Understanding Co-speech Gestures in-the-wild

• URL: http://arxiv.org/abs/2503.22668v1
• Date: Fri, 28 Mar 2025 17:55:52 GMT
• Title: Understanding Co-speech Gestures in-the-wild
• Authors: Sindhu B Hegde, K R Prajwal, Taein Kwon, Andrew Zisserman,
• Abstract summary: We introduce a new framework for co-speech gesture understanding in the wild.<n>We propose three new tasks and benchmarks to evaluate a model's capability to comprehend gesture-text-speech associations.<n>We present a new approach that learns a tri-modal speech-text-video-gesture representation to solve these tasks.
• Score: 52.5993021523165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Co-speech gestures play a vital role in non-verbal communication. In this paper, we introduce a new framework for co-speech gesture understanding in the wild. Specifically, we propose three new tasks and benchmarks to evaluate a model's capability to comprehend gesture-text-speech associations: (i) gesture-based retrieval, (ii) gestured word spotting, and (iii) active speaker detection using gestures. We present a new approach that learns a tri-modal speech-text-video-gesture representation to solve these tasks. By leveraging a combination of global phrase contrastive loss and local gesture-word coupling loss, we demonstrate that a strong gesture representation can be learned in a weakly supervised manner from videos in the wild. Our learned representations outperform previous methods, including large vision-language models (VLMs), across all three tasks. Further analysis reveals that speech and text modalities capture distinct gesture-related signals, underscoring the advantages of learning a shared tri-modal embedding space. The dataset, model, and code are available at: https://www.robots.ox.ac.uk/~vgg/research/jegal

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