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Current gesture recognition systems primarily focus on identifying gestureswithin a predefined set, leaving a gap in connecting these gestures tointeractive GUI elements or system functions (e.g., linking a 'thumb-up'gesture to a 'like' button). We introduce GestureGPT, a novel zero-shot gestureunderstanding and grounding framework leveraging large language models (LLMs).Gesture descriptions are formulated based on hand landmark coordinates fromgesture videos and fed into our dual-agent dialogue system. A gesture agentdeciphers these descriptions and queries about the interaction context (e.g.,interface, history, gaze data), which a context agent organizes and provides.Following iterative exchanges, the gesture agent discerns user intent,grounding it to an interactive function. We validated the gesture descriptionmodule using public first-view and third-view gesture datasets and tested thewhole system in two real-world settings: video streaming and smart home IoTcontrol. The highest zero-shot Top-5 grounding accuracies are 80.11% for videostreaming and 90.78% for smart home tasks, showing potential of the new gestureunderstanding paradigm.

GestureGPT: Zero-shot Interactive Gesture Understanding and Grounding with Large Language Model Agents