Performance of Large Pre-trained Gesture Recognition Models on Real-world Datasets after Synthetic Fine-tuning

Abstract

Abstract: In this work, we explore the possibility of using synthetically generated data for video-based gesture recognition with large pre-trained models. We consider whether these models have sufficiently robust and expressive representation spaces to enable "training-free" classification. Specifically, we utilize various state-of-the-art video encoders to extract features for use in k-nearest neighbors classification, where the training data points are derived from synthetic videos only. We compare these results with another training-free approach – zero-shot classification using text descriptions o

Research Question

How do large pre-trained models for gesture recognition perform when evaluated on real-world video datasets after fine-tuning on synthetically generated videos, measured by classification accuracy and generalization to unseen gestures?

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Truth-Engine Gate Verdict

This record has passed Gate 2: a Lean4 proof source type-checks, or a sealed-sandbox run reproduced the reported results within the stated tolerance. A reproducible artifact (proof source or repro script and results) is attached to this record. VERIFIED requires an attached reproducible artifact (Lean4 proof source, or repro script and results) before this status can be set; it is not derived from review score or claim count.

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Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

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