SLIDE: A Unified Mesh and Texture Generation Framework with Enhanced Geometric Control and Multi-view Consistency

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Computer Vision Pub Date : 2024-12-23 DOI:10.1007/s11263-024-02326-x
Jinyi Wang, Zhaoyang Lyu, Ben Fei, Jiangchao Yao, Ya Zhang, Bo Dai, Dahua Lin, Ying He, Yanfeng Wang
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Abstract

The generation of textured mesh is crucial for computer graphics and virtual content creation. However, current generative models often struggle with challenges such as irregular mesh structures and inconsistencies in multi-view textures. In this study, we present a unified framework for both geometry generation and texture generation, utilizing a novel sparse latent point diffusion model that specifically addresses the geometric aspects of models. Our approach employs point clouds as an efficient intermediate representation, encoding them into sparse latent points with semantically meaningful features for precise geometric control. While the sparse latent points facilitate a high-level control over the geometry, shaping the overall structure and fine details of the meshes, this control does not extend to textures. To address this, we propose a separate texture generation process that integrates multi-view priors post-geometry generation, effectively resolving the issue of multi-view texture inconsistency. This process ensures the production of coherent and high-quality textures that complement the precisely generated meshes, thereby creating visually appealing and detailed models. Our framework distinctively separates the control mechanisms for geometry and texture, leading to significant improvements in the generation of complex, textured 3D content. Evaluations on the ShapeNet dataset for geometry and the Objaverse dataset for textures demonstrate that our model surpasses existing methods in terms of geometric quality, control, and the generation of coherent, high-quality textures.

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一个统一的网格和纹理生成框架,增强几何控制和多视图一致性
纹理网格的生成对于计算机图形学和虚拟内容的创建至关重要。然而,当前的生成模型经常面临诸如不规则网格结构和多视图纹理不一致等挑战。在这项研究中,我们提出了一个统一的框架,用于几何生成和纹理生成,利用一个新的稀疏潜点扩散模型,专门解决模型的几何方面。我们的方法采用点云作为有效的中间表示,将其编码为具有语义有意义特征的稀疏潜在点,以实现精确的几何控制。虽然稀疏的隐点有助于对几何结构的高级控制,塑造网格的整体结构和精细细节,但这种控制不能扩展到纹理。为了解决这个问题,我们提出了一个单独的纹理生成过程,该过程集成了多视图先验后几何生成,有效地解决了多视图纹理不一致的问题。这个过程确保生产连贯和高质量的纹理,以补充精确生成的网格,从而创建视觉上吸引人的和详细的模型。我们的框架独特地分离了几何和纹理的控制机制,从而显著改善了复杂的纹理3D内容的生成。对ShapeNet的几何数据集和Objaverse的纹理数据集的评估表明,我们的模型在几何质量、控制和生成连贯、高质量的纹理方面超越了现有的方法。
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来源期刊
International Journal of Computer Vision
International Journal of Computer Vision 工程技术-计算机:人工智能
CiteScore
29.80
自引率
2.10%
发文量
163
审稿时长
6 months
期刊介绍: The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.
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