# VQ-Touch: A Data-Efficient Tactile Generation Framework Across Sensors and Scenarios

arXiv: https://arxiv.org/abs/2607.14728 · HTML: https://mlsanity.com/paper/2607.14728/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 28 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 22 of 22)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://mlsanity.com/paper/2111.14822/) (2021)
- [Vector-quantized Image Modeling with Improved VQGAN](https://mlsanity.com/paper/2110.04627/) (2021)
- [More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch](https://mlsanity.com/paper/1805.11085/) (2018)
- [Binding Touch to Everything: Learning Unified Multimodal Tactile Representations](https://mlsanity.com/paper/2401.18084/) (2024)
- [Touch and Go: Learning from Human-Collected Vision and Touch](https://mlsanity.com/paper/2211.12498/) (2022)
- [A Touch, Vision, and Language Dataset for Multimodal Alignment](https://mlsanity.com/paper/2402.13232/) (2024)
- [Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks](https://mlsanity.com/paper/2406.13640/) (2024)
- [Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformers](https://mlsanity.com/paper/2310.05400/) (2023)
- [Active Clothing Material Perception using Tactile Sensing and Deep Learning](https://mlsanity.com/paper/1711.00574/) (2017)
- [Visuotactile Affordances for Cloth Manipulation with Local Control](https://mlsanity.com/paper/2212.05108/) (2022)
- [GelSight Fin Ray: Incorporating Tactile Sensing into a Soft Compliant Robotic Gripper](https://mlsanity.com/paper/2204.07146/) (2022)
- [M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation](https://mlsanity.com/paper/2401.17032/) (2024)
- [Multimodal Zero-Shot Learning for Tactile Texture Recognition](https://mlsanity.com/paper/2306.12705/) (2023)
- [TouchFormer: A Robust Transformer-based Framework for Multimodal Material Perception](https://mlsanity.com/paper/2511.19509/) (2025)
- [Multimodal perception for dexterous manipulation](https://mlsanity.com/paper/2112.14298/) (2021)
- [TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation](https://mlsanity.com/paper/2606.11637/) (2026)
- [TacReasoner: A Dynamic Tactile-Language Framework for Interactive Reasoning in Real-World Scenarios](https://mlsanity.com/paper/2607.05131/) (2026)

## Abstract (arXiv)

Tactile image generation significantly reduces the dependency on expensive and wear-prone sensors by synthesizing high-fidelity tactile data, offering an efficient solution for tactile information acquisition in robotic perception and human-machine interaction systems. However, existing methods depend on large-scale, diverse datasets from specific sensors and lack efficient data utilization and robust generalization capabilities, struggling in vision-limited environments. To address this, we introduce VQ-Touch, a tactile generation framework that supports both cross-sensor and multi-scenario applications. Specifically, to efficiently extract complex deformation and texture features from the data, we propose DM-VQGAN, an effective tactile representation learner. Furthermore, we introduce a discrete diffusion decoder with a unified conditioning interface, supporting multimodal generation tasks such as images and labels, and enhances the model's generalization capability through few-shot mixed training, thus achieving compatibility with current mainstream sensors and their variants. Experiments show that VQ-Touch surpasses state-of-the-art methods in multiple tasks.