# Deep Geometric Texture Synthesis

arXiv: https://arxiv.org/abs/2007.00074 · HTML: https://mlsanity.com/paper/2007.00074/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 30 of 51)

- [Text2Mesh: Text-Driven Neural Stylization for Meshes](https://mlsanity.com/paper/2112.03221/) (2021)
- [TEXTure: Text-Guided Texturing of 3D Shapes](https://mlsanity.com/paper/2302.01721/) (2023)
- [Learning Smooth Neural Functions via Lipschitz Regularization](https://mlsanity.com/paper/2202.08345/) (2022)
- [TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition](https://mlsanity.com/paper/2210.11277/) (2022)
- [Deep Learning on Implicit Neural Representations of Shapes](https://mlsanity.com/paper/2302.05438/) (2023)
- [Subdivision-Based Mesh Convolution Networks](https://mlsanity.com/paper/2106.02285/) (2021)
- [Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields](https://mlsanity.com/paper/2106.05187/) (2021)
- [3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations](https://mlsanity.com/paper/2108.12958/) (2021)
- [3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation](https://mlsanity.com/paper/2311.09571/) (2023)
- [Learning to Generate 3D Shapes from a Single Example](https://mlsanity.com/paper/2208.02946/) (2022)
- [Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape](https://mlsanity.com/paper/2305.15399/) (2023)
- [EASI-Tex: Edge-Aware Mesh Texturing from Single Image](https://mlsanity.com/paper/2405.17393/) (2024)
- [SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps](https://mlsanity.com/paper/2201.05120/) (2022)
- [Catch-A-Waveform: Learning to Generate Audio from a Single Short Example](https://mlsanity.com/paper/2106.06426/) (2021)
- [Geometry in Style: 3D Stylization via Surface Normal Deformation](https://mlsanity.com/paper/2503.23241/) (2025)
- [Neural Progressive Meshes](https://mlsanity.com/paper/2308.05741/) (2023)
- [SinGRAF: Learning a 3D Generative Radiance Field for a Single Scene](https://mlsanity.com/paper/2211.17260/) (2022)
- [What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives](https://mlsanity.com/paper/2211.06009/) (2022)
- [Deep Internal Learning: Deep Learning from a Single Input](https://mlsanity.com/paper/2312.07425/) (2023)
- [TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generation](https://mlsanity.com/paper/2210.05735/) (2022)
- [PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation](https://mlsanity.com/paper/2207.11790/) (2022)
- [Text-guided Controllable Mesh Refinement for Interactive 3D Modeling](https://mlsanity.com/paper/2406.01592/) (2024)
- [DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement](https://mlsanity.com/paper/2409.06129/) (2024)
- [TetWeave: Isosurface Extraction using On-The-Fly Delaunay Tetrahedral Grids for Gradient-Based Mesh Optimization](https://mlsanity.com/paper/2505.04590/) (2025)
- [ART-DECO: Arbitrary Text Guidance for 3D Detailizer Construction](https://mlsanity.com/paper/2505.20431/) (2025)
- [PEGAsus: 3D Personalization of Geometry and Appearance](https://mlsanity.com/paper/2602.08198/) (2026)
- [Representation-Agnostic Shape Fields](https://mlsanity.com/paper/2203.10259/) (2022)
- [Text-guided Image-and-Shape Editing and Generation: A Short Survey](https://mlsanity.com/paper/2304.09244/) (2023)
- [Directional Texture Editing for 3D Models](https://mlsanity.com/paper/2309.14872/) (2023)
- [Retargeting Visual Data with Deformation Fields](https://mlsanity.com/paper/2311.13297/) (2023)

## Abstract (arXiv)

Recently, deep generative adversarial networks for image generation have advanced rapidly; yet, only a small amount of research has focused on generative models for irregular structures, particularly meshes. Nonetheless, mesh generation and synthesis remains a fundamental topic in computer graphics. In this work, we propose a novel framework for synthesizing geometric textures. It learns geometric texture statistics from local neighborhoods (i.e., local triangular patches) of a single reference 3D model. It learns deep features on the faces of the input triangulation, which is used to subdivide and generate offsets across multiple scales, without parameterization of the reference or target mesh. Our network displaces mesh vertices in any direction (i.e., in the normal and tangential direction), enabling synthesis of geometric textures, which cannot be expressed by a simple 2D displacement map. Learning and synthesizing on local geometric patches enables a genus-oblivious framework, facilitating texture transfer between shapes of different genus.