SuperCarver: Texture-Consistent 3D Geometry Super-Resolution for High-Fidelity Surface Detail Generation

• URL: http://arxiv.org/abs/2503.09439v1
• Date: Wed, 12 Mar 2025 14:38:45 GMT
• Title: SuperCarver: Texture-Consistent 3D Geometry Super-Resolution for High-Fidelity Surface Detail Generation
• Authors: Qijian Zhang, Xiaozheng Jian, Xuan Zhang, Wenping Wang, Junhui Hou,
• Abstract summary: SuperCarver is a 3D geometry framework specifically tailored for adding texture-consistent surface details to given coarse meshes.<n>To achieve geometric detail generation, we develop a deterministic prior-guided normal diffusion model fine-tuned on a dataset of paired low-poly and high-poly normal renderings.<n>To optimize mesh structures from potentially imperfect normal map predictions, we design a simple yet effective noise-resistant inverse rendering scheme.
• Score: 70.76810765911499
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional production workflow of high-precision 3D mesh assets necessitates a cumbersome and laborious process of manual sculpting by specialized modelers. The recent years have witnessed remarkable advances in AI-empowered 3D content creation. However, although the latest state-of-the-arts are already capable of generating plausible structures and intricate appearances from images or text prompts, the actual mesh surfaces are typically over-smoothing and lack geometric details. This paper introduces SuperCarver, a 3D geometry super-resolution framework particularly tailored for adding texture-consistent surface details to given coarse meshes. Technically, we start by rendering the original textured mesh into the image domain from multiple viewpoints. To achieve geometric detail generation, we develop a deterministic prior-guided normal diffusion model fine-tuned on a carefully curated dataset of paired low-poly and high-poly normal renderings. To optimize mesh structures from potentially imperfect normal map predictions, we design a simple yet effective noise-resistant inverse rendering scheme based on distance field deformation. Extensive experiments show that SuperCarver generates realistic and expressive surface details as depicted by specific texture appearances, making it a powerful tool for automatically upgrading massive outdated low-quality assets and shortening the iteration cycle of high-quality mesh production in practical applications.

Related papers

• Textured Mesh Saliency: Bridging Geometry and Texture for Human Perception in 3D Graphics [50.23625950905638]
  We present a new dataset for textured mesh saliency, created through an innovative eye-tracking experiment in a six degrees of freedom (6-DOF) VR environment.<n>Our proposed model predicts saliency maps for textured mesh surfaces by treating each triangular face as an individual unit and assigning a saliency density value to reflect the importance of each local surface region.
  arXiv  Detail & Related papers  (2024-12-11T08:27:33Z)
• 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)
• CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner [34.78919665494048]
  CraftsMan can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces. Our method achieves high efficacy in producing superior-quality 3D assets compared to existing methods.
  arXiv  Detail & Related papers  (2024-05-23T18:30:12Z)
• 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)
• Facial Geometric Detail Recovery via Implicit Representation [147.07961322377685]
  We present a robust texture-guided geometric detail recovery approach using only a single in-the-wild facial image. Our method combines high-quality texture completion with the powerful expressiveness of implicit surfaces. Our method not only recovers accurate facial details but also decomposes normals, albedos, and shading parts in a self-supervised way.
  arXiv  Detail & Related papers  (2022-03-18T01:42:59Z)
• 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.