Uplifting 2D to 3D Human Poses with Joint Rotations and Bone Constraints: A Strong Baseline for Sports and Fitness Applications

Accurate 3D human pose estimation plays a pivotal role in AI-driven systems for assessing and improving skilled human activities in domains such as fitness and sports. Despite significant progress in 2D-to-3D pose uplifting, current methods often focus on isolated components, such as kinematics modeling, bone-length constraints, or joint orientation estimation. Surprisingly, little effort has been made to combine these complementary approaches into a unified framework, even though their integration could naturally enhance performance. To address this, we introduce a strong baseline for 2D-to-3D pose uplifting that combines quaternion-based joint orientation estimation, bone length constraints for anatomically plausible predictions, and kinematic modeling for improved stability. Our approach is evaluated on the traditional Human3.6M dataset, and three challenging, underexplored benchmarks: Fit3D, which focuses on fitness exercises, AthletePose3D, designed to study professional athletic performance, and Harmony4D with complex inter-person interactions. These datasets pose unique challenges, such as high-speed and non-regular motions, which are more representative of real-world applications than conventional datasets. Results demonstrate that integrating these complementary techniques improves 3D pose estimation accuracy and provides a robust foundation for downstream tasks such as skill assessment and feedback generation. By addressing the challenges posed by complex datasets and highlighting the advantages of combining well-established methods, this work aims to inspire further research in skilled activity understanding. Our strong baseline provides a lightweight and effective baseline for 3D human pose estimation in fitness and sports contexts. The code is available at: https://github. com/Mickael-Cormier/3d-hpe-strong-baseline.