EA-RAS: Towards Efficient and Accurate End-to-End Reconstruction of Anatomical Skeleton

TL;DR

EA-RAS is a real-time, lightweight, end-to-end method for anatomically accurate human skeleton reconstruction from a single RGB image, outperforming existing methods in speed and precision.

Contribution

The paper introduces EA-RAS, a novel single-stage model that achieves real-time, accurate anatomical skeleton reconstruction with minimal data and enhanced optimization strategies.

Findings

Over 800 times faster than existing methods.

Post-processing improves accuracy by over 50%.

Achieves real-time performance with high anatomical fidelity.

Abstract

Efficient, accurate and low-cost estimation of human skeletal information is crucial for a range of applications such as biology education and human-computer interaction. However, current simple skeleton models, which are typically based on 2D-3D joint points, fall short in terms of anatomical fidelity, restricting their utility in fields. On the other hand, more complex models while anatomically precise, are hindered by sophisticate multi-stage processing and the need for extra data like skin meshes, making them unsuitable for real-time applications. To this end, we propose the EA-RAS (Towards Efficient and Accurate End-to-End Reconstruction of Anatomical Skeleton), a single-stage, lightweight, and plug-and-play anatomical skeleton estimator that can provide real-time, accurate anatomically realistic skeletons with arbitrary pose using only a single RGB image input. Additionally, EA-RAS estimates the conventional human-mesh model explicitly, which not only enhances the functionality but also leverages the outside skin information by integrating features into the inside skeleton modeling process. In this work, we also develop a progressive training strategy and integrated it with an enhanced optimization process, enabling the network to obtain initial weights using only a small skin dataset and achieve self-supervision in skeleton reconstruction. Besides, we also provide an optional lightweight post-processing optimization strategy to further improve accuracy for scenarios that prioritize precision over real-time processing. The experiments demonstrated that our regression method is over 800 times faster than existing methods, meeting real-time requirements. Additionally, the post-processing optimization strategy provided can enhance reconstruction accuracy by over 50% and achieve a speed increase of more than 7 times.