Social touch is a rich channel of human communication, conveying emotion, intent, and meaning embedded in context. Yet most HCI studies treat touch in isolation, overlooking the layered subtleties that shape interpretation. We present a contextual analysis of 5,016 social touch events, grounded in a large collection of annotated scenes from films, dramas, and documentaries. Using a computer vision pipeline, we segmented touch events from video and annotated them across dimensions, including who is involved, how the gesture is performed, where on the body it occurs, and the cultural backdrop. Our analysis shows that identical gestures can convey distinct meanings depending on body location, relationship type, and context. Similar intentions—like comfort, encouragement, or dominance—may be expressed through different gestures or locations, shaped by relational dynamics, cultural norms, and public or private settings. These insights inform the design of socially aware touch technologies, including avatars, social agents, and mediated communication systems.

Creating interactive VR content typically requires extensive modeling effort, making scalable production difficult. We present an automated computational pipeline that converts a single image into an interactive 3D scene with plausible kinesthetic feedback. Our approach leverages a Large Language Model (LLM) to detect objects in the image and infer haptic properties from visual and textual cues. These extracted results are then utilized to synthesize 3D models and optimize haptic properties for perceptual distinguishability, while combining them into a complete interactive environment. A user study shows that the generated VR scenes provide compelling visuo-haptic experiences, highlighting the potential of our method for scalable multisensory world generation. To our knowledge, this is the first system to automatically produce VR scenes with force feedback from a single image, pointing toward a practical direction for lowering the barriers to creating haptically enriched VR content.