A geometrically aware auto-encoder for multi-texture synthesis

• URL: http://arxiv.org/abs/2302.01616v3
• Date: Thu, 29 Jun 2023 12:48:29 GMT
• Title: A geometrically aware auto-encoder for multi-texture synthesis
• Authors: Pierrick Chatillon, Yann Gousseau, Sidonie Lefebvre
• Abstract summary: We propose an auto-encoder architecture for multi-texture synthesis. Images are embedded in a compact and geometrically consistent latent space. Texture synthesis and tasks can be performed directly from these latent codes.
• Score: 1.2891210250935146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an auto-encoder architecture for multi-texture synthesis. The approach relies on both a compact encoder accounting for second order neural statistics and a generator incorporating adaptive periodic content. Images are embedded in a compact and geometrically consistent latent space, where the texture representation and its spatial organisation are disentangled. Texture synthesis and interpolation tasks can be performed directly from these latent codes. Our experiments demonstrate that our model outperforms state-of-the-art feed-forward methods in terms of visual quality and various texture related metrics.

Related papers

• Generating Non-Stationary Textures using Self-Rectification [70.91414475376698]
  This paper addresses the challenge of example-based non-stationary texture synthesis. We introduce a novel twostep approach wherein users first modify a reference texture using standard image editing tools. Our proposed method, termed "self-rectification", automatically refines this target into a coherent, seamless texture.
  arXiv  Detail & Related papers  (2024-01-05T15:07:05Z)
• GM-NeRF: Learning Generalizable Model-based Neural Radiance Fields from Multi-view Images [79.39247661907397]
  We introduce an effective framework Generalizable Model-based Neural Radiance Fields to synthesize free-viewpoint images. Specifically, we propose a geometry-guided attention mechanism to register the appearance code from multi-view 2D images to a geometry proxy.
  arXiv  Detail & Related papers  (2023-03-24T03:32:02Z)
• Paying U-Attention to Textures: Multi-Stage Hourglass Vision Transformer for Universal Texture Synthesis [2.8998926117101367]
  We present a novel U-Attention vision Transformer for universal texture synthesis. We exploit the natural long-range dependencies enabled by the attention mechanism to allow our approach to synthesize diverse textures. We propose a hierarchical hourglass backbone that attends to the global structure and performs patch mapping at varying scales.
  arXiv  Detail & Related papers  (2022-02-23T18:58:56Z)
• SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps [3.504542161036043]
  We present SeamlessGAN, a method capable of automatically generating tileable texture maps from a single input exemplar. In contrast to most existing methods, focused solely on solving the synthesis problem, our work tackles both problems, synthesis and tileability, simultaneously.
  arXiv  Detail & Related papers  (2022-01-13T18:24:26Z)
• Cycle-Consistent Inverse GAN for Text-to-Image Synthesis [101.97397967958722]
  We propose a novel unified framework of Cycle-consistent Inverse GAN for both text-to-image generation and text-guided image manipulation tasks. We learn a GAN inversion model to convert the images back to the GAN latent space and obtain the inverted latent codes for each image. In the text-guided optimization module, we generate images with the desired semantic attributes by optimizing the inverted latent codes.
  arXiv  Detail & Related papers  (2021-08-03T08:38:16Z)
• Dynamic Texture Recognition via Nuclear Distances on Kernelized Scattering Histogram Spaces [95.21606283608683]
  This work proposes to describe dynamic textures as kernelized spaces of frame-wise feature vectors computed using the Scattering transform. By combining these spaces with a basis-invariant metric, we get a framework that produces competitive results for nearest neighbor classification and state-of-the-art results for nearest class center classification.
  arXiv  Detail & Related papers  (2021-02-01T13:54:24Z)
• Conceptual Compression via Deep Structure and Texture Synthesis [42.68994438290913]
  We propose a novel conceptual compression framework that encodes visual data into compact structure and texture representations, then decodes in a deep synthesis fashion. In particular, we propose to compress images by a dual-layered model consisting of two complementary visual features. At the encoder side, the structural maps and texture representations are individually extracted and compressed, generating the compact, interpretable, inter-operable bitstreams. During the decoding stage, a hierarchical fusion GAN (HF-GAN) is proposed to learn the synthesis paradigm where the textures are rendered into the decoded structural maps, leading to high-quality reconstruction
  arXiv  Detail & Related papers  (2020-11-10T08:48:32Z)
• Co-occurrence Based Texture Synthesis [25.4878061402506]
  We propose a fully convolutional generative adversarial network, conditioned locally on co-occurrence statistics, to generate arbitrarily large images. We show that our solution offers a stable, intuitive and interpretable latent representation for texture synthesis.
  arXiv  Detail & Related papers  (2020-05-17T08:01:44Z)
• Towards Analysis-friendly Face Representation with Scalable Feature and Texture Compression [113.30411004622508]
  We show that a universal and collaborative visual information representation can be achieved in a hierarchical way. Based on the strong generative capability of deep neural networks, the gap between the base feature layer and enhancement layer is further filled with the feature level texture reconstruction. To improve the efficiency of the proposed framework, the base layer neural network is trained in a multi-task manner.
  arXiv  Detail & Related papers  (2020-04-21T14:32:49Z)
• Hierarchy Composition GAN for High-fidelity Image Synthesis [57.32311953820988]
  This paper presents an innovative Hierarchical Composition GAN (HIC-GAN) HIC-GAN incorporates image synthesis in geometry and appearance domains into an end-to-end trainable network. Experiments on scene text image synthesis, portrait editing and indoor rendering tasks show that the proposed HIC-GAN achieves superior synthesis performance qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2019-05-12T11:11:24Z)

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.