What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs

• URL: http://arxiv.org/abs/2401.02411v1
• Date: Thu, 4 Jan 2024 18:50:38 GMT
• Title: What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs
• Authors: Alex Trevithick, Matthew Chan, Towaki Takikawa, Umar Iqbal, Shalini De Mello, Manmohan Chandraker, Ravi Ramamoorthi, Koki Nagano
• Abstract summary: 3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries. Yet, the significant memory and computational costs of dense sampling in volume rendering have forced 3D GANs to adopt patch-based training or employ low-resolution rendering with post-processing 2D super resolution. We propose techniques to scale neural volume rendering to the much higher resolution of native 2D images, thereby resolving fine-grained 3D geometry with unprecedented detail.
• Score: 82.3936309001633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of 2D images via neural volume rendering. Yet, the significant memory and computational costs of dense sampling in volume rendering have forced 3D GANs to adopt patch-based training or employ low-resolution rendering with post-processing 2D super resolution, which sacrifices multiview consistency and the quality of resolved geometry. Consequently, 3D GANs have not yet been able to fully resolve the rich 3D geometry present in 2D images. In this work, we propose techniques to scale neural volume rendering to the much higher resolution of native 2D images, thereby resolving fine-grained 3D geometry with unprecedented detail. Our approach employs learning-based samplers for accelerating neural rendering for 3D GAN training using up to 5 times fewer depth samples. This enables us to explicitly "render every pixel" of the full-resolution image during training and inference without post-processing superresolution in 2D. Together with our strategy to learn high-quality surface geometry, our method synthesizes high-resolution 3D geometry and strictly view-consistent images while maintaining image quality on par with baselines relying on post-processing super resolution. We demonstrate state-of-the-art 3D gemetric quality on FFHQ and AFHQ, setting a new standard for unsupervised learning of 3D shapes in 3D GANs.

Related papers

• GETAvatar: Generative Textured Meshes for Animatable Human Avatars [69.56959932421057]
  We study the problem of 3D-aware full-body human generation, aiming at creating animatable human avatars with high-quality geometries and textures. We propose GETAvatar, a Generative model that directly generates Explicit Textured 3D rendering for animatable human Avatar.
  arXiv  Detail & Related papers  (2023-10-04T10:30:24Z)
• Guide3D: Create 3D Avatars from Text and Image Guidance [55.71306021041785]
  Guide3D is a text-and-image-guided generative model for 3D avatar generation based on diffusion models. Our framework produces topologically and structurally correct geometry and high-resolution textures.
  arXiv  Detail & Related papers  (2023-08-18T17:55:47Z)
• Mimic3D: Thriving 3D-Aware GANs via 3D-to-2D Imitation [29.959223778769513]
  We propose a novel learning strategy, namely 3D-to-2D imitation, which enables a 3D-aware GAN to generate high-quality images. We also introduce 3D-aware convolutions into the generator for better 3D representation learning. With the above strategies, our method reaches FID scores of 5.4 and 4.3 on FFHQ and AFHQ-v2 Cats, respectively, at 512x512 resolution.
  arXiv  Detail & Related papers  (2023-03-16T02:18:41Z)
• Self-Supervised Geometry-Aware Encoder for Style-Based 3D GAN Inversion [115.82306502822412]
  StyleGAN has achieved great progress in 2D face reconstruction and semantic editing via image inversion and latent editing. A corresponding generic 3D GAN inversion framework is still missing, limiting the applications of 3D face reconstruction and semantic editing. We study the challenging problem of 3D GAN inversion where a latent code is predicted given a single face image to faithfully recover its 3D shapes and detailed textures.
  arXiv  Detail & Related papers  (2022-12-14T18:49:50Z)
• XDGAN: Multi-Modal 3D Shape Generation in 2D Space [60.46777591995821]
  We propose a novel method to convert 3D shapes into compact 1-channel geometry images and leverage StyleGAN3 and image-to-image translation networks to generate 3D objects in 2D space. The generated geometry images are quick to convert to 3D meshes, enabling real-time 3D object synthesis, visualization and interactive editing. We show both quantitatively and qualitatively that our method is highly effective at various tasks such as 3D shape generation, single view reconstruction and shape manipulation, while being significantly faster and more flexible compared to recent 3D generative models.
  arXiv  Detail & Related papers  (2022-10-06T15:54:01Z)
• 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)
• 3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations [31.095503715696722]
  We propose the factorized representations which are view-independent and light-disentangled, and training schemes with randomly sampled light conditions. We demonstrate the superiority of our method by visualizing factorized representations, re-lighted images, and albedo-textured meshes. This is the first work that extracts albedo-textured meshes with unposed 2D images without any additional labels or assumptions.
  arXiv  Detail & Related papers  (2022-03-12T15:23:17Z)
• StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation [34.01352591390208]
  We introduce a high resolution, 3D-consistent image and shape generation technique which we call StyleSDF. Our method is trained on single-view RGB data only, and stands on the shoulders of StyleGAN2 for image generation.
  arXiv  Detail & Related papers  (2021-12-21T18:45:45Z)
• Efficient Geometry-aware 3D Generative Adversarial Networks [50.68436093869381]
  Existing 3D GANs are either compute-intensive or make approximations that are not 3D-consistent. In this work, we improve the computational efficiency and image quality of 3D GANs without overly relying on these approximations. We introduce an expressive hybrid explicit-implicit network architecture that synthesizes not only high-resolution multi-view-consistent images in real time but also produces high-quality 3D geometry.
  arXiv  Detail & Related papers  (2021-12-15T08:01:43Z)

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.