TouchTwin: Human-AI Haptic Authoring for 3D-Scanned Tabletop Scenes through Language and Touch
Abstract
Creating haptic experiences from real-world environments remains difficult: visual scans capture appearance but not material feel, while manual haptic authoring requires specialized expertise. We present TouchTwin, a human-AI approach that turns phone scans of tabletop scenes into editable haptic scenes. TouchTwin uses AI to propose material regions that users can experience and revise through language and spatial edits. A model trained on measured tool-surface interactions generates the haptic responses, allowing users to work with material descriptions rather than low-level signal parameters. In a study with N=12 participants and physical reference objects present, automatic drafts scored 4.0/7 for realism and 5.4/7 as useful starting points. After a median of 6.6 minutes of editing, realism increased to 5.6/7, while scoring the saved material layers against ground truth showed material-family accuracy improving from 52% to 92%. Participants also used the same workflow to author material feels without a physical reference.
