A Comprehensive Survey of Action Quality Assessment: Method and Benchmark

TL;DR

This paper provides a comprehensive survey of Action Quality Assessment, introducing a taxonomy, establishing a unified benchmark, and analyzing trends, challenges, and future directions in the field.

Contribution

It proposes a hierarchical taxonomy for AQA methods, creates a unified benchmark with standardized evaluation protocols, and offers an analysis of research trends and future opportunities.

Findings

Organized existing AQA methods into video-based, skeleton-based, and multi-modal categories.

Established a unified benchmark for consistent comparison of AQA methods.

Analyzed emerging trends, challenges, and future directions in AQA research.

Abstract

Action Quality Assessment (AQA) aims to automatically evaluate how well human actions are performed and has been widely applied in sports analysis, skill assessment, and healthcare. However, AQA studies are often developed under heterogeneous datasets and evaluation settings, making systematic comparison across methods difficult. To address these challenges, we present a comprehensive survey of recent advances in AQA. In particular, we propose a modality-driven hierarchical taxonomy that organizes existing methods into video-based, skeleton-based, and multi-modal approaches, and analyze the methodological evolution of representative models. We further establish a unified benchmark for representative video-based AQA methods by integrating diverse datasets and standardized evaluation protocols, enabling consistent comparison in terms of both accuracy and computational efficiency. Finally, we analyze emerging research trends, identify key challenges in current AQA research, and outline future directions ranging from near-term methodological advances to longer-term opportunities enabled by emerging AI paradigms. The project web page can be found at https://ZhouKanglei.github.io/AQA-Survey.