# Streamlining Haptic Design with Micro-Collision Haptic Map Generated by Stable Diffusion +
## 1 Introduction  
Haptic feedback is critical in a virtual environment(VE), enabling users to experience more realistic physical sensations [1]. Simulating real-world tactile sensations in virtual environments is a focus research objective, such as rendering haptic feedback for virtual surface textures [2]. The expansion of the haptic device market is largely fueled by advancements in the entertainment and the experience economy, as seen in the widespread use of compact mobile devices such as game controllers and VR headsets equipped with linear resonant actuators (LRAs) [3]. As demand for haptic experiences grows, many studies have explored advanced acquisition devices and haptic actuators [4]. While these technologies enhance user experiences, they also raise costs. Additionally, we observed that current haptic design workflows, such as Core Haptics of Apple (Apple Inc., Cupertino, CA, USA) [5] and RichTap Creator of AAC Technologies (AAC Technologies Holdings Inc., Shenzhen,China) [6], often require designers to rely on proprietary editors or have programming expertise. Although some of these tools support audio-based haptic feedback generation, creating haptic feedback that matches with visual texture information remains mainly a case-by-case process, resulting in significant workload. With the widespread adoption of generative artificial intelligence technologies, knowledge can now be embedded into the design process through generative methods. As a result, leveraging generative techniques to support haptic design has become an inevitable trend and holds great potential for enhancing the quality of haptic experiences. Generative methods have been applied in haptic design to generate matching vibrotactile feedback from visual and auditory modalities [7; 8; 9; 10; 11]. To streamline the haptic design workflow, enabling high-fidelity haptic feedback on widely available devices, we introduce micro-collision haptic (MCHa) maps and use a fine-tuned variational autoencoder (VAE) decoder of Stable Diffusion. This method simultaneously generates visual textures and matching MCHa maps. Visual texture maps define surface contact characteristics via displacement and roughness information. User interactions with micro surface dimples can be modeled as a trajectory composed of sequential collisions. Haptic feedback for virtual texture enables users to sense material properties through micro texture collisions [12], with pronounced feedback over significant height variations. Thus, height and roughness data from 2D visual texture maps can be utilized to derive a corresponding 2D haptic map. To further understand the impact of our generative tool on the haptic design workflow, we conducted a user study. We designed a haptic creation task in which designers were asked to create haptic feedback for four materials--gravel, metal, fabric, and wood--and assign them randomly to a \(4\times 4\) tactile square. Eight professional haptic designers, each with over 3 years of experience, first completed the task using a traditional workflow, and then repeated it using our generative haptic tool. In parallel, ten non-haptic interaction designers were recruited. After a brief training, they completed the same task using both the conventional workflow and our generative tool. After each task, the designers provided subjective evaluations of their experience. To ensure the practical applicability and relevance of our approach, all experiments were conducted on an iPhone 13 mini with LRA, representing one of the most widely accessible consumer-level platform types for tactile interaction. To assess the output quality, 30 users were recruited to evaluate the haptic feedback from all four groups. The participants also performed a material identification task on the \(4\times 4\) tactile square to assess the recognizability of the designed textures. The NASA-TLX results revealed no significant differences in overall user preference, but the designers reported significantly lower workload and improved efficiency when using our generative tool.