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

2026-04-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

Gesture recognition research faces significant data scarcity, traditionally relying on expensive human recordings or image processing methods that lack authentic variability. Recent image-to-video foundation models, capable of generating photorealistic, semantically rich videos from natural language, present a new avenue for creating synthetic data. This research introduces and analyzes a prompt-based video generation pipeline to construct a realistic deictic gestures dataset. The proposed method generates deictic gestures from a small number of human reference samples, making it accessible for broader use. The synthetic gestures demonstrate high visual fidelity, meaningful variability, and novelty compared to real gestures, leading to superior performance in various deep models when using a mixed dataset. These findings indicate that early-stage image-to-video techniques offer a powerful zero-shot approach for gesture synthesis, benefiting downstream tasks.

Key takeaway

For research scientists developing gesture recognition systems, the ability to augment scarce human-recorded data with high-fidelity, variable synthetic gestures from image-to-video models is crucial. You should explore integrating prompt-based video generation pipelines to enrich your datasets, potentially improving the performance of deep learning models and accelerating research in this data-constrained field.

Key insights

Image-to-video models can generate realistic, variable deictic gestures, augmenting scarce human-recorded data for improved downstream task performance.

Principles

• Synthetic data can enrich real data.
• Zero-shot generation is effective.
• Variability enhances model performance.

Method

A data generation pipeline produces deictic gestures from a small set of human reference samples using prompt-based video generation, then evaluates effectiveness for downstream tasks.

In practice

• Generate synthetic gestures from prompts.
• Combine real and synthetic gesture data.
• Use image-to-video for data augmentation.

Topics

• Gesture Recognition
• Image-to-Video Generation
• Deictic Gestures
• Synthetic Data Generation
• Foundation Models

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.