TextToucher: Fine-Grained Text-to-Touch Generation

TL;DR

TextToucher introduces a novel fine-grained text-to-touch generation framework that leverages multimodal language models and dual-grain text conditioning to produce high-quality tactile data, advancing multi-modal and embodied AI capabilities.

Contribution

The paper proposes a new method combining object-level and sensor-level text descriptions with diffusion transformers for detailed tactile image generation, along with a novel evaluation metric.

Findings

Outperforms existing tactile generation methods

Produces high-quality, detailed tactile samples

Demonstrates effectiveness through extensive experiments

Abstract

Tactile sensation plays a crucial role in the development of multi-modal large models and embodied intelligence. To collect tactile data with minimal cost as possible, a series of studies have attempted to generate tactile images by vision-to-touch image translation. However, compared to text modality, visual modality-driven tactile generation cannot accurately depict human tactile sensation. In this work, we analyze the characteristics of tactile images in detail from two granularities: object-level (tactile texture, tactile shape), and sensor-level (gel status). We model these granularities of information through text descriptions and propose a fine-grained Text-to-Touch generation method (TextToucher) to generate high-quality tactile samples. Specifically, we introduce a multimodal large language model to build the text sentences about object-level tactile information and employ a set of learnable text prompts to represent the sensor-level tactile information. To better guide the tactile generation process with the built text information, we fuse the dual grains of text information and explore various dual-grain text conditioning methods within the diffusion transformer architecture. Furthermore, we propose a Contrastive Text-Touch Pre-training (CTTP) metric to precisely evaluate the quality of text-driven generated tactile data. Extensive experiments demonstrate the superiority of our TextToucher method. The source codes will be available at \url{https://github.com/TtuHamg/TextToucher}.