SuperCarver: Texture-Consistent 3D Geometry Super-Resolution for High-Fidelity Surface Detail Generation
- URL: http://arxiv.org/abs/2503.09439v2
- Date: Mon, 04 Aug 2025 02:36:36 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: We introduce SuperCarver, a 3D geometry super-resolution pipeline for supplementing texture-consistent surface details onto a given coarse mesh.<n> Experiments demonstrate that our SuperCarver is capable of generating realistic and expressive surface details depicted by the actual texture appearance.
- Score: 70.76810765911499
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Conventional production workflow of high-precision mesh assets necessitates a cumbersome and laborious process of manual sculpting by specialized 3D artists/modelers. The recent years have witnessed remarkable advances in AI-empowered 3D content creation for generating plausible structures and intricate appearances from images or text prompts. However, synthesizing realistic surface details still poses great challenges, and enhancing the geometry fidelity of existing lower-quality 3D meshes (instead of image/text-to-3D generation) remains an open problem. In this paper, we introduce SuperCarver, a 3D geometry super-resolution pipeline for supplementing texture-consistent surface details onto a given coarse mesh. We start by rendering the original textured mesh into the image domain from multiple viewpoints. To achieve detail boosting, we construct a deterministic prior-guided normal diffusion model, which is fine-tuned on a carefully curated dataset of paired detail-lacking and detail-rich normal map renderings. To update mesh surfaces from potentially imperfect normal map predictions, we design a noise-resistant inverse rendering scheme through deformable distance field. Experiments demonstrate that our SuperCarver is capable of generating realistic and expressive surface details depicted by the actual texture appearance, making it a powerful tool to both upgrade historical low-quality 3D assets and reduce the workload of sculpting high-poly meshes.
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) - Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation [39.702921832009466]
We introduce a new method that incorporates touch as an additional modality to improve the geometric details of generated 3D assets.<n>We design a lightweight 3D texture field to synthesize visual and tactile textures, guided by 2D diffusion model priors.<n>We are the first to leverage high-resolution tactile sensing to enhance geometric details for 3D generation tasks.
arXiv Detail & Related papers (2024-12-09T18:59:45Z) - 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) - GETAvatar: Generative Textured Meshes for Animatable Human Avatars [69.56959932421057]
We study the problem of 3D-aware full-body human generation, aiming at creating animatable human avatars with high-quality geometries and textures.
We propose GETAvatar, a Generative model that directly generates Explicit Textured 3D rendering for animatable human Avatar.
arXiv Detail & Related papers (2023-10-04T10:30:24Z) - 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) - 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) - GET3D: A Generative Model of High Quality 3D Textured Shapes Learned
from Images [72.15855070133425]
We introduce GET3D, a Generative model that directly generates Explicit Textured 3D meshes with complex topology, rich geometric details, and high-fidelity textures.
GET3D is able to generate high-quality 3D textured meshes, ranging from cars, chairs, animals, motorbikes and human characters to buildings.
arXiv Detail & Related papers (2022-09-22T17:16:19Z) - Texturify: Generating Textures on 3D Shape Surfaces [34.726179801982646]
We propose Texturify to learn a 3D shape that predicts texture on the 3D input.
Our method does not require any 3D color supervision to learn 3D objects.
arXiv Detail & Related papers (2022-04-05T18:00:04Z) - 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) - OSTeC: One-Shot Texture Completion [86.23018402732748]
We propose an unsupervised approach for one-shot 3D facial texture completion.
The proposed approach rotates an input image in 3D and fill-in the unseen regions by reconstructing the rotated image in a 2D face generator.
We frontalize the target image by projecting the completed texture into the generator.
arXiv Detail & Related papers (2020-12-30T23:53:26Z)
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.