Summary of Ea-ras: Towards Efficient and Accurate End-to-end Reconstruction Of Anatomical Skeleton, by Zhiheng Peng et al.
EA-RAS: Towards Efficient and Accurate End-to-End Reconstruction of Anatomical Skeleton
by Zhiheng Peng, Kai Zhao, Xiaoran Chen, Li Ma, Siyu Xia, Changjie Fan, Weijian Shang, Wei Jing
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes EA-RAS, a single-stage anatomical skeleton estimator that uses a single RGB image input to provide accurate and realistic skeletons with arbitrary pose. Unlike existing methods, EA-RAS is lightweight, plug-and-play, and efficient, making it suitable for real-time applications. The model estimates the conventional human-mesh model explicitly, leveraging skin information to improve inside skeleton modeling. A progressive training strategy and enhanced optimization process enable the network to obtain initial weights using a small skin dataset and achieve self-supervision in skeleton reconstruction. Optional post-processing optimization can further enhance accuracy while maintaining speed. Experimental results show that EA-RAS is over 800 times faster than existing methods, meeting real-time requirements, with optional post-processing providing up to 50% accuracy improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to estimate human skeletal information using just one picture. This helps in areas like biology education and computer-human interaction. Current skeleton models are either simple but not accurate or complex but take too much time and data. The researchers propose a single-stage model called EA-RAS that is fast, easy to use, and produces realistic skeletons. It can estimate the human-mesh model and use skin information to make it more accurate. They also developed a way to train this model using less data and get better results. This new method is much faster than existing methods, making it perfect for real-time applications. |
Keywords
* Artificial intelligence * Optimization
