Scene2Hap: Combining LLMs and Physical Modeling for Automatically Generating Vibrotactile Signals for Full VR Scenes (2504.19611v1)

Abstract: Haptic feedback contributes to immersive virtual reality (VR) experiences. Designing such feedback at scale, for all objects within a VR scene and their respective arrangements, remains a time-consuming task. We present Scene2Hap, an LLM-centered system that automatically designs object-level vibrotactile feedback for entire VR scenes based on the objects' semantic attributes and physical context. Scene2Hap employs a multimodal LLM to estimate the semantics and physical context of each object, including its material properties and vibration behavior, from the multimodal information present in the VR scene. This semantic and physical context is then used to create plausible vibrotactile signals by generating or retrieving audio signals and converting them to vibrotactile signals. For the more realistic spatial rendering of haptics in VR, Scene2Hap estimates the propagation and attenuation of vibration signals from their source across objects in the scene, considering the estimated material properties and physical context, such as the distance and contact between virtual objects. Results from two user studies confirm that Scene2Hap successfully estimates the semantics and physical context of VR scenes, and the physical modeling of vibration propagation improves usability, perceived materiality, and spatial awareness.