Incorporating Spatial Awareness in Data-Driven Gesture Generation for Virtual Agents
원문 초록
This paper focuses on enhancing human-agent communication by integrating spatial context into virtual agents' non-verbal behaviors, specifically gestures. Recent advances in co-speech gesture generation have primarily utilized data-driven methods, which create natural motion but limit the scope of gestures to those performed in a void. Our work aims to extend these methods by enabling generative models to incorporate scene information into speech-driven gesture synthesis. We introduce a novel synthetic gesture dataset tailored for this purpose. This development represents a critical step toward creating embodied conversational agents that interact more naturally with their environment and users.
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DeepGesture: A conversational gesture synthesis system based on emotions and semantics
VarGes: Improving Variation in Co-Speech 3D Gesture Generation via StyleCLIPS
GestureLSM: Latent Shortcut based Co-Speech Gesture Generation with Spatial-Temporal Modeling
MotionCraft: Crafting Whole-Body Motion with Plug-and-Play Multimodal Controls
MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space Models
EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling
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