UV-free Texture Generation with Denoising and Geodesic Heat Diffusions

• URL: http://arxiv.org/abs/2408.16762v2
• Date: Thu, 10 Oct 2024 14:48:57 GMT
• Title: UV-free Texture Generation with Denoising and Geodesic Heat Diffusions
• Authors: Simone Foti, Stefanos Zafeiriou, Tolga Birdal,
• Abstract summary: Seams, wasted UV space, and varying resolution over the surface are the most prominent issues of the standard UV-based processing mechanism of meshes. We propose to represent textures as coloured point-cloud colours generated by a denoising diffusion model constrained to operate on the surface of 3D meshes.
• Score: 50.55154348768031
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Seams, distortions, wasted UV space, vertex-duplication, and varying resolution over the surface are the most prominent issues of the standard UV-based texturing of meshes. These issues are particularly acute when automatic UV-unwrapping techniques are used. For this reason, instead of generating textures in automatically generated UV-planes like most state-of-the-art methods, we propose to represent textures as coloured point-clouds whose colours are generated by a denoising diffusion probabilistic model constrained to operate on the surface of 3D objects. Our sampling and resolution agnostic generative model heavily relies on heat diffusion over the surface of the meshes for spatial communication between points. To enable processing of arbitrarily sampled point-cloud textures and ensure long-distance texture consistency we introduce a fast re-sampling of the mesh spectral properties used during the heat diffusion and introduce a novel heat-diffusion-based self-attention mechanism. Our code and pre-trained models are available at github.com/simofoti/UV3-TeD.

Related papers

• TEXGen: a Generative Diffusion Model for Mesh Textures [63.43159148394021]
  We focus on the fundamental problem of learning in the UV texture space itself. We propose a scalable network architecture that interleaves convolutions on UV maps with attention layers on point clouds. We train a 700 million parameter diffusion model that can generate UV texture maps guided by text prompts and single-view images.
  arXiv  Detail & Related papers  (2024-11-22T05:22:11Z)
• CVT-xRF: Contrastive In-Voxel Transformer for 3D Consistent Radiance Fields from Sparse Inputs [65.80187860906115]
  We propose a novel approach to improve NeRF's performance with sparse inputs. We first adopt a voxel-based ray sampling strategy to ensure that the sampled rays intersect with a certain voxel in 3D space. We then randomly sample additional points within the voxel and apply a Transformer to infer the properties of other points on each ray, which are then incorporated into the volume rendering.
  arXiv  Detail & Related papers  (2024-03-25T15:56:17Z)
• Nuvo: Neural UV Mapping for Unruly 3D Representations [61.87715912587394]
  Existing UV mapping algorithms operate on geometry produced by state-of-the-art 3D reconstruction and generation techniques. We present a UV mapping method designed to operate on geometry produced by 3D reconstruction and generation techniques.
  arXiv  Detail & Related papers  (2023-12-11T18:58:38Z)
• Texture Generation on 3D Meshes with Point-UV Diffusion [86.69672057856243]
  We present Point-UV diffusion, a coarse-to-fine pipeline that marries the denoising diffusion model with UV mapping to generate high-quality texture images in UV space. Our method can process meshes of any genus, generating diversified, geometry-compatible, and high-fidelity textures.
  arXiv  Detail & Related papers  (2023-08-21T06:20:54Z)
• Relightify: Relightable 3D Faces from a Single Image via Diffusion Models [86.3927548091627]
  We present the first approach to use diffusion models as a prior for highly accurate 3D facial BRDF reconstruction from a single image. In contrast to existing methods, we directly acquire the observed texture from the input image, thus, resulting in more faithful and consistent estimation.
  arXiv  Detail & Related papers  (2023-05-10T11:57:49Z)
• FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction [46.3392612457273]
  This dataset contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions. Our pipeline utilizes the recent advances in StyleGAN-based facial image editing approaches. Experiments show that our method improves the reconstruction accuracy over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-25T03:21:05Z)

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.