GramGAN: Deep 3D Texture Synthesis From 2D Exemplars

• URL: http://arxiv.org/abs/2006.16112v2
• Date: Tue, 30 Jun 2020 10:33:59 GMT
• Title: GramGAN: Deep 3D Texture Synthesis From 2D Exemplars
• Authors: Tiziano Portenier, Siavash Bigdeli, Orcun Goksel
• Abstract summary: We present a novel texture synthesis framework, enabling the generation of infinite, high-quality 3D textures given a 2D exemplar image. Inspired by recent advances in natural texture synthesis, we train deep neural models to generate textures by non-linearly combining learned noise frequencies. To achieve a highly realistic output conditioned on an exemplar patch, we propose a novel loss function that combines ideas from both style transfer and generative adversarial networks.
• Score: 7.553635339893189
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel texture synthesis framework, enabling the generation of infinite, high-quality 3D textures given a 2D exemplar image. Inspired by recent advances in natural texture synthesis, we train deep neural models to generate textures by non-linearly combining learned noise frequencies. To achieve a highly realistic output conditioned on an exemplar patch, we propose a novel loss function that combines ideas from both style transfer and generative adversarial networks. In particular, we train the synthesis network to match the Gram matrices of deep features from a discriminator network. In addition, we propose two architectural concepts and an extrapolation strategy that significantly improve generalization performance. In particular, we inject both model input and condition into hidden network layers by learning to scale and bias hidden activations. Quantitative and qualitative evaluations on a diverse set of exemplars motivate our design decisions and show that our system performs superior to previous state of the art. Finally, we conduct a user study that confirms the benefits of our framework.

Related papers

• DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features [65.8738034806085]
  DistillNeRF is a self-supervised learning framework for understanding 3D environments in autonomous driving. It predicts a rich neural scene representation from sparse, single-frame multi-view camera inputs. It is trained self-supervised with differentiable rendering to reconstruct RGB, depth, or feature images.
  arXiv  Detail & Related papers  (2024-06-17T21:15:13Z)
• High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization [51.878078860524795]
  We present a high-fidelity 3D generative adversarial network (GAN) inversion framework that can synthesize photo-realistic novel views. Our approach enables high-fidelity 3D rendering from a single image, which is promising for various applications of AI-generated 3D content.
  arXiv  Detail & Related papers  (2022-11-28T18:59:52Z)
• 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)
• Unsupervised High-Fidelity Facial Texture Generation and Reconstruction [20.447635896077454]
  We propose a novel unified pipeline for both tasks, generation of both geometry and texture, and recovery of high-fidelity texture. Our texture model is learned, in an unsupervised fashion, from natural images as opposed to scanned texture maps. By applying precise 3DMM fitting, we can seamlessly integrate our modeled textures into synthetically generated background images.
  arXiv  Detail & Related papers  (2021-10-10T10:59:04Z)
• Deep Hybrid Self-Prior for Full 3D Mesh Generation [57.78562932397173]
  We propose to exploit a novel hybrid 2D-3D self-prior in deep neural networks to significantly improve the geometry quality. In particular, we first generate an initial mesh using a 3D convolutional neural network with 3D self-prior, and then encode both 3D information and color information in the 2D UV atlas. Our method recovers the 3D textured mesh model of high quality from sparse input, and outperforms the state-of-the-art methods in terms of both the geometry and texture quality.
  arXiv  Detail & Related papers  (2021-08-18T07:44:21Z)
• Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction [76.1612334630256]
  We harness the power of Generative Adversarial Networks (GANs) and Deep Convolutional Neural Networks (DCNNs) to reconstruct the facial texture and shape from single images. We demonstrate excellent results in photorealistic and identity preserving 3D face reconstructions and achieve for the first time, facial texture reconstruction with high-frequency details.
  arXiv  Detail & Related papers  (2021-05-16T16:35:44Z)
• Towards Realistic 3D Embedding via View Alignment [53.89445873577063]
  This paper presents an innovative View Alignment GAN (VA-GAN) that composes new images by embedding 3D models into 2D background images realistically and automatically. VA-GAN consists of a texture generator and a differential discriminator that are inter-connected and end-to-end trainable.
  arXiv  Detail & Related papers  (2020-07-14T14:45:00Z)
• Neural Human Video Rendering by Learning Dynamic Textures and Rendering-to-Video Translation [99.64565200170897]
  We propose a novel human video synthesis method by explicitly disentangling the learning of time-coherent fine-scale details from the embedding of the human in 2D screen space. We show several applications of our approach, such as human reenactment and novel view synthesis from monocular video, where we show significant improvement over the state of the art both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2020-01-14T18:06:27Z)
• On Demand Solid Texture Synthesis Using Deep 3D Networks [3.1542695050861544]
  This paper describes a novel approach for on demand texture synthesis based on a deep learning framework. A generative network is trained to synthesize coherent portions of solid textures of arbitrary sizes. The synthesized volumes have good visual results that are at least equivalent to the state-of-the-art patch based approaches.
  arXiv  Detail & Related papers  (2020-01-13T20:59:14Z)

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.