arXiv:2605.30608v1 Announce Type: new Abstract: Learning a shared representation between spoken text and gesture is central to co-speech gesture retrieval, synthesis, and understanding, but remains challenging for semantically meaningful gestures whose communicative intent is not captured by motion alone. Direct contrastive alignment between transcripts and continuous motion embeddings often overemphasizes low-level kinematics and misses the symbolic content of semantic gestures. We propose semantic motion anchors, natural-language abstractions of gesture motion capturing physical form and com
The continuous advancements in AI, particularly in multimodal learning and natural language processing, are pushing the boundaries of human-computer interaction and gesture understanding.
This research enables more natural and effective AI-human collaboration by allowing AI systems to understand and generate co-speech gestures, crucial for agentic systems and embodied AI.
The ability to bridge semantic meaning and physical motion in gestures changes how AI can interpret human communication and generate more human-like responses.
- · AI agents developers
- · Robotics companies
- · Virtual reality/augmented reality sector
- · Customer service automation
- · Companies relying on rudimentary gesture recognition
- · Interfaces lacking multimodal understanding
More expressive and context-aware AI agents capable of richer human-like interaction.
Improved human-robot collaboration in both digital and physical spaces due to enhanced non-verbal communication.
The development of new forms of 'gesture-first' digital interfaces and artistic expressions enabled by fluent AI-gesture generation.
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Read at arXiv cs.CL