative modeling aims to generate 3D-consistent images with explicitly controllable camera poses. Recent works have shown promising results by training neural radiance field (NeRF) generators on unstructured 2D images, but still can not generate highly-realistic images with fine details. A critical reason is that the high memory and computation cost of volumetric representation learning greatly restricts the number of point samples for radiance integration during training. Deficient sampling not only limits the expressive power of the generator to handle fine details but also impedes effective GAN training due to the noise caused by unstable Monte Carlo sampling. We propose a novel approach that regulates point sampling and radiance field learning on 2D manifolds, embodied as a set of learned implicit surfaces in the 3D volume. For each viewing ray, we calculate ray-surface intersections and accumulate their radiance generated by the network. By training and rendering such radiance manifolds, our generator can produce high quality images with realistic fine details and strong visual 3D consistency.

Related papers

• Denoising Diffusion via Image-Based Rendering [54.20828696348574]
  We introduce the first diffusion model able to perform fast, detailed reconstruction and generation of real-world 3D scenes. First, we introduce a new neural scene representation, IB-planes, that can efficiently and accurately represent large 3D scenes. Second, we propose a denoising-diffusion framework to learn a prior over this novel 3D scene representation, using only 2D images.
  arXiv  Detail & Related papers  (2024-02-05T19:00:45Z)
• ZIGNeRF: Zero-shot 3D Scene Representation with Invertible Generative Neural Radiance Fields [2.458437232470188]
  We introduce ZIGNeRF, an innovative model that executes zero-shot Generative Adrial Network (GAN)versa for the generation of multi-view images from a single out-of-domain image. ZIGNeRF is capable of disentangling the object from the background and executing 3D operations such as 360-degree rotation or depth and horizontal translation.
  arXiv  Detail & Related papers  (2023-06-05T09:41:51Z)
• Likelihood-Based Generative Radiance Field with Latent Space Energy-Based Model for 3D-Aware Disentangled Image Representation [43.41596483002523]
  We propose a likelihood-based top-down 3D-aware 2D image generative model that incorporates 3D representation via Neural Radiance Fields (NeRF) and 2D imaging process via differentiable volume rendering. Experiments on several benchmark datasets demonstrate that the NeRF-LEBM can infer 3D object structures from 2D images, generate 2D images with novel views and objects, learn from incomplete 2D images, and learn from 2D images with known or unknown camera poses.
  arXiv  Detail & Related papers  (2023-04-16T23:44:41Z)
• GVP: Generative Volumetric Primitives [76.95231302205235]
  We present Generative Volumetric Primitives (GVP), the first pure 3D generative model that can sample and render 512-resolution images in real-time. GVP jointly models a number of primitives and their spatial information, both of which can be efficiently generated via a 2D convolutional network. Experiments on several datasets demonstrate superior efficiency and 3D consistency of GVP over the state-of-the-art.
  arXiv  Detail & Related papers  (2023-03-31T16:50:23Z)
• NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions [97.27105725738016]
  integration of Neural Radiance Fields (NeRFs) and generative models, such as Generative Adversarial Networks (GANs) has transformed 3D-aware generation from single-view images. We propose a simple and effective method, based on re-using the well-disentangled latent space of a pre-trained NeRF-GAN in a pose-conditioned convolutional network to directly generate 3D-consistent images corresponding to the underlying 3D representations.
  arXiv  Detail & Related papers  (2023-03-22T18:59:48Z)
• Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator [68.0533826852601]
  3D-aware image synthesis aims at learning a generative model that can render photo-realistic 2D images while capturing decent underlying 3D shapes. Existing methods fail to obtain moderate 3D shapes. We propose a geometry-aware discriminator to improve 3D-aware GANs.
  arXiv  Detail & Related papers  (2022-09-30T17:59:37Z)
• GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds [28.660893916203747]
  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. Experiments on FFHQ and AFHQv2 datasets show that our method can produce high-quality 3D-consistent results.
  arXiv  Detail & Related papers  (2022-06-15T02:35:51Z)
• DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars [92.37436369781692]
  We present DRaCoN, a framework for learning full-body volumetric avatars. It exploits the advantages of both the 2D and 3D neural rendering techniques. Experiments on the challenging ZJU-MoCap and Human3.6M datasets indicate that DRaCoN outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T17:59:15Z)
• 3D-aware Image Synthesis via Learning Structural and Textural Representations [39.681030539374994]
  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.
  arXiv  Detail & Related papers  (2021-12-20T18:59:40Z)
• A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis [163.96778522283967]
  We propose a shading-guided generative implicit model that is able to learn a starkly improved shape representation. An accurate 3D shape should also yield a realistic rendering under different lighting conditions. Our experiments on multiple datasets show that the proposed approach achieves photorealistic 3D-aware image synthesis.
  arXiv  Detail & Related papers  (2021-10-29T10:53:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.