# TactStyle: Generating Tactile Textures with Generative AI for Digital Fabrication

arXiv: https://arxiv.org/abs/2503.02007 · HTML: https://mlsanity.com/paper/2503.02007/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 61 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images](https://mlsanity.com/paper/2209.11163/) (2022)
- [InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models](https://mlsanity.com/paper/2404.07191/) (2024)
- [Text2Mesh: Text-Driven Neural Stylization for Meshes](https://mlsanity.com/paper/2112.03221/) (2021)
- [TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion](https://mlsanity.com/paper/2401.09416/) (2024)
- [X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance](https://mlsanity.com/paper/2303.15764/) (2023)
- [Style2Fab: Functionality-Aware Segmentation for Fabricating Personalized 3D Models with Generative AI](https://mlsanity.com/paper/2309.06379/) (2023)
- [Shaping Realities: Enhancing 3D Generative AI with Fabrication Constraints](https://mlsanity.com/paper/2404.10142/) (2024)
- [SliceHub: Augmenting Shared 3D Model Repositories with Slicing Results for 3D Printing](https://mlsanity.com/paper/2109.14722/) (2021)
- [Shape-Haptics: Planar & Passive Force Feedback Mechanisms for Physical Interfaces](https://mlsanity.com/paper/2202.10655/) (2022)

## Built on by (most cited first, 4 of 4)

- [MechStyle: Augmenting Generative AI with Mechanical Simulation to Create Stylized and Structurally Viable 3D Models](https://mlsanity.com/paper/2509.20571/) (2025)
- [Why Modeling Human Haptic Material Perception with AI Is Difficult](https://mlsanity.com/paper/2605.16602/) (2026)
- [Compos3D: Interactive Part-Based Composition for Creative Control in Generative 3D Models](https://mlsanity.com/paper/2607.12193/) (2026)
- [InstructMesh: Selective Refinement of Generative 3D Models for Fabrication](https://mlsanity.com/paper/2608.28534/) (2026)

## Abstract (arXiv)

Recent work in Generative AI enables the stylization of 3D models based on image prompts. However, these methods do not incorporate tactile information, leading to designs that lack the expected tactile properties. We present TactStyle, a system that allows creators to stylize 3D models with images while incorporating the expected tactile properties. TactStyle accomplishes this using a modified image-generation model fine-tuned to generate heightfields for given surface textures. By optimizing 3D model surfaces to embody a generated texture, TactStyle creates models that match the desired style and replicate the tactile experience. We utilize a large-scale dataset of textures to train our texture generation model. In a psychophysical experiment, we evaluate the tactile qualities of a set of 3D-printed original textures and TactStyle's generated textures. Our results show that TactStyle successfully generates a wide range of tactile features from a single image input, enabling a novel approach to haptic design.