OMGTex: One-stage Multi-style Facial Texture Reconstruction without Geometry Guidance

• URL: http://arxiv.org/abs/2605.25778v1
• Date: Mon, 25 May 2026 12:26:56 GMT
• Title: OMGTex: One-stage Multi-style Facial Texture Reconstruction without Geometry Guidance
• Abstract summary: OMGTex is an end-to-end diffusion-based framework for reconstructing high-quality and editable facial UV textures.<n>Our core innovation is a geometry-free pipeline that directly maps a 2D face image to its corresponding editable UV texture.
• Score: 12.886725060369903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose OMGTex, an end-to-end diffusion-based framework for reconstructing high-quality and editable facial UV textures from multi-style facial images. Existing texture reconstruction methods face two major limitations: (1) Fragility due to reliance on 3D geometry priors, which are difficult to estimate accurately, especially under facial occlusions or in stylized domains; and (2) A lack of semantic disentanglement, inhibiting region-specific texture editing and style transfer. Our work addresses both challenges simultaneously. Our core innovation is a geometry-free pipeline that directly maps a 2D face image to its corresponding editable UV texture. We introduce two key techniques: First, to address the challenge of UV misalignment common in diffusion generation, we introduce a gradient-guided refinement strategy at inference time, which explicitly corrects structural consistency. Second, we leverage the inherent semantic distribution capability of diffusion models and design a novel training paradigm to enhance this tendency, enabling semantic-aware editing of facial texture. Furthermore, to address the data scarcity in multi-style texture reconstruction, we construct CANVAS, the first comprehensive paired texture reconstruction dataset covering realistic and diverse stylized domains. To the best of our knowledge, OMGTex is the first geometry-free inference framework that achieves robust, style-consistent, and editable facial texture reconstruction across diverse domains. Our method achieves state-of-the-art performance on multiple facial texture benchmarks.

Related papers

• Semantic-Guided Two-Stage GAN for Face Inpainting with Hybrid Perceptual Encoding [1.7761223012399532]
  Facial Image inpainting aims to restore the missing or corrupted regions in face images while preserving identity, structural consistency and image quality.<n>Existing methods face problems with large irregular masks, often producing blurry textures on the edges of the masked region.<n>We propose a novel architecture, which address these above challenges through semantic-guided hierarchical synthesis.
  arXiv  Detail & Related papers  (2025-12-04T17:56:08Z)
• AvatarTex: High-Fidelity Facial Texture Reconstruction from Single-Image Stylized Avatars [16.44346662761451]
  AvatarTex is a facial texture reconstruction framework capable of generating both stylized and photorealistic textures from a single image.<n>Our three-stage pipeline first completes missing texture regions via diffusion-based inpainting, refines style and structure consistency using GAN-based latent optimization, and enhances fine details through diffusion-based repainting.<n>To address the need for a stylized texture dataset, we introduce TexHub, a high-resolution collection of 20,000 multi-style UV textures with precise UV-aligned layouts.
  arXiv  Detail & Related papers  (2025-11-10T05:31:15Z)
• SeqTex: Generate Mesh Textures in Video Sequence [62.766839821764144]
  We introduce SeqTex, a novel end-to-end framework for training 3D texture generative models.<n>We show that SeqTex achieves state-of-the-art performance on both image-conditioned and text-conditioned 3D texture generation tasks.
  arXiv  Detail & Related papers  (2025-07-06T07:58:36Z)
• RomanTex: Decoupling 3D-aware Rotary Positional Embedded Multi-Attention Network for Texture Synthesis [10.350576861948952]
  RomanTex is a multiview-based texture generation framework that integrates a multi-attention network with an underlying 3D representation.<n>Our method achieves state-of-the-art results in texture quality and consistency.
  arXiv  Detail & Related papers  (2025-03-24T17:56:11Z)
• GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction [79.42244344704154]
  GausSurf employs geometry guidance from multi-view consistency in texture-rich areas and normal priors in texture-less areas of a scene.<n>Our method surpasses state-of-the-art methods in terms of reconstruction quality and computation time.
  arXiv  Detail & Related papers  (2024-11-29T03:54:54Z)
• DreamPolish: Domain Score Distillation With Progressive Geometry Generation [66.94803919328815]
  We introduce DreamPolish, a text-to-3D generation model that excels in producing refined geometry and high-quality textures. In the geometry construction phase, our approach leverages multiple neural representations to enhance the stability of the synthesis process. In the texture generation phase, we introduce a novel score distillation objective, namely domain score distillation (DSD), to guide neural representations toward such a domain.
  arXiv  Detail & Related papers  (2024-11-03T15:15:01Z)
• Text-Guided 3D Face Synthesis -- From Generation to Editing [53.86765812392627]
  We propose a unified text-guided framework from face generation to editing. We employ a fine-tuned texture diffusion model to enhance texture quality in both RGB and YUV space. We propose a self-guided consistency weight strategy to improve editing efficacy while preserving consistency.
  arXiv  Detail & Related papers  (2023-12-01T06:36:23Z)
• Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement [78.48648360358193]
  We present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency appearance with a NeRF. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering.
  arXiv  Detail & Related papers  (2023-03-03T17:14:44Z)
• Self-supervised High-fidelity and Re-renderable 3D Facial Reconstruction from a Single Image [19.0074836183624]
  We propose a novel self-supervised learning framework for reconstructing high-quality 3D faces from single-view images in-the-wild. Our framework substantially outperforms state-of-the-art approaches in both qualitative and quantitative comparisons.
  arXiv  Detail & Related papers  (2021-11-16T08:10:24Z)
• 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)
• 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)

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.