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

2025-06-19

AI Summary

Existing video synchronization methods heavily rely on audio or domain-specific visual cues (e.g., human pose), resulting in poor generalization to audio-free, single-/multi-person, and non-human scenarios; moreover, the lack of a universal, reproducible benchmark hinders progress. This paper proposes the first feature-agnostic, preprocessing-robust framework for general multi-view video synchronization, decoupling feature extraction from temporal offset prediction. We identify and rectify a systematic preprocessing bias inherent in the state-of-the-art SeSyn-Net. Furthermore, we introduce the first open-source evaluation benchmark covering diverse content types, equipped with a synthetic data generation pipeline and a bias-aware evaluation protocol. Under fair, controlled comparisons, our method reduces mean synchronization error by 32% over prior approaches. All code, tools, and datasets are publicly released.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Aligning multiple video streams without relying on audio or specific visual cues
• Addressing limitations in existing video synchronization benchmarks and methods
• Developing a generalizable framework for diverse video content synchronization

Innovation

• General-purpose video synchronization framework
• Independent of specific feature extraction methods
• CNN-based model for offset prediction