Beyond Audio and Pose: A General-Purpose Framework for Video Synchronization

TL;DR

This paper introduces VideoSync, a versatile video synchronization framework that operates independently of specific feature extraction methods, providing a new, more generalizable approach with rigorous evaluation and improved performance.

Contribution

The work presents VideoSync, a general-purpose video synchronization framework evaluated on diverse datasets, correcting biases in prior methods and establishing reproducible benchmarks.

Findings

VideoSync outperforms existing methods like SeSyn-Net under fair conditions.

A CNN-based model is identified as the most effective for offset prediction.

The framework is applicable across single-human, multi-human, and non-human scenarios.

Abstract

Video synchronization-aligning multiple video streams capturing the same event from different angles-is crucial for applications such as reality TV show production, sports analysis, surveillance, and autonomous systems. Prior work has heavily relied on audio cues or specific visual events, limiting applicability in diverse settings where such signals may be unreliable or absent. Additionally, existing benchmarks for video synchronization lack generality and reproducibility, restricting progress in the field. In this work, we introduce VideoSync, a video synchronization framework that operates independently of specific feature extraction methods, such as human pose estimation, enabling broader applicability across different content types. We evaluate our system on newly composed datasets covering single-human, multi-human, and non-human scenarios, providing both the methodology and code for dataset creation to establish reproducible benchmarks. Our analysis reveals biases in prior SOTA work, particularly in SeSyn-Net's preprocessing pipeline, leading to inflated performance claims. We correct these biases and propose a more rigorous evaluation framework, demonstrating that VideoSync outperforms existing approaches, including SeSyn-Net, under fair experimental conditions. Additionally, we explore various synchronization offset prediction methods, identifying a convolutional neural network (CNN)-based model as the most effective. Our findings advance video synchronization beyond domain-specific constraints, making it more generalizable and robust for real-world applications.