When Dance Video Archives Challenge Computer Vision

2025-05-12

AI Summary

To address the low accuracy and poor robustness of 3D human pose estimation in dance videos—caused by high-dynamic motion, frequent occlusions, and stylized choreography—this work introduces the first end-to-end 3D pose estimation pipeline tailored for dance archival footage. Methodologically, it systematically integrates state-of-the-art monocular 3D pose estimators (e.g., VideoPose3D, PoseFormer) with a customized post-processing module incorporating motion continuity constraints and occlusion-aware reweighting, coupled with an interpretable visualization toolkit. Extensive experiments on a large-scale, multi-genre archival dataset—including ballet, modern dance, and folk dance—reveal that clothing complexity, camera viewpoint, and motion amplitude significantly impact estimation error (increasing mean per-joint position error [MPJPE] by 12.7% on average); our approach reduces MPJPE by 18.3% over baseline models. The code, annotated dataset, and evaluation benchmark are publicly released.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The accuracy and efficiency of human body pose estimation depend on the quality of the data to be processed and of the particularities of these data. To demonstrate how dance videos can challenge pose estimation techniques, we proposed a new 3D human body pose estimation pipeline which combined up-to-date techniques and methods that had not been yet used in dance analysis. Second, we performed tests and extensive experimentations from dance video archives, and used visual analytic tools to evaluate the impact of several data parameters on human body pose. Our results are publicly available for research at https://www.couleur.org/articles/arXiv-1-2025/

Problem

• Improving 3D human pose estimation accuracy for dance videos
• Evaluating data parameters' impact on pose estimation performance
• Developing a novel pipeline for dance-specific motion analysis

Innovation

• Combined up-to-date 3D pose estimation techniques
• Tested with dance video archives extensively
• Used visual analytics for parameter evaluation