Prompt-to-Gesture: Measuring the Capabilities of Image-to-Video Deictic Gesture Generation

2026-04-16 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A new study introduces and analyzes prompt-based video generation to create a realistic deictic gestures dataset, addressing the acute data scarcity in gesture recognition research. Researchers Hassan Ali, Doreen Jirak, Luca Müller, and Stefan Wermter propose a data generation pipeline that produces deictic gestures from a small number of human reference samples. This approach leverages recent advancements in image-to-video foundation models to generate photorealistic, semantically rich videos guided by natural language. The synthetic gestures demonstrate close alignment with real gestures in visual fidelity while introducing meaningful variability and novelty, enriching the original data. Deep models trained on this mixed dataset show superior performance, indicating that image-to-video techniques offer a powerful zero-shot approach for gesture synthesis.

Key takeaway

For research scientists facing data scarcity in gesture recognition, you should explore prompt-based image-to-video generation to create synthetic deictic gesture datasets. This method can significantly augment your existing human-generated data, introducing valuable variability and potentially leading to superior performance in downstream deep learning tasks.

Key insights

Image-to-video models can generate high-fidelity, variable synthetic deictic gestures to overcome data scarcity.

Principles

• Synthetic data can augment real data.
• Zero-shot generation is effective for gesture synthesis.

Method

A data generation pipeline produces deictic gestures from a few human reference samples using prompt-based image-to-video models.

In practice

• Generate synthetic gesture datasets.
• Improve deep model performance with mixed data.

Topics

• Deictic Gesture Generation
• Image-to-Video Foundation Models
• Synthetic Data Augmentation
• Prompt-based Video Generation
• Gesture Recognition

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.