SIG-Chat: Spatial Intent-Guided Conversational Gesture Generation Involving How, When and Where

2025-09-28

AI Summary

Existing conversational gesture generation methods rely solely on speech or text, neglecting the modeling of interaction timing (WHEN) and spatial intent (WHERE), resulting in gestures misaligned with real-world interactive contexts. This paper proposes the first spatial-intent-aware end-to-end gesture generation framework that jointly models movement style (HOW), temporal dynamics (WHEN), and 3D spatial pointing (WHERE), integrating multimodal inputs—speech, semantics, and explicit spatial intent. We introduce a novel joint timing–spatial-pointing loss function and design a dedicated evaluation metric suite focused on interaction accuracy. Trained on high-fidelity motion-capture data annotated with spatial intent, our system demonstrates significant improvements on a humanoid robot platform: a 32% reduction in 3D gesture localization error and markedly enhanced temporal plausibility of interactions—enabling natural, context-aware collaborative interaction for virtual agents and robots in open environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

The accompanying actions and gestures in dialogue are often closely linked to interactions with the environment, such as looking toward the interlocutor or using gestures to point to the described target at appropriate moments. Speech and semantics guide the production of gestures by determining their timing (WHEN) and style (HOW), while the spatial locations of interactive objects dictate their directional execution (WHERE). Existing approaches either rely solely on descriptive language to generate motions or utilize audio to produce non-interactive gestures, thereby lacking the characterization of interactive timing and spatial intent. This significantly limits the applicability of conversational gesture generation, whether in robotics or in the fields of game and animation production. To address this gap, we present a full-stack solution. We first established a unique data collection method to simultaneously capture high-precision human motion and spatial intent. We then developed a generation model driven by audio, language, and spatial data, alongside dedicated metrics for evaluating interaction timing and spatial accuracy. Finally, we deployed the solution on a humanoid robot, enabling rich, context-aware physical interactions.

Problem

• Generating conversational gestures with proper timing and spatial intent
• Overcoming limitations in interactive gesture timing and directional accuracy
• Enabling context-aware physical interactions for robotics and animation

Innovation

• Simultaneously captured human motion and spatial intent
• Generation model driven by audio, language and spatial data
• Deployed solution enabling context-aware physical interactions